This study explores fault-tolerant consensus in leader-following heterogeneous multi-agent systems, focusing on actuator failures in uncrewed aerial vehicles (UAVs) and uncrewed ground vehicles (UGVs). An optimization-based fault-tolerant consensus algorithm is proposed. The algorithm utilizes the Euler-Lagrange formula to ensure system consistency under actuator failures, with the Lyapunov stability theory proving the asymptotic stability of the consistency error. The algorithm is applied to heterogeneous multi-agent systems of UAVs and UGVs to derive optimal fault-tolerant consensus control laws for each vehicle type. Simulation experiments give evidence for the feasibility of the proposed control strategy.

Abstract: Author attribution refers to identifying the author of a document by distinguishing between texts written by various authors. Since the author’s writing style is a straightforward concept of his writing habits, an author’s attribution can be used in various sections of society, including legal work, court cases, and plagiarism. This paper studies and compares a deep architecture that automatically identifies Persian writers through their writing styles. For this purpose, since there were no existing datasets suitable for author identification in Persian, the first step was to collect three different datasets: Novels, News, and Twitter. Afterward, the Word2Vec algorithm is trained on many Persian datasets and used as word embedding. To have syntactic features besides semantic ones, Part Of Speech (POS) embedding has also been used. In the next step, an attention mechanism and Long Short-Term Memory (LSTM) are coupled with residual connections. Thus, the current layer input becomes the output of all previous layers rather than the top one. The model implemented is superior in performance for all three types of data compared to earlier models in text classification and author attribution. Absolute accuracies of 90.22%, 85.10%, and 71.39% were achieved among the three Novels, News, and Tweets datasets.

The profile of secondary metabolites occurring in apple cuticular layer is not only characteristic for particular apple cultivar, but also dynamically reflects various external factors in growing environment. In this study, the possibility to authenticate apple samples based on analysis of their cuticular layer extracts was investigated. Ultra-high-performance liquid chromatography coupled to high resolution tandem mass spectrometry (UHPLC-HRMS/MS) was employed for obtaining metabolomic fingerprints. In total, 274 authentic apple samples, four cultivars harvested within the seasons 2020 to 2022 in the Czech Republic and Poland were analysed. The generated complex data processed by univariate and multivariate statistical methods enabled to build classification models for discrimination of apple cultivars as well as their geographical origin. The models showed very good performance in differentiation of Czech and Polish samples for three of four cultivars: ‘Gala’, ‘Golden Delicious’ and ‘Idared’, moreover, the validity of the models was verified over harvest seasons. The diagnostic markers, besides metabolites of triterpene biosynthetic pathway, mainly represented wax esters. ‘Jonagold’, which is known to be susceptible to mutations, was the only cultivar for which the unambiguous classification of geographical origin was not possible.

This article argues that philosophy and its influential narrative should take and think "food" - the gastro-world - in order to radically renew itself and develop a gastrocentric view of the world and the future of humanity. It discusses first some of the basic ideas of a gastrocentric understanding of the world, second the adventure of becoming a Terran, and third the daily practice of what I will call "terra madre forming". It tells us this: If there is to be a future of human survival and well-being on Earth, if we are to achieve the goals of sustainable development, then we have a clear agenda for tomorrow with the re-humidification of the soil - madre terra - that nourishes us. What needs to be done is quite simple and down-to-earth: We, each of us, if possible, must participate in the regeneration of the earth. In conclusion, it is time for the Slow Food movement to blossom into a geo-gastro-activism. The future task of this organization is to make itself electable as a political party to support the much-needed transformation of humans into Terrans.

Intense climate change and rapid urbanization have increased the risk of urban flooding, seriously affecting urban economic and social stability. Enhancing the urban flood resilience (UFR) has required a new solution to cope with urban flood disasters. In this study, taking Yingtan city as an example, a system of indicators for evaluating UFR was constructed, with 17 representative indicators, comprising three subsystems: socio-economic, ecological and infrastructural. A hybrid (FAHP-EWM)-TOPSIS model was applied, to develop an index-based measurement to compare and evaluate UFR, and Gray Relational Analysis (GRA) was used to discover the main factors affecting UFR. In addition, the natural discontinuous method was innovatively used to divide the UFR grade interval into levels, and the grade change was evaluated based on the TOPSIS method. The results showed that (1) from 2010 to 2022, the UFR in Yingtan City increased by 80.69%, and the factors affecting UFR are highly correlated with urban infrastructure development. However, the ecological resilience in the subsystem showed a fluctuating downward trend, because of the influence of the surface area of lakes and rivers. (2) The grades of UFR for Yingtan City increased from Level III (2010 and 2016) to Level IV (2022), with local financial expenditures and the age structure of the population being the main factors currently limiting the development of UFR. The study provides a theoretical basis for the construction of an indicator system for assessing the UFR of Yingtan and proposes practical improvement directions for UFR.

The main objective of this paper is to identify the major research areas of ChatGPT through term and keyword co-occurrence network mapping techniques. For conducting the present study, total of 577 publications were retrieved from the Lens database for the network visualization. The findings of the study showed that “chatgpt” occurrence in maximum number of times followed by its related terms such as artificial intelligence, large language model, gpt, study etc. This study will be helpful to library and information science as well as computer or information technology professionals.

We present a case of adult-onset systemic chronic active EBV disease (CAEBV) in a 40-year-old woman with chronic HBV hepatitis. Initial symptoms resembled a viral illness, progressing to recurrent fever, transaminitis, and anasarca. Investigations revealed high-level EBV viremia and an abnormal T-cell population in liver and bone marrow, indicative of CAEBV. Liver biopsy showed CD3+ T-cells lacking TCRbeta and displaying dim/negative CD5, with elevated EBV-infected T-cells. Next-generation sequencing identified rare variants in CREBBP, SPEN, TP73, and PLCG2, suggesting potential contributions to disease pathogenesis. This case underscores diagnostic challenges and management complexities of adult-onset CAEBV, particularly with underlying chronic HBV infection. Genomic profiling offers crucial insights into the molecular landscape of rare lymphoid malignancies, highlighting the importance of personalized treatment strategies. The distinct immunophenotypic features underscore the heterogeneity in EBV-associated T-cell LPDs, urging further research for optimized clinical management

Plant cells' ability to withstand abiotic stress is strongly linked to modifications in their mechanical characteristics. Nevertheless, the lack of a workable method for consistently tracking plant cell mechanical properties severely restricts our comprehension of the mechanical alterations in plant cells under stress. With Polyethylene Glycol (PEG6000), we created a drought-like environment, and we used the Double Resonator Piezoelectric Cytometry (DRPC) method to dynamically and non-invasively track changes in the stress (ΔS) created and viscoelasticity (storage modulus G' and loss modulus G") of protoplasts and suspension cells of rice during drought stress. The findings demonstrate that, rice suspension cells and protoplasts react mechanically differently to 5%–15% PEG6000 stress, implying distinct resistance mechanisms. However, neither of them can withstand 25% PEG6000 stress, they respond mechanically similarly to 25% PEG6000 stress. The results of DRPC are further corroborated by the morphological alterations of rice cells and protoplasts observed under an optical microscope. To sum up, the DRPC technique functions as a precise cellular mechanical sensor and offers novel research tools for the evaluation of plant cell adversity and differentiating between the mechanical reactions of cells and protoplasts under abiotic stress.

Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) continues to be a global threat due to its ability to evolve and generate new subvariants, leading to new waves of infection. Additionally, other coronaviruses like Middle East respiratory syndrome coronavirus (MERS-CoV, formerly known as hCoV-EMC) which first emerged in 2012, persists and continues to present a threat of severe illness to humans. The continued identification of novel coronaviruses, coupled with the potential for genetic recombination between different strains, raises the possibility of new coronavirus clades of global concern emerging. As a result, there is a pressing need for pan-CoV therapeutic drugs and vaccines. After extensive optimization of an HCV protease inhibitor screening hit, a novel 3CLPro inhibitor (MK-7845) was discovered and subsequently profiled. MK-7845 exhibited nanomolar in vitro potency with broad spectrum activity against a panel of clinical SARS-CoV-2 subvariants and MERS-CoV. Furthermore, when administered orally, MK-7845 demonstrated a notable reduction of viral burdens by >6 log orders in the lungs of transgenic mice infected with SARS-CoV-2 (K18-hACE2 mice) and MERS-CoV (K18-hDDP4 mice).

The evolving collective psyche of humanity reveals a shift from predominantly masculine to increasingly feminine characteristics since around 1500 A.D. This transformation, termed 'trans-genderization,' is attributed to identified psychological factors. Remarkably, the collective psyche has undergone similar transitions in the past since the emergence of humanity.

Plato defined a quality as “some degree of perfection” [49]. Studies on quality have unveiled the internal and external quality factors [17] that significantly impact product quality [4]. Internal factors are invisible to the end customer and are dependent on overall work organisation by the development team [17]. This work organisation is called “the process” [11],[33]. In this article, we will discuss how to address the drone-dedicated software development process to support system verification and ensure product quality by supporting quality characteristics and therefore increase software development awareness and control. We will discuss the process's influence on overall work activities and relationships between those activities by addressing the software development process utilised in the drone-dedicated GNSS-denied navigation system development project. The result was observable after three weeks of starting the software development. It was focused on better team awareness, faster task completion, lower number of development mistakes, and better and faster change response than we observed in other projects that didn’t follow the predefined process.

Mint is considered to have a greater number of phenolic acids, flavonoids, antioxidants and other bioactive components, and is widely used as food, medicine, spices, and flavoring agents. Thus, chemical composition is an important parameter for assessing the quality of mint. In this study, two mint cultivars were sampled, purple mint and green mint. The purple mint had much higher anthocyanin and total flavone content compared with green mint. Transcriptome and metabolome technique were employed to elucidate the regulation network of mint pigment and flavone. A total of 167,901 unigenes were obtained by high-throughput RNA-Seq and 34,608 genes were differentially expressed. The differentially expressed genes (DEGs) were mainly involved in lignin metabolic process and flavonoid biosynthetic process. A total of 143 differentially expressed metabolites (DEMs) were enriched in isoflavonoid biosynthesis, flavonoid biosynthesis, flavone and flavonol biosynthesis, and anthocyanin biosynthesis pathways. The co-analysis results of DEGs and DEMs showed that the flavone and flavonol biosynthesis pathway (ko00944) contained the most DEMs, followed by flavonoid biosynthesis pathway (ko00941) and anthocyanin biosynthesis pathway (ko00942). Furthermore, the most important nine genes and metabolites were screened using O2PLS model. The results will provide a theoretical basis for revealing pigment and flavone biosynthesis network of mint.

The way people innovate and create new ideas and bring them to the market is undergoing a fundamental change from closed innovation to open innovation. Why and how do firms perform open innovation? Firms’ open innovation is measured through the levels of firms’ joint patent applications. Next, we analyze network structures and characters of firms’ joint patent applications such as betweenness and degree centrality, structure hole, and closure. From this research, we drew conclusions as follows. First, the structure of collaboration networks has both direct and indirect effects on firms’ innovative performance. Second, in the process of joint patent applications, there is a long tail phenomenon in networks of joint patent applications. Third, the number of patents and International Patent Classification (IPC) subclasses together constitute a meaningful measure of the innovation performance of firms.

In an era when medical literature is increasing daily, researchers in biomedical and clinical areas have joined efforts with language engineers to analyze large amount of biomedical and molecular biology literature (such as PubMed), patient data or health records. With such a huge amount of reports, evaluating their impact has long seized to be a trivial task. In this context, this paper intends to introduce a non-scientific factor that represents an important element in the effort of gaining acceptance of claims. Thus, we postulate that the confidence the author is expressing in his work plays an important role in shaping the first impression that influences the reader’s perception of the paper. The results discussed in this paper are based on a series of experiments ran over data from the Open Archives Initiative (OAI) corpus that provides interoperability standards in order to facilitate the effectiveness dissemination of the content. This method can be useful to the direct beneficiaries (authors, who are engaged in medical or academic research), but, also, researchers in the fields of BioNLP and NLP, etc.

Sparse training (ST) aims to ameliorate deep learning by replacing fully connected artificial neural networks (ANNs) with sparse or ultra-sparse ones, such as brain networks are, therefore it might benefit to borrow brain-inspired learning paradigms from complex network intelligence theory. Here, we launch the ultra-sparse advantage challenge, whose goal is to offer evidence on the extent to which ultra-sparse (around 1% connection retained) topologies can achieve any leaning advantage against fully connected. Epitopological learning is a field of network science and complex network intelligence that studies how to implement learning on complex networks by changing the shape of their connectivity structure (epitopological plasticity). One way to implement Epitopological (epi- means new) Learning is via link prediction: predicting the likelihood of non-observed links to appear in the network. Cannistraci-Hebb learning theory inspired the CH3-L3 network automata rule for link prediction which is effective for general-purpose link prediction. Here, starting from CH3-L3 we propose Epitopological Sparse Meta-deep Learning (ESML) to apply Epitopological Learning to sparse training. In empirical experiments, we find that ESML learns ANNs with ultra-sparse hyperbolic (epi-)topology in which emerges a community layer organization that is meta-deep (meaning that each layer also has an internal depth due to power-law node hierarchy). Furthermore, we discover that ESML can in many cases automatically sparse the neurons during training (arriving even to 30% neurons left in hidden layers), this process of node dynamic removal is called percolation. Starting from this network science evidence, we design Cannistraci-Hebb training (CHT), a 4-step training methodology that puts ESML at its heart. We conduct experiments on 7 datasets and 5 network structures comparing CHT to dynamic sparse training SOTA algorithms and the fully connected counterparts. The results indicate that, with a mere 1% of links retained during training, CHT surpasses fully connected networks on VGG16, GoogLeNet, ResNet50, and ResNet152. This key finding is an evidence for ultra-sparse advantage and signs a milestone in deep learning. CHT acts akin to a gradient-free oracle that adopts CH3-L3-based epitopological learning to guide the placement of new links in the ultra-sparse network topology to facilitate sparse-weight gradient learning, and this in turn reduces the convergence time of ultra-sparse training. Finally, CHT offers the first examples of parsimony dynamic sparse training because, in many datasets, it can retain network performance by percolating and significantly reducing the node network size. Our code is available at: https://github.com/biomedical-cybernetics/Cannistraci-Hebb-training.

Graft injury affects over 50% of liver transplant (LT) recipients, but non-invasive biomarkers to diagnose and guide treatment are currently limited. We aimed to develop a biomarker of graft injury by integrating serum metabolomic profiles with clinical variables. Serum from 55 LT recipients with biopsy confirmed metabolic-dysfunction associated steatohepatitis (MASH), T-cell mediated rejection (TCMR) and biliary complications was collected and processed using a combination of LC-MS/MS assay. The metabolomic profiles were integrated with clinical information using a multi-class Machine Learning (ML) classifier. The efficacy of the model was assessed through the evaluation of the Out-of-Bag (OOB) error estimate. Our ML model yielded an overall accuracy of 79.66% with an OOB estimate of the error rate at 19.75%. The model exhibited a maximum ability to distinguish MASH, with an OOB error estimate of 7.4% compared to 22.2% for biliary and 29.6% for TCMR. The metabolites serine and serotonin emerged as the topmost predictors. When predicting binary outcomes using 3 models: Biliary (biliary vs. rest), MASH (MASH vs. rest) and TCMR (TCMR vs. rest), the AUCs were 0.882, 0.972 and 0.96 respectively. Our ML tool integrating serum metabolites with clinical variables shows promise as a non-invasive, multi-class serum biomarker of graft pathology.

In this study, we investigate the growth of EV ownership among Swedish households. Through detailed socio-economic data and the Swedish vehicle register, we are able compare characteristics of households that bought an EV in 2016 with those that became EV owners in 2020. We are thus able to describe and analyse the transition from the early EV owners to the early majority. Unlike many prior studies in the field, which are based on surveys or interviews, this study relies on registry data, thereby encompassing all car owners in Sweden. In this study, we have also scrutinized the significance of ownership mode by distinguishing between households that have chosen to purchase a car and those that have chosen to lease one. The results show that the demand for EVs has reached new consumer groups, e.g. both younger and older car buyers, and that the option to lease a car privately has broadened the market for new cars in general, and for EV’s in particular.

Marine sand, in addition to oil and gas resources, is the second largest marine mineral resource. The rational development and utilization of marine sand resources are conducive to the growth of the marine economy. Marine sand can be categorized into surface marine sand and buried marine sand. Buried marine sand deposits are buried beneath the sea floor, making it challenging to locate them due to their thin distribution. Consequently, there exist numerous technical difficulties associated with marine sand exploration. In this study, various geophysical methods such as sub-bottom profile survey and single-channel seismic survey were employed in the northern offshore waters of the South China Sea along with offshore engineering geological drilling. As a result, two distinct marine sand bodies were delineated within the study area. Additionally, five reflective interfaces (R0, R1, R2, R3 and R4) were identified from top to bottom. These interfaces can be divided into five seismic sequences: A2, B2, C2, D2 and E2 respectively. Three sets of strata were recognized: the Quaternary Holocene Marine sedimentary layer (Q4m), the Upper Pleistocene alluvial layer, and the diluvial sedimentary layer (Q3al+pl) as well as the Upper Pleistocene Marine sedimentary layer (Q3m). Sequence A2 and B2 corresponded to Quaternary Holocene Formation (Q4m) within the study area while sequence C2 corresponded to B2 as well as upper Pleistocene alluvial and alluvial deposits (Q3al+pl), which represent one of the main occurring layers of sand in this region. Lastly, E2 represents a layer composed mainly of silty clay and round gravel known as upper Pleistocene marine sediment layer (Q3m). In total, two ore bodies containing marine sand have been discovered during this study. The estimated total resource amounts approximately 46 million m³ with an average thickness around 11 meters.

Intramuscular fat content (IFC) and meat color are vital indicators affecting pork quality. A significant positive correlation between IFC and redness meat color (a* value) indicates that these two traits are likely to be regulated by shared molecular pathways. To identify candidate genes, hub genes, and signaling pathways that regulate these two traits, we measured the IFC and a* value in 147 hybrid pigs and selected individuls with extreme phenotypes for transcriptome analysis. The results revealed 485 and 394 overlapping differentially expressed genes (DEGs) using DESeq2, limma, and edgeR packages affecting IFC and a* value, respectively. Weighted gene co-expression network analysis (WGCNA) identified four modules significantly correlated with IFC and a* value. Moreover, we integrated functional enrichment analysis results based on DEGs, GSEA, and WGCNA conditions to identify candidate genes, and identified 47 and 53 candidate genes affecting IFC and a* value, respectively. The protein-protein interaction (PPI) network analysis of candidate genes showed that 6 and 14 hub genes affecting IFC and a* value, respectively. These genes mainly participated in various pathways related to lipid metabolism and redox reactions. Notably, four crucial hub genes (MYC, SOX9, CEBPB, and PPAGRC1A) were shared for these two traits. After functional annotation of these four hub genes, we hypothesized that the SOX9/CEBPB/PPARGC1A axis could co-regulate lipid metabolism and myoglobin redox response. Further research on these hub genes, especially the SOX9/CEBPB/PPARGC1A axis, will help to understand the molecular mechanism of the co-regulation of IFC and a* value, which will provide the theoretical basis for improving pork quality.

Dynamic sparse training is an effective strategy to alleviate the training and inference demands of artificial neural networks. However, current sparse training methods face a challenge in achieving high levels of sparsity while maintaining performance comparable to that of their fully connected counterparts. The Cannistraci-Hebb training (CHT) method produces an ultra-sparse advantage compared to fully connected training in various tasks by using a gradient-free link regrowth method, which relies solely on the network topology. However, its rigid selection based on link prediction scores may lead to epitopological local minima, especially at the beginning of the training process when the network topology might be noisy and unreliable. In this article, we introduce the Cannistraci-Hebb training soft rule (CHTs), which applies a flexible approach to both the removal and regrowth of links during training, fostering a balance between exploring and exploiting network topology. Additionally, we investigate the network topology initialization using several approaches, including the bipartite scale-free and bipartite small-world network models. Empirical results show that CHTs can surpass the performance of fully connected networks with MLP architecture by using only 1% of the connections (99% sparsity) on the MNIST, EMNIST, and Fashion MNIST datasets and can provide remarkable results with only 0.1% of the links (99.9% sparsity). In some MLPs for image classification tasks, CHTs can reduce the active neuron network size to 20% of the original nodes (neurons), demonstrating a remarkable ability to generalize better than fully connected architectures, reducing the entire model size. This represents a relevant result for dynamic sparse training. Finally, we present evidence from larger network models such as Transformers, with 10% of the connections (90% sparsity), where CHTs outperform other prevalent dynamic sparse training methods in machine translation tasks.

Parasomnias are a group of sleep disorders characterized by abnormal and unpleasant motor, verbal or behavioral events that occur during sleep or during transitions between wake and sleep states. They disrupt sleep and can have a detrimental impact on the individual experiencing them. Our goal was to identify types of parasomnias and their prevalence in the post-secondary student population and to explore students' coping strategies for parasomnias they found distressing. Seventy-seven post-secondary students completed the 21-item Munich Parasomnia Screening (MUPS) frequency scale. They also rated, on a 10-point scale, how disturbing each parasomnia was. Not only did 92% percent of students report at least one parasomnia, but our results also indicate that the vast majority of students experienced more than one parasomnia. This led us to investigate the likelihood of the co-occurrence of different parasomnias. With respect to the level of subjectively experienced distress, the most prevalent parasomnias were not always the more disturbing ones. Coded open-ended responses about what students do about the disturbing parasomnias indicate that grounding strategies and physical manipulation of one’s body were the most common, although most participants indicated that in spite of distress, they do nothing to cope. In conclusion, our study found a strikingly high prevalence of parasomnias in this sample of young adults and a lack of knowledge about effective means of dealing with these.

Regulation of the diet in terms of the content of soluble and insoluble fibre is of great importance from the point of view of dietetics, microbiology of the gastrointestinal tract and immunology of the organism. Dietary fibre plays an important role in maintaining the balance between the con-tinuity of gut mucus production (intestinal mucosa), the stability of the intestinal microbiome composition, the regeneration of the intestinal epithelium and the immune status within the gut-associated lymphoid tissue (GALT) are played by pectin and their metabolic products in the form of pectin oligosaccharides. The main purpose of this review was to focus on pectin, their functions, their impact on the human's health and on some of the most promising prebiotics due to their solubility in the gastrointestinal tract in the form of pectin oligosaccharides. They have the properties of pectin, but have also additional features and postbiotics obtained in fermentation process can affect the intestinal microbiome and GALT-related immunity. They can be obtained from plant agro-wastes.

Background. Gene rearrangements affecting KMT2A are frequent in acute myeloid leukemia (AML) and are often associated with a poor prognosis. KMT2A gene fusions are often detected by chromosome banding analysis and confirmed by fluorescence in-situ hybridization. However, small intragenic insertions, termed KMT2A partial tandem duplication (PTD), are particularly challenging to detect using standard molecular and cytogenetic approaches. Methods. We have validated the use of a custom hybrid capture based next-generation sequencing (NGS) panel for comprehensive profiling of AML patients seen at our institution. This NGS panel targets the entire consensus coding DNA sequence of KMT2A; to deduce the presence of a PTD we used the relative ratio of KMT2A exons coverage. We sought to corroborate the KMT2A-PTD NGS results using (1) multiplex-ligation probe amplification (MLPA) and (2) optical genome mapping (OGM). Results. We analyzed 932 AML cases and identified 41 individuals harboring a KMT2A-PTD. MLPA, NGS and OGM confirmed the presence of a KMT2A-PTD in 22 of the cases analyzed where orthogonal testing was possible. Two false positive PTD calls by NGS could be explained by the presence of cryptic structural variants impacting KMT2A and interfering with PTD analysis. OGM revealed the nature of these previously undetected gene rearrangements in KMT2A, while MLPA yielded in-conclusive results. MLPA analysis for KMT2A-PTD is limited to exon 4, whereas NGS and OGM resolved KMT2A-PTD sizes and copy number levels. Conclusion. KMT2A-PTDs are complex gene rearrangements that cannot be fully ascertained using a single genomic platform. MLPA, NGS panels and OGM are complementary technologies applied in standard of care testing for AML patients. MLPA and NGS panels are designed for targeted copy number analysis; however, our results show that integration of concurrent genomic alterations is needed for accurate KMT2A-PTD identification. Unbalanced chromosomal rearrangements overlapping with KMT2A can interfere with the diagnostic sensitivity and specificity of copy number based KMT2A-PTD detection methodologies.

Previous high throughput screening studies indicated trans-eQTLs tend to be tissue specific. In this study, I probed if this feature can be used to identify tissue-specific gene regulatory networks. eQTL data (P<5e-8) for 16,259 genes were identified and their summary association statistics were obtained from the eQTLGene database. Next, eQTLs that display both cis and trans regulatory effects were selected and the association between their corresponding genes were examined using Mendelian randomization. A total of 169 genes that exerted trans-regulatory impacts on 692 genes were identified. 90% of the genes (N=749) aggregated into a gene regulatory network significantly enriched in hemo-immune processes. The robustness of finding was confirmed through simulation. The identified network displayed the scale-free topology. This provided the reason to examine the association of the network’s hub genes with the phenome. The outcome of analyses revealed GSDMB and ORMDL3 impact several disorders of immune origin and ALDH2 overexpression contributes to obesity. This study reports trans-eQTLs can be used to identify tissue-specific gene regulatory networks and describes a workflow to achieve this purpose. The network identified in this study showed scale-free topology indicating the hub genes of a GRN could be targeted to prevent disease outcomes.

The paper presents the problem of damage detection in steel girders. Static displacements at the selected point of the structure play the role of measured variables. Structural response signal decomposition is performed according to the Mallat pyramid algorithm, which is used to perform the discrete wavelet transform (DWT). This procedure allows us to quite well determine the location of structural damage. The geometry and the placement of any defective part of the structure may have a random character. It can be assumed that the random processes occurring in the broadly understood structure mechanics are Gaussian in nature. The first four probabilistic moments are estimated using three approaches independent: semi-analytical (SAM), perturbative (SPT), and Monte-Carlo simulations (MCS). The semi-analytical random approach seems to be the most optimal due to the necessary computation time. The incorporation of the mathematical stochastic apparatus into the classical (deterministic) analysis of the statics of the structure makes it possible to estimate the reliability measures of the analyzed girder.

In a vacuum tube, two identical and parallel Ag-O-Cs surfaces, with a work function of approximately 0.8eV, ceaselessly emit thermal electrons at room temperature. The thermal electrons are so controlled by a static uniform magnetic field that they can fly only from one Ag-O-Cs surface to the other, resulting in a potential difference and an electric current, and transferring a power to a resistance outside the tube. The ambient air is a single heat reservoir in the experiment, and all the heat extracted by the tube from the air is converted into electric energy without producing any other effect. The authors maintain that the experiment is in contradiction to the Kelvin statement of the second law of thermodynamics.

Analysis of plesiosaur swim dynamics by means of a digital 3D armature (wireframe “skeleton”) of a pliosauromorph (“Ava”) demonstrates that: 1, plesiosaurs used all four flippers for primary propulsion; 2, plesiosaurs utilized all four flippers simultaneously; 3, respective pairs of flippers of Plesiosauridae, front and rear, traveled through distinctive, separate planes of motion, and; 4, the ability to utilize all four paddles simultaneously allowed these largely predatory marine reptiles to achieve a significant increase in acceleration and speed, which, in turn, contributed to their sustained dominance during the Mesozoic.

Considering characteristics of high river density, special underlying surface in plain river network region, and municipal drainage system function, river network drainage unit model is proposed, which is defined as a region surrounded by main river or embankment. Flood storage variety and control projects regulation of small rivers in each unit is simulated. With drainage catchment as an object, according to its drainage capacity, simplified municipal drainage model was developed. Coupling river network drainage unit model, simplified municipal drainage model and 2D flood routing model, urban flood analysis model for plain tidal river network region was developed, which could be applied to analyzing flood from upstream river, storm surge and local rainfall. Demonstration research was carried out in Puxi flood protected area in Shanghai.

In this paper, the deformation behavior of S32750 duplex stainless steel during low cycle fatigue was studied by controlling number of cycles. The microstructure of the specimens under different cycles was characterized by optical microscope (OM), scanning electron microscope (SEM), electron backscatter diffraction (EBSD) and transmission electron microscope (TEM). Microhardness of two phases was measured by a digital microhardness instrument. The results showed that the microhardness of ferrite increases significantly after the first 4000 cycles, while the austenite shows a higher strain hardening rate after fatigue fracture, and the microhardness of ferrite and austenite increases by 23 HV and 87 HV, respectively. Two-phase kernel average misorientation (KAM) diagram showed that the continuous accumulation of plastic deformation easily leads to the initiation of cracks inside the austenite and at the phase boundaries. The evolution of dislocation morphology in two phases was obviously different. With the increase of cycle number, the dislocation in ferrite gradually transforms from dislocation bundles and dislocation array to sub-grain structure, while the dislocation in austenite gradually develops from dipole array to ordered Taylor lattice network structure.

We study L_2-approximation problems in the weighted Hilbert spaces in the worst case setting. Three interesting weighted Hilbert spaces appear in this paper, whose weights are equipped with two positive parameters $\ga_j$ and $\az_j$ for $j=1,\,2,\dots,\,d$. We consider the worst case error of algorithms that use finitely many arbitrary continuous linear functionals. We discuss the exponential convergence-(t,s)-weak tractability (EC-(t,s)-WT) of these L_2-approximation problems under the absolute or normalized error criterion. In particular, we obtain the sufficient and necessary conditions for EC-(1,1)-WT and EC-(t,1)-WT with t

This review examines additive manufacturing for refractory tungsten (W) and its alloys. It emphasizes the primary challenges and determining factors involved in additive manufacturing pure W, W alloys, and composites. Our focus in this regard extends to both process and alloying strategies designed to address critical issues such as densification, micro-cracking, and mechanical properties in tungsten-based components produced through additive techniques. Throughout the review, we organize existing knowledge and insights into convenient tables, serving as valuable resources for researchers embarking on a deeper exploration of these topics.

The European Green Deal poses a two-pronged challenge for the automotive industry: migrating to solutions based in light structures, requiring light weight concepts and light materials, while at the same time avoiding dependence towards the importation of these advanced materials. Aluminium alloys are lightweight and cost-effective materials that can successfully cover the requirements of many structural applications, however their production requires from bauxite and other Critical Raw Materials (CRM), such as Si and Mg. Aluminium alloys are fully recyclable, but scrap is usually contaminated and its use is related to an increment of impurities, tramp element and undesired inclusions. Traditionally, the use of secondary alloys has been restricted to low performance applications. The present work analyses the effect that the use of scrap has in the quantity of inclusions present in the alloy and in other properties relevant for the material processing. The study was carried out for common alloys belonging to 3 of the most common aluminium processes used in the car manufacturing industry: High Pressure Die Casting (HPDC) (EN AC-43500), extrusion (6063) and sheet metal forming (5754 and 6181). Reference alloy were mixed with different level of scrap (0, 20, 40, 60, 80 and 100%) trying to keep the chemical composition as unaffected as possible. The inclusions level of the alloy was characterized with the Prefil Foot-printer® test. In addition, the obtained materials, after being cast in an open mould, were subjected to metallographic characterization. Relevant properties to assess the formability quality of the alloys for the corresponding transforming process, were obtained: flowability test for HPDC alloy and high temperature compression for extrusion alloys. The results obtained suggest that the number of inclusions present in the melt highly increase with the amount of scrap used to produce the alloy. These inclusions are also related with an important lost of flowability, but do not have a noticeable impact on microstructure.

Quaking aspen (Populus tremuloides Michx.) is an important deciduous species in western U.S. forests. Existing maps of aspen distribution are based on Landsat imagery and often miss small stands (&lt;0.09 ha or 30 m²), which rapidly regrow when managed or following disturbance. In this study, we present methods for deriving a new regional map of aspen forests using one year of Sentinel-1 (S1) and Sentinel-2 (S2) imagery in Google Earth Engine. We assessed the annual phenology of aspen in the Southern Rockies and leveraged the frequent temporal resolution of S1 and S2 to create ecologically relevant seasonal composites. Additionally, we derived spectral indices and radar textural features targeting the canopy structure, moisture, and chlorophyll content. Using spatial block cross-validation and Random Forests, we assessed the accuracy of different scenarios and selected the best performing set of features for classification. Comparisons were then made with existing landcover products across the study region. The resulting map improves on existing landcover products in both accuracy (average F1-score = 93.1%) and detection of smaller forest patches. These methods enable accurate mapping at spatial and temporal scales relevant to forest management for one of the most widely distributed tree species in North America.

High dimensional statistical analysis for the yield of large-scale circuits is quite difficult due to expensive simulations, especially for the memory circuits with high sigma requirement (e.g., SRAM). In this paper, we developed an efficient sparse additive model to substitute simulations. To fit high sigma region accurately, the modeling center is moved to near failure boundary searched by scaling the shape of sampling function. To solve the model efficiently, the process variables are grouped by standard cells so that the model can be solved by our developed blockwise greedy algorithm. The experiments on the 28nm memory circuits validate that our method achieves high accuracy and efficiency compared with other state-of-art works.

Healthcare professionals constantly face situations that reflect ecosystems’ degradation. These can negatively affect their mental health. Research suggests that hope and resilience can play an important role in this scenario, since they are related to/predict mental health in highly heterogeneous samples (considering geography, age, profession, health, etc.). In this context, the aims of the present study are: to characterize and explore the relationship between hope, resilient coping and mental health of Portuguese healthcare professionals. Using Google Forms, 276 healthcare professionals answered to the GHQ-28, the (adult) Trait Hope Scale, and the Brief Resilient Coping Scale (cross-sectional study). The minimum and maximum possible scores were reached, with the exception of the maximum score of GHQ-28-Total. Regarding Hope, 19.6% scored below the midpoint (M=43.46, SD=11.97); 29.3% revealed low resilience (M=14.93, SD=4.05); and the average of 4 of the 5 Mental Health scores (exception: Severe Depression) indicates the probability of a psychiatric case. Hope correlated with Social Dysfunction and GHQ-28-Total; resilient coping proved to be a (weak) predictor of 4 of the 5 GHQ-28 indicators (exception: Severe depression). The results support the need to promote the sample's mental health, hope and resilient coping. They also suggest that stimulating resilient coping may contribute to improving healthcare professionals’ mental health.

Large Language Models (LLMs) have demonstrated promising capabilities to solve complex tasks in critical sectors such as healthcare. However, LLMs are limited by their training data which is often outdated, the tendency to generate inaccurate ("hallucinated") content and a lack of transparency in the content they generate. To address these limitations, retrieval augmented generation (RAG) grounds the responses of LLMs by exposing them to external knowledge sources. However, in the healthcare domain there is currently a lack of systematic understanding of which datasets, RAG methodologies and evaluation frameworks are available. This review aims to bridge this gap by assessing RAG-based approaches employed by LLMs in healthcare, focusing on the different steps of retrieval, augmentation and generation. Additionally, we identify the limitations, strengths and gaps in the existing literature. Our synthesis shows that proprietary models such as GPT-3.5/4 are the most used for RAG applications in healthcare. Also, there is a lack of standardized evaluation frameworks for RAG-based applications. In addition, the majority of the studies do not assess or address ethical considerations related to RAG in healthcare. It is important to account for ethical challenges that are inherent when AI systems are implemented in the clinical setting. Lastly, we highlight the need for further research and development to ensure responsible and effective adoption of RAG in the medical domain.

Proteomics research has become one of the most important topics in the fields of life science and natural science. At present, research on protein–protein interaction networks (PPINs) mainly focuses on detecting protein complexes or function modules. However, existing approaches are either ineffective or incomplete. In this paper, we investigate function module detection mechanisms in PPIN, including open databases, existing detection algorithms and recent solutions. After that, we describe the proposed solution based on simplified swarm optimization (SSO) algorithm and gene ontology knowledge. The proposed solution implements SSO algorithm for clustering proteins with similar function, and imports biological gene ontology knowledge for further identifying function complexes and improving detection accuracy. Furthermore, we use four different categories of species dataset for experiment: Fruitfly, Mouse, Scere, and Human. The testing and analysis result show that the proposed solution is feasible, efficient and could achieve a higher accuracy of prediction than existing approaches.

A new model for international bed numbers is applied to local area populations for English Clinical Commissioning Groups (CCGs) where the Age Standardized Mortality Rate (ASMR) is used as a measure of population health and hence for total occupied hospital bed demand (elective plus emergency admission types, including acute, rehabilitation, mental health, and maternity), rather than simple bed availability. A 100-unit increase in ASMR (European Standard population) leads to a 15.3 – 30.7 (feasible range) unit increase in occupied beds per 1,000 deaths. ASMR shows why the Australian states of the Northern Territory and Tasmania have an intrinsic higher bed demand than the other states – although in both cases the bed planning methodologies failed to indicate this need and both states show a large bed deficit. Due to the high volatility in bed demand the average bed demand for 2019 was determined by a linear trend using data for 2014/15 to 2019/20 — as the pre-COVID-19 baseline. The slope of the relationship between occupied beds per 1,000 deaths and deaths per 1,000 population shows a power law function. As is to be expected there is scatter around the trend line which will partly arise from uncertainty in the expected occupied beds in 2019, fluctuations in both ASMR and deaths per 1,000 population from one year to another, changes in the number of persons on the elective, outpatient and diagnostic waiting lists, and local area variation in births and consequently upon maternity, neonatal and pediatric bed demand. Additional variation in bed demand will arise from differences in the level of funding for social care, especially elderly care, and to a lesser extent upon patients using private health care, and charitable hospices for palliative care. However, after including the change in bed demand arising from ASMR the model is sufficiently accurate to identify gross differences in bed demand as a prelude to further investigation. The model requires further extension to cover specialty-level demand, and perhaps segregation between elective and emergency types of inpatient demand. A method for benchmarking the whole hospital’s average bed occupancy is given which enables them to run at optimum efficiency and safety. Using this method English hospitals can be shown to operate at highly disruptive and unsafe levels of bed occupancy leading to deleterious ‘turn-away’. Turn-away implies bed unavailability for the next arriving patient. The problems associated with crafting effective bed planning are illustrated using the English NHS as an example.

(1) Background: Multiple alterations of cellular metabolism have been documented in experimental studies of autosomal dominant polycystic kidney disease (ADPKD) and are thought to contribute to its pathogenesis. (2) Methods: To elucidate the molecular pathways and transcriptional regulators associated with the metabolic changes of renal cysts in ADPKD, we compared global gene expression data from human PKD1 renal cysts, minimally cystic tissues (MCT) from the same patients, and healthy human kidney cortical tissue samples. (3) Results: We found PKD1 renal cysts displayed the Warburg effect with gene pathway changes favoring increased cellular glucose uptake and lactate production, instead of pyruvate oxidation. Additionally, mitochondrial energy metabolism was globally depressed, associated with downregulation of gene pathways related to fatty acid oxidation (FAO), branched-chain amino acid (BCAA) degradation, the Krebs cycle, and oxidative phosphorylation (OXPHOS) in renal cysts. Activation of mTORC1 and its two target proto-oncogenes, HIF-1α and MYC, was predicted to drive the expression of multiple genes involved in the observed metabolic reprogramming (e.g., GLUT3, HK1/HK2, ALDOA, ENO2, PKM, LDHA/LDHB, MCT4, PDHA1, PDK1/3, MPC1/2, CPT2, BCAT1, NAMPT); indeed, their predicted expression patterns were confirmed by our data. Conversely, we found AMPK inhibition was predicted in renal cysts. AMPK inhibition was associated with decreased expression of PGC-1α, a transcriptional coactivator for transcription factors PPARα, ERRα, and ERRγ, all of which play a critical role in regulating oxidative metabolism and mitochondrial biogenesis. (4) Conclusions: These data provide a comprehensive map of metabolic pathway reprogramming in ADPKD and highlight nodes of regulation that may serve as targets for therapeutic intervention.

The interest in the Spark Plasma Sintering (SPS) technique has continuously increased over the last few years. This article shows the possibility of the development of a SPS device used for material processing and synthesis both in both scientific and industrial applications. This work presents an example of processing Arc-Meleted (half-Heusler, cobalt triantimonide) and SHS-synthesized semiconductors (bismuth telluride) materials with SPS device. The system functionalities expansion is presented, showing the possible way of increasing the information amount obtained about SPS processes and using the SPS apparatus for conducting synthesis of materials and increasing reproducibility and accuracy of process parameters control.

The Food and Drug Administration’s 2011 Process Validation Guidance and International Council for Harmonization Quality Guidelines recommend continued process verification (CPV) as a mandatory requirement for pharmaceutical, biopharmaceutical, and other regulated industries. As a part of product life cycle management, after process characterization in stage 1 and process qualification and validation in stage-2, CPV is performed as stage-3 validation during commercial manufacturing. CPV ensures that the process continues to remain within a validated state. CPV requires the collection and analysis of data related to critical quality attributes, critical material attributes, and critical process parameters on a minimum basis. Data is then used to elucidate process control regarding the capability to meet predefined specifications and stability via statistical process control tools. In statistical process control, control charts and Nelson rules are commonly used throughout the industry to monitor and trend data to ensure that a process remains in control. In the control chart, the mean value is taken as the center line, and 3σ limits are placed on either side of the center line. In CPV, manufactured lots are summarized, trended, and discussed at a cross-functional level at pre-defined intervals (either weekly, bi-weekly, or even monthly) based on the batch run rate. A cross-functional team assesses details regarding 1) violation frequency, 2) statistical strength of the signal, 3) violation magnitude and its impact on process or quality, and 4) any potential root causes for out-of-expectation or out-of-trend signals. The cross-functional team must analyze whether any signal identified by the control chart is reliable and must determine whether corrective action should be taken and justify the decision in the CPV meeting minutes based on statistical strength and reliability of the signal. However, basic control charts are susceptible to false alarms and nuisance alarms. Therefore, it is imperative to understand the assumptions behind control charts and the inherent false alarm rates for different Nelson rules. In this article, the authors have detailed the assumptions behind the usage of control charts, the rate of false alarms for different Nelson rules, the impact of skewness and kurtosis of a data distribution on the false alarm rate, and methods for optimizing control chart design by reducing false alarm rates and nuisance signals.

In the process of oil and gas development and production, when the flow rate in the wellbore is large, the materials underground will be eroded, causing the operation to be unable to proceed smoothly and reducing the efficiency of oil and gas production. Therefore, optimizing the anti-erosion coating under high-speed flow conditions in wellbore is a very interesting thing. It can greatly improve operational efficiency. In this paper, laser cladding was used to prepare the new coating used in the PDC drill bit. Then, the effects of tungsten carbide and multi-layer graphene on the microstructure and properties of the tungsten carbide coating were studied. The research results indicate that, for new coatings, 60% tungsten carbide and 0.3% graphene are the optimal ratios. After adding WC, the hardness has significantly improved. However, when the WC content further increases more than 30%, the increase in hardness is relatively small. In addition, when the content of graphene is more than 0.3%, the branched structure became thicker, and not only the segregation of Cr, Si and W became very obvious again, but also the segregation of Fe occurred at the Ni enrichment site. The research results contribute to improving oil and gas production and operational efficiency under high-speed fluid flow conditions in the wellbore.

Every scholarly journal's ultimate objective and purpose is to publish high-quality work. This article discusses the adoption of a worldwide virtual writing camp as an alternate approach given by journal editorial boards to increase the quality of prospective authors during the current Sars-Cov-2 pandemic, when face-to-face meetings are impractical. A virtually entire training program was designed for those interested in submitting their manuscript to journals but lack the necessary understanding and expertise to write a so-called high-quality paper. This strategy consisted of nine phases and required around three months to complete. By the end of the camp, participants should have written one or more papers that were ready to send and submit.

The accurate detection of clouds is an important first-step in the processing of remotely sensed satellite data analyses and subsequent cloud model predictions. While initial cloud retrieval technology began with the exploitation of one or two bands of satellite imagery, it has accelerated rapidly in recent years as sensor and retrieval technology have exploded, creating a new era in space exploration. Additionally, initial emphasis in satellite retrieval technology focused on cloud detection for cloud forecast models, more recently cloud screening in satellite acquired data is playing an increasingly critical role in the investigation of cloud free data for the retrieval of soil moisture, vegetation cover, ocean colour concentration and sea surface temperatures, as well as environmental monitoring of a host of products, e.g. atmospheric aerosol data, to study the Earth’s atmospheric and climatic systems. With about 60% of the Earth covered by clouds, on average, it is necessary to both accurately detect clouds in remote sensing data to screen cloud contaminate data in remote sensing analyses. In this paper, the evolution of cloud detection technology is highlighted with advancement in sensor hardware technology and possible AI algorithmic advances. Additionally, a discussion is presented on methods to obtain the cloud truth data needed to determine the accuracy of these cloud detection approaches.

The paper delves into the critical issue of damage detection within the guy cable of a truss steel mast, a pivotal component in structural integrity. It introduces a model wherein damage manifests as a localized cross-section reduction in a single element of the cable. Employing Discrete Wavelet Transform (DWT), a pioneering methodology, the study scrutinizes the behavior of the affected elements through static structural analyses. Signal decomposition via the Mallat pyramid algorithm facilitates comprehensive examination. Static displacements at the connection point between the cable and the truss mast serve as the measured variables. Through systematic investigation, the paper evaluates the impact of damage size and location, external loading force, and cable tension force on the efficacy of the proposed approach. Utilizing data derived from Finite Element Method (FEM) computations for wavelet analysis the authors substantiate the findings with numerical examples, thus offering valuable insights into damage detection strategies for structural health monitoring and engineering applications.

Objective. To test the hypothesis that a session of unilateral tSMS over the dominant primary motor cortex (M1) in healthy subjects produces a transient decrease in manual dexterity. Methods. A triple-blinded randomized control trial was conducted in 40 healthy participants who were allocated to real- or sham-tSMS group. Each participant underwent one session of tSMS and were assessed before, immediately after, 15 minutes, and 40 minutes after stimulation. Outcome measures were Finger Tapping Test (FTT), 5-seconds FTT, nine Hole Peg Test, and the hand grip assessment. Results: Significant differences were found between groups 15 minutes after stimulation in favor of the tSMS group in the FTT (2.2 ± 0.99; CI= .18-4.22; d= .84p=.034), and 5-second FTT (4.2 ± 1.98; CI= .199-8.2, d= .68; p=.04). In the intragroup analysis, a significantly lower number of taps 15 minutes after stimulation was observed in sham group (3.3 ± 1.01; CI= .47-6.13; d= .42; p=.015), but not in the real stimulation group. Conclusion: A unilateral session of tSMS in the M1 of the dominant hemisphere of healthy individuals does not reduce manual dexterity or grip strength. This technique could have benefits on the motor performance of the arm contralateral to the stimulated motor cortex

This study aimed to solve the problems of sealing and tearing failure of packer’s rubber cylinders during CO2 downhole injection. Using the simulation software, Comsol, a rubber cylinder model in a supercritical CO2 (SC-CO2) environment was established. The thermal analogy method was used to simulate the CO2 diffusion and rubber cylinder swelling process, thus we analyzed the deformation and stress of the rubber cylinder caused by temperature, pressure and CO2 as swelling agent. Results showed that, in the SC-CO2 environment, under the influence of CO2 diffusion and the consequent swelling, the rubber cylinder body is prone to large deformation, and the maximum shear stress is significantly increased, leading to shear failure of the rubber cylinder. The reduction of the initial seating pressure can alleviate the impact of deformation, whereas the reduction of the maximum contact pressure could cause the rubber cylinder to lose its seal. We also analyzed the change rules between various factors and the maximum contact pressure of the rubber cylinder, which provides a theoretical basis and technical support for improving the sealing performance of the packer’s rubber cylinder in the SC-CO2 environment.

Fire detection from satellite sensors relies on an accurate estimation of the unperturbed state of a target pixel, from which an anomaly can be isolated. Methods for estimating the radiation budget of a pixel without fire depend upon training data derived from the location's recent history of brightness temperature variation over the diurnal cycle, which can be vulnerable to cloud contamination and the effects of weather. This study proposes a new method that utilises the common solar budget found at a given latitude in conjunction with an area's local solar time to aggregate a broad-area training dataset, which can be used to model the expected diurnal temperature cycle of a location. This training data is then used in a temperature fitting process with the measured brightness temperatures in a pixel, and compared to pixel-derived training data and contextual methods of background temperature determination. Results of this study show similar accuracy between clear-sky medium wave infrared upwelling radiation and the diurnal temperature cycle estimation compared to previous methods, with demonstrable improvements in processing time and training data availability. This method can be used in conjunction with brightness temperature thresholds to provide a baseline for upwelling radiation, from which positive thermal anomalies such as fire can be isolated.

As critical biocontrol agents, Trichoderma and Metarhizium have demonstrated vast potential in disease prevention and pest control respectively. Current applications, however, are predominantly confined to singular use or simplistic combinations of the two fungi. This study innovatively utilizes Metarhizium and Trichoderma as experimental subjects, employing non-targeted metabolomics to compare the differences in metabolites between co-culture and separate fermentation. The metabolite distinctions in co-culture are meticulously analyzed using sample correlation, least squares discriminant analysis, cluster analysis, and KEGG enrichment analysis. Metabolome sequencing analysis is then applied to compare the co-culture fermentation filtrate with Trichoderma atroviride and Metarhizium anisopliae. Differential metabolite analysis found that co-culture had significantly up-regulated or down-regulated metabolites compared to singular cultures.However,significant metabolite discrepancies are discovered in singular cultures, notably the up-regulated metabolites such as osthole and spirocyclic amine, which have documented antibacterial functions that may prove effective against Fusarium graminearum. Ultimately, the findings from this research contribute to the exploration of synergistic applications of biocontrol fungi and serve as a valuable reference for future development of bio-pesticides for disease and pest control.

Wind turbines (WTs) are clean a renewable energy source that has been growing in popularity in recent years. Gearboxes are complex, expensive, and critical components of WT, which are subject to high maintenance costs and several stresses including high loads and harsh environments that can lead to failure with significant downtime and financial losses. This paper focuses on the development of a digital twin-based approach for the modelling and simulation of WT gearboxes with the aim to improve their design, diagnosis, operation, and maintenance by providing insights into their behavior under different operating conditions. Pow-erful commercial computer-aided design tools (CAD) and computer-aided engineering (CAE) software are embedded into a computationally efficient multi-objective optimization framework (modeFrontier) with the aim of maximizing the power density, compactness, performance, and reliability of the WT gearbox. This entails minimizing its weight, volume, and maximum stresses and strains achieved without compromising efficiency. The 3D CAD model of the WT gearbox is carried out using SolidWorks, the Finite Element Analysis (FEA) to obtain the stresses and strains fields are modelled using Ansys Workbench, while the multibody kinematic and dynamic system is analyzed using MSC Adams. The method has been successfully applied to different case studies to find the optimal design and analyze the performance of the WT gearboxes. Simulation results can be used to identify potential failure modes and optimize gearbox design to improve performance, extend service life and relia-bility, thereby ensuring proper operation over its lifetime and reducing maintenance costs.

The Solvency II regulatory framework specifies procedures and parameters for determining solvency capital requirements (SCRs) for insurance companies. The proposed standard SCR calculations involve two steps. The Value–at–Risk (VaR) of each risk driver is measured and, in a second step, all components are aggregated to the company’s overall SCR, using the Standard Formula. This formula has two inputs: the VaRs of the individual risk drivers and their correlations. The appropriate calibration of these input parameters has been the purpose of various Quantitative Impact Studies that have been conducted during recent years. This paper demonstrates that the parameter calibration for the equity–risk module—overall, with about 25%, the most significant risk component—is seriously flawed, giving rise to spurious and highly erratic parameter values. As a consequence, an implementation of the Standard Formula with the currently proposed calibration settings for equity–risk is likely to produce inaccurate, biased and, over time, highly erratic capital requirements.

Automobile industry is recognized as one of the most influential sectors shaping the global economies, societies, and individual lifestyles. Therefore, fierce competitions among different companies are continuously undergoing, and innovations deserve special attention to improve the competitiveness. In the past several years, additive manufacturing (AM) is emerging as an innovative technology in the applications of automobile industry with significant advantages over traditional techniques. As a result, increasing attention has been attracted to combining AM technology with the development of automobile industry. Currently, many automobile players are optimizing their industrial layout by incorporating with innovative AM techniques, and meanwhile, many research progresses have been achieved to meet the market demand. This article aims at presenting a timely review to conclude the recent advances in the application of AM techniques in automobile industry, focusing on the available AM techniques, printable materials, and the industry applications. The future research opportunities and challenges are also outlooked. Hopefully, this work can be useful to the related researchers, as well as the game players in the industry of this field.

High-power lasers play an important role in modern science, industry, and medicine. A signifi-cant milestone was reached on December 5, 2022, when Inertial Confinement Nuclear Fusion (ICF) achieved scientific breakeven, releasing more energy than the input laser energy. Additionally, Extreme Ultraviolet Lithography (EUVL) has enabled the development of microchips with 3 nm process nodes, marking a leap in semiconductor technology. These examples together with the recent achievement of a 10 PW (1015 W) laser output heralds remarkable advancements in technology and science soon. Laser systems are broadly classified based on their operating re-gimes into two main categories: Continuous Wave (CW) operation, where the laser is continuously pumped and emits a steady beam of light, and the pulsed regime, in which the laser pro-duces single or multiple pulses at various repetition rates. This review will primarily focus on pulsed laser systems, exploring their various types and recent technological advancements.

Simulation tools capturing the interactions of communication and electrical system operation represent a powerful support for fully assessing the potential benefits and impacts of ICT in future smart power distribution network. A strong interest is upon the possibility of exploiting the last generation communication systems for supporting the transition of distribution network towards a smart grid scenario. Having in mind the above, the authors propose a numerical co-simulation tool useful to thoroughly understand the impact of the communication networks on the performance of whole power system dynamics. The co-simulation tool has been purposely developed to simulate the highly time-critical smart grid application of fault management and network reconfiguration and permits reproducing and evaluating the behavior of the public mobile telecommunication system 4G Long Term Evolution (LTE), as communication technology for smart grid applications. Results of the paper demonstrates that LTE provides good performances for supporting the data communication required to perform fault location, extinction and a subsequent network reconfiguration in smart power distribution networks.

With the rapid growth of large language models (LLMs), there is increasing demand for memory and computation in LLMs. Recent efforts on post-training pruning of LLMs aim to reduce the model size and computation requirements, yet the performance is still sub-optimal. In this paper, we present a plug-and-play solution for post-training pruning of LLMs. The proposed solution has two innovative components: 1) Relative Importance and Activations (RIA), a new pruning metric that jointly considers the weight and activations efficiently on LLMs; and 2) Channel Permutation, a new approach to maximally preserve important weights under N:M sparsity. The two proposed components can be readily combined to further enhance the N:M semi-structured pruning of LLMs. Our empirical experiments show that RIA alone can already surpass all existing post-training pruning methods on prevalent LLMs, e.g., LLaMA ranging from 7B to 65B. Furthermore, N:M semi-structured pruning with channel permutation can even outperform the original LLaMA2-70B on zero-shot tasks, together with practical speed-up on specific hardware. Our code is available at: https://github.com/biomedical-cybernetics/Relative-importance-and-activation-pruning

Real time condition monitoring and precision health assessment system is a mandatory need for effective maintenance program in industrial sector. Rapid advancement in the information technology and other engineering technologies have invited more proactive attention from research and development in industrial sectors and particularly in condition monitoring of machines and related Industrial processes. In this work, the drill bit condition monitoring techniques have been developed based on the wavelet analysis and artificial neural network (ANN) as automatic drill bit fault detection and classification. An experimental work has been conducted to capture the vibration signals for analysis. In this experiment, the CNC drill machine is used with high carbon steel drill bit and mild steel material as work piece. The cutting condition parameters are kept constant and the wear level is changed. The Data Acquisition system (DAQ) with Lab VIEW software is used to capture the vibration signals for drill bit with different wear conditions. The captured vibrations data are analyzed using continuous wavelet transform (CWT) with Morlet wavelet and Daubechies wavelet as a prime function. In general, the CWT coefficient is used to generate the inputs features to ANN for automatic tool condition classification, with two outputs (0, 1) for healthy and (1, 0) for faulty. The results show the effectiveness of the combed WT and ANN for automatic classification of tool wear conditions with high success rate.

The evolution of the H5 highly pathogenic avian influenza (HPAI) viruses has led to the emergence of distinct groups with genetically similar clusters of hemagglutinin (HA) sequences. In this study, a consensus H5 HA sequence was cloned into the baculovirus expression system. The HA protein was expressed in baculovirus-infected insect cells and utilized as the antigen for the production of an oil emulsion-based H5 avian influenza vaccine (rBacH5Con5Mut). 21-day old SPF chickens were immunized with this vaccine and then challenged at 21 days post-vaccination (dpv) with clade 2.3.2.1, clade 2.3.4.4 and clade 7.2 of H5 HPAI virus, respectively. The chicken sera of vaccinated chickens exhibited high hemagglutination inhibition (HI) titers against the rBacH5 vaccine antigen, while lower HI titers were observed against the different challenge virus H5 hemagglutinins. Furthermore, the rBacH5Con5Mut vaccine provided 100% protection from mortality and clinical signs. Virus isolation results showed that oropharyngeal and cloacal shedding was prevented in 100% of the vaccinated chickens when challenged with clade 2.3.2.1 and clade 2.3.4.4 H5 viruses. When the rBacH5Con5Mut vaccine candidate was administrated at one day of age, 100% protection was demonstrated against challenge of clade 2.3.4.4 virus at 3 weeks of age, indicating the potential of this vaccine for hatchery vaccination. Overall, the rBacH5Con5Mut vaccine candidate with a consensus HA antigen and a single dose can protect chickens against different clades of H5 HPAI viruses throughout the rearing period of broiler chickens without a boost, thus fulfilling the criteria for an efficacious broad-spectrum H5 avian influenza vaccine.

Background and Objectives: Knee osteoarthritis (KOA) is a degenerative disease that is targeting continuously people of different ages, but especially the elderly population, the number of which tends to increase continuously at the global level. Apart from age, excess weight can influence the evolution of the disease, with obesity being associated with a weak inflammation stage, and an imbalance between pro-inflammatory and anti-inflammatory cytokines. The present work aimed to identify specific biomarkers alike ACRP-30, IL-10, TNF-α, and IL-6 in synovial fluid and correlate them with patients’ clinical data. Materials and Methods: 24 subjects with KOA and over 50 years of age are involved in the present study. All patients performed a supine X-ray at the target knee. Ultrasound was performed using an ESAOTE MyLab Omega (ESAOTE Spa, Italy) ultrasound device and a 6-19 MHz linear array transducer. Results: The obtained results highlighted a significant correlation between age and adiponectin level (p=0.0451, r=-0.412). Also, the IL-10 values are lower in cases where the intensity of the pain is more pronounced (p=0.0405, r=-0.421). In addition, analyzing the data by gender, it was observed that in the case of males, stiffness is more related to age (p=0.0079, r=0.7993), compared to women (p=0.0203, r=0.6223). In the case of women, the progression of the disease tends to increase more intensively total WOMAC score’s values (p=0.00031, r=0.8342), compared with men (p=0.0289, r=7013). Regarding interleukins and BMI, significant correlations were observed only in the case of men. Conclusions: A significant correlation between age and adiponectin, and adiponectin and IL-6, suggests that advanced age may contribute to adiponectin reduction. Comparing men with women, it was observed that men's age is more related to rigidity, and IL-6 and IL-10 are directly correlated to BMI and women seem to be more sensitive to pain and stiffness.

Newborn screening (NBS) is hailed as a public health success, but little is known about long-term outcomes following a positive newborn screen. There has been difficulty gathering long-term follow-up (LTFU) data consistently, reliably, and with minimal effort. Six programs developed and tested a core set of minimal LTFU data elements. After an iterative data collection process and the development of a data collection tool, the group agreed on minimal LTFU data elements. The denominator captured all infants with an NBS diagnosis, accounting for children who moved or died prior to the follow-up year. They also agreed on three LTFU outcomes: if the child was still alive, had contact with a specialist, and received appropriate care specific to their diagnosis within the year. The six programs representing NBS public health programs, clinical providers, and research programs provided data across multiple NBS disorders. In 2022, 83.8% (563/672) of the children identified by the LTFU programs were alive and living in the jurisdiction; of those, 92.0% (518/563) saw a specialist, and 87.7% (494/563) received appropriate care. The core LTFU data elements can be applied as a foundation to address the impact of early diagnosis by NBS within and across jurisdictions.

The sweetpotato whitefly, Bemisia tabaci MEAM1, is a pest known to significantly impact tomato development and yields through direct damage and virus transmission. To manage this pest, the current study compared the effectiveness of various insecticide rotations. Field trials included rotations involving synthetic insecticides, biochemicals, and microbial agents, applied according to their labeled highest concentrations. Results indicated that while standard synthetic insecticides consistently reduced whitefly egg and nymph counts significantly, microbial biopesticide rotations also achieved reductions, although less consistently. This study demonstrated that while traditional chemical treatments remain highly effective, integrating microbial biopesticides containing Beauveria bassiana and Cordyceps javanica present a viable alternative that could mitigate resistance development and reduce environmental impact. The findings suggest the potential for optimizing integrated pest management (IPM) and Insecticide Resistance Management (IRM) strategies by incorporating a variety of modes of action to sustainably manage B. tabaci MEAM1 populations in agricultural settings.

Background. Keyword co-occurrence analysis is a crucial tool for comprehending research trends, identifying relevant studies, and gaining insight into the connections between various concepts and topics. Objective. This study focuses on analyzing the co-occurrence of keywords using FP-growth algorithm and direct search methods. Materials and methods. The methodology involved extracting bibliometric data of Energies journal for 2023-2024 from MDPI publisher platform, keyword lemmatization and keyword co-occurrence estimation. Clustering and visualization were performed using Multidendrograms and Scimago Graphica software. Results. The results showed that the FP-growth algorithm can achieve a close match with the direct search results, which facilitates data preparation for clustering. In addition, finding the co-occurrence of three or more keywords significantly reduced the number of possible combinations, which allowed the identification of specific research topics. Conclusions. This study highlights the usefulness of the FP-growth algorithm in keyword analysis and provides insights into ways to refine search queries to abstract databases for the purpose of designing and writing literature and systematic reviews.

Deep learning (DL) algorithms used for DOTATATE PET lesion detection typically require large, well-annotated training datasets. These are difficult to obtain due to low incidence of gastroenteopancreatic neuroendocrine tumors (GEP-NETs), and the high cost of manual annotation. Furthermore, networks trained and tested with data acquired from site specific PET/CT instrumentation, acquisition and processing protocols have reduced performance when tested with offsite data. This lack of generalizability requires even larger, more diverse training datasets. The objective of this study is to investigate the feasibility of improving DL algorithm performance by better matching the background noise in training datasets to higher noise, out-of-domain testing datasets. 68Ga-DOTATATE PET/CT datasets were obtained from two scanners: Scanner1, a state-of-the-art digital PET/CT (GE DMI PET/CT; n=83 subjects), and Scanner2, an older-generation analog PET/CT (GE STE; n=123 subjects). Set1, the data set from Scanner1 was reconstructed with standard clinical parameters (5 minutes; Q.Clear) and list-mode reconstructions (VPFXS 2, 3, 4, and 5-minutes). Set2, data from Scanner2, representing out-of-domain clinical scans, used standard iterative reconstruction (5 minutes; OSEM). Reconstructed datasets were divided into training, validation, and testing datasets in a 60%, 20%, 20% proportion, and a deep neural network was trained with each: Network1 for Scanner1 and Network2 for Scanner2. DL performance (Network1) was tested with out-of-domain test data (Set2). To evaluate the effect of training sample size, we tested DL model performance using a fraction (25%, 50%, and 75%) of Set1 for training. Scanner1, list-mode 2-minute reconstructed data demonstrated the most similar noise level compared that of Set2, resulting in the best performance (F1=0.713). This was not significantly different compared to the highest performance, upper-bound limit using in-domain training for Network2 (F1=0.755; p-value=0.103). Regarding sample size, the F1 score significantly increased from 25% training data (F1=0.478) to 100% training data (F1=0.713; p&lt;0.001). List-mode data from modern PET scanners can be reconstructed to better match the noise properties of older scanners. Using existing data and their associated annotations dramatically reduces the cost and effort in generating these datasets, and significantly improves the performance of existing DL algorithms. List-mode reconstructions can provide an efficient, low-cost method to improve DL algorithm generalizability.

This study explores the public library service evaluation domain through user-generated content on Google Maps, highlighting digital feedback's significant yet underexplored potential in understanding public library patronage across Taiwan's six major cities. Utilizing a mixed-methods research design, this study integrates Google Maps review content analysis with Social Network Analysis to delineate public perceptions and identify areas for service enhancement in public libraries. It innovatively leverages personal experiences extracted from over 60,000 Google Maps reviews to evaluate public library services in cities such as Taipei, New Taipei, Taoyuan, Taichung, Tainan, and Kaohsiung. The research taps into the National Library of Taiwan's National Library Statistics System to provide a robust analysis of library performance and user satisfaction, offering a novel perspective by emphasizing user-centric feedback from Google Maps as a primary data source. This approach provides quantitative data on library usage and geographic distribution and enriches our understanding of the qualitative experiences of library users. In analyzing the keywords from Google Maps reviews of public libraries, we categorize and interpret these under the three core LibQUAL+ dimensions—Affect of Service, Information Control, and Library as Place. The findings expose variances in perceived service quality among the cities, with Kaohsiung and Taichung receiving the highest accolades for service satisfaction. Simultaneously, the study identifies potential areas for improvement, particularly in cities with lower satisfaction ratings like Taipei. This personalized feedback illustrates the intimate relationship between public libraries and their communities, offering invaluable insights for policymakers and library management to enhance service delivery and user experience.

The study employed simulation-based measurement approaches to evaluate and compare the performance of conventional networks to IRS-enhanced systems in terms of upstream spectral efficiency. The study discovered that applying IRS greatly improves coverage, or transmitting potency, for 6G services.

To decrease oral pathogens and increase the number of beneficial bacteria, probiotics and bioactive compounds made via microbial bioconversion are recently used. In addition, the interest regarding probiotics-mediated bioconversion with popular medicinal plants is increasing. These compounds produced during the process exhibit synergistic effect and enhance a variety of biological activities including pathogen inhibition in host. Therefore, we developed bioconverted milk with high inhibitory activity against oral pathogens and α-glucosidase. Lactiplantibacillus plantarum SMFM2016-RK was chosen as the probiotic due to its beneficial characteristics such as high acid and bile tolerance, antioxidant activity, and α-glucosidase inhibition. Based on the minimal bactericidal concentration against three oral pathogens, the following appropriate concentrations of Artemisia herba-alba extract were added to milk: 5 mg/mL of A. herba-alba ethanol extract and 25 mg/mL of A. herba-alba hot-water extract. Four bioconverted milks (BM), BM1, BM2, BM3, and BM4, were produced by combining L. plantarum SMFM2016-RK alone, L. plantarum SMFM2016-RK and ethanol extract, L. plantarum SMFM2016-RK and hot-water extract, and L. plantarum SMFM2016-RK with both extracts. As a result of antimicrobial activity, BM3 inhibited the growth of Aggregatibacter actinomycetemcomitans and Porphyromonas gingivalis the most, and BM4 suppressed the growth of Fusobacterium nucleatum the most. In addition, bioconverted milk containing A. herba-alba (BM2, BM3, and BM4) inhibited α-glucosidase more effectively than BM1. In conclusion, A. herba-alba-added milk bioconverted by L. plantarum SMFM2016-RK displayed the growth inhibitory effect on oral pathogens and the α-glucosidase inhibitory activity, thus it necessitates to evaluate the effects on the alleviation of oral diseases and glycemic control through future animal experiments.

In tropical developing regions, typhoid fever and malaria often co-occur, posing serious health risks. The two illnesses have similar symptoms, which makes diagnosis and treatment more difficult. Through a thorough analysis of existing literature on typhoid fever, malaria, and co-infection, including research articles, case studies, and review papers, this review seeks to understand the dynamic interactions between typhoid fever and malaria co-infection. The review highlights important problems with co-infection between typhoid and malaria, such as higher morbidity and death, diagnostics-related challenges, and antibiotic resistance. Overlapping symptoms and limitations in current diagnostic tests impede accuracy. Treatment plans are made more difficult by the advent of typhoid strains that are resistant to drugs. The analysis concludes by highlighting the necessity of developing more sensitive and specific diagnostic methods in order to accurately diagnose co-infections. Going forward, the review emphasizes the need for more investigation into the immunological connections between malaria and typhoid illnesses. Essentially, this paper offers a thorough summary of the difficulties and possibilities related to co-infection between typhoid and malaria. It provides a framework for upcoming studies and initiatives that seek to improve public health and patient outcomes in the face of these intricately linked health problems.

Logistics Engineering has transcended over the decades into an approach for competitive benefits in organisational performance and logistics efficiency. Most industries are recognising that significant savings are available to companies that are able to ordinate and improve within their logistics operations. Companies today face great challenges because the successful supply of many products and services needs to the effective integration of logistics activities across a prolongation supply chain and an increasing geographical separation. Moreover, logistics integration approach involves both internal integration for an ordinated, unified process as well as relationships to react flexibly, changeability, and responsibility to customer's demands. In this study, by using of Fuzzy Vikor (F-Vikor), the best combination of RIR was selected and RIR Locating was done. The main aim of this paper is survey and design of the integrated operational logistics network (IOLN) and also, a proposed IOLN to integration of the Iran Khodro Co Supply Network.

Visual question answering (VQA) is receiving increasing attention from researchers in both the computer vision and natural language processing fields. There are two key components in the VQA task: feature extraction and multi-modal fusion. For feature extraction, we introduce a novel co-attention scheme by combining Sentence-guide Word Attention (SWA) and Question-guide Image Attention (QIA) in a unified framework. To be specific, the textual attention SWA relies on the semantics of the whole question sentence to calculate contributions of different question words for text representation. For the multi-modal fusion, we propose a “Cross-modal Multistep Fusion (CMF)” network to generate multistep features and achieve multiple interactions for two modalities, rather than focusing on modeling complex interactions between two modals like most current feature fusion methods. To avoid the linear increase of the computational cost, we share the parameters for each step in the CMF. Extensive experiments demonstrate that the proposed method can achieve competitive or better performance than the state-of-the-art.

Alpha emitters like plutonium pose severe health risks when ingested, damaging DNA and potentially causing cancer. Traditional detection methods require proximity within millimeters of the contamination source, presenting safety risks and operational inefficiencies. Long-range detection through alpha radioluminescence (RL) offers a promising alternative. However, most of the previous experiments have been carried out under controlled conditions that preclude the overwhelming effect of ambient light. This study demonstrates successful detection of a 3 MBq alpha emitter in an open environment using a newly developed, lightweight compact alpha camera. This camera incorporates a deep-cooled CCD and a specially designed lens system with a low f-number to maximum the signal intensity. The lens was also designed to minimize blue shift effects of filters. Nighttime imaging was achieved with a dual-filter system using a sandwich filters assembly centered at 337 nm and 343 nm for capturing alpha RL and subtracting background light, respectively. At night, the alpha source was detected from 1 meter away within one minute. The system was also evaluated under simulated urban lighting conditions. For daytime imaging, a stack of tilted 276 nm short pass filters minimized sunlight interference, enabling clear detection of the alpha source at 70 cm within 10 minutes under indirect sunlight. This research not only underscores the viability of long-range optical detection of alpha emitters for environmental monitoring and scientific research but also highlights its ability to enhance nuclear safety and public health in real-world settings.

Shafts with a stepped shoulder are particularly well known in the field of drive technology. In combination with a form-fit shaft–hub connection, the shaft shoulder fixes the hub on the shaft as well as the absorption of the axial forces. With profiled shafts, there is a notch overlay in the shaft shoulder, involving the shaft shoulder and profile. If the hub is also connected with the profiled shaft, the hub edge acts as an additional notch in the shaft shoulder area. The multiple resulting notches have not previously been part of research activities in the field of innovative trochoidal profile connections. Compared to conventional positive-locking connections, such as the keyway connection or the involute splined shaft profile, the favourable features of trochoidal profiles have only been based on connections with smooth shafts without a shoulder in previous studies. Accordingly, this article addresses the numerical and experimental investigations of trochoidal profile connections with offset shafts for pure torsional loading. Focusing on a hybrid trochoid with four eccentricities and six drivers, a well-founded numerical and experimental investigation was carried out with numerous fatigue tests. In addition, the influence of a shaft shoulder was also demonstrated on simple epi- and hypotrochoidal profiles.

Commercial protein supplements are the main sources for the sports nutrition. Nowadays, those protein supplements mainly differ in the ingredients and formulations, possibly affecting the digestion behavior and protein absorption during gastrointestinal digestion. Herein, this work aimed to investigate the gastric digestion behavior and protein hydrolysis of commercial protein supplements by using an in vitro dynamic digestion model. Five commercial protein supplements were selected, including three protein powders and two liquid protein beverages. The results indicated that liquid protein beverages had smaller particle size than protein powder dispersions, and one of the protein powders existed as the microcapsules. During gastric digestion, all samples tended to form aggregates as the pH decreased, and the degree of aggregation was more pronounced in liquid protein beverages than in protein powders, where the intact structure of the microcapsules was gradually disrupted. Protein hydrolysis degree in liquid protein beverages was higher than that in protein powders. One of the liquid products (M20) had the highest protein hydrolysis degree of around 35.74% after 120 min gastric digestion and a half-empty time of 37.82 min. These findings suggest that the formulation and ingredients of commercial protein supplements will affect their digestion behavior, resulting in varying degree of protein hydrolysis. Hence, sports supplements should be carefully designed to provide precise nutrition for the athletes and exercisers.

Complexity science studies physical phenomena that cannot be explained by the mere analysis of the single units of a system but requires to account for their interactions. A feature of complexity in connected systems is the emergence of mesoscale patterns in a geometric space, such as groupings in bird flocks. These patterns are formed by groups of points that tend to separate from each other, creating mesoscale structures. When multidimensional data or complex networks are embedded in a geometric space, some mesoscale patterns can appear respectively as clusters or communities, and their geometric separability is a feature according to which the performance of an algorithm for network embedding can be evaluated. Here, we introduce a framework for the definition and measure of the geometric separability (linear and nonlinear) of mesoscale patterns by solving the travelling salesman problem (TSP), and we offer experimental evidence on embedding and visualization of multidimensional data or complex networks, which are generated artificially or are derived from real complex systems. For the first time in literature the TSP’s solution is used to define a criterion of nonlinear separability of points in a geometric space, hence redefining the separability problem in terms of the travelling salesman problem is an innovation which impacts both computer science and complexity theory.

This study analyses regional development in one of the poorest provinces in China, Guizhou Province, between 2000 and 2012 using a multiscale and multi-mechanism framework. In general, regional inequality has been declining since 2000. In addition, economic development in Guizhou Province presented spatial agglomeration and club convergence, which shows how the development pattern of one core area, two-wing areas and a contiguous area at the edge of the province have been developed between 2006 and 2012. Multilevel regression analysis revealed that industrialization and investment level were the primary driving forces of regional economic disparity in Guizhou Province. The influences of marketization and decentralization on regional economic disparity were relatively weak. Investment level reinforced regional economic disparity and the development of core-periphery structure in the province. However, investment level actually weakened the regional economic disparity in Guizhou Province when the variable of time was considered. In addition, both the topography and urban–rural differentiation were the two main reasons for forming a core-periphery structure in Guizhou Province.

Responding to changing value creation processes in the sheet metal working sec-tor, where the complexity and interchangeability of products challenge traditional dif-ferentiation strategies, this empirical analysis explores integrating service-oriented and data-driven business models as new paths to ensure competitiveness, especially for small and medium-sized enterprises (SMEs). This study aims to capture the current state and challenges associated with the implementation of these business models in this sector. The research was conducted through semi-structured interviews with SMEs in the industry. The findings indicate that service-oriented and data-driven business models are not yet widely adopted and that manufacturing companies re-quire support in their implementation. Fields of action were identified for the industry. These are "Creating awareness and understanding", "Recognizing added value", "In-creasing company maturity" and "Understanding the change process". Cooperation between science and industry is essential in tackling these fields of action to ensure the successful integration of such business models in manufacturing companies. This pa-per identifies challenges in the fields of action that companies must address through a structured approach promoting awareness, recognizing value, improving organiza-tional maturity, and understanding the change process to successfully implement ser-vice-oriented and data-driven business models.

Polycentric governance, as conceptualized by the Nobel Laureate Elinor Ostrom, has been widely celebrated as a promising approach to fostering inclusive innovation ecosystems in the Global South. By emphasizing the coordination of multiple decision-making centers and the engagement of diverse stakeholders, polycentric systems are believed to enhance the inclusivity and responsiveness of innovation processes, crucial for addressing societal challenges and promoting equitable development. However, this article argues that the theoretical and practical application of polycentric governance may not adequately address the inherent power structures and complexities within inclusive innovation ecosystems. Drawing on critical analysis, empirical evidence, and complementary theoretical perspectives, this paper interrogates the limitations of polycentric governance and proposes a more nuanced, multidimensional framework for inclusive innovation governance in the context of the Global South.

Artificial intelligence (AI) is a current trend in computer science, which extends itself its amazing capacities to other technologies such as mechatronics and robotics. Going beyond technological applications, the philosophy behind AI is that there is a vague and potential convergence of artificial manufacture and natural world although the limiting approach may be still very far away, but why? The implicit problem is that Darwin theory of evolution focuses on natural world where breeding conservation is the cornerstone of the existence of creature world but there is no similar concept of breeding conservation in artificial world whose things are created by human. However, after developing for a long time until now, AI issues an interesting concept of generation in which artifacts created by computer science can derive their new generations inheriting their aspects / characteristics. Such generated artifacts make us look back on offsprings by the process of breeding conservation in natural world. Therefore, it is possible to think that AI generation, which is a recent subject of AI, is a significant development in computer science as well as high-tech domain. AI generation does not help us to reach near biological evolution even in the case that AI can combine with biological technology but, AI generation can help us to extend our viewpoint about Darwin theory of evolution as well as there may exist some uncertain relationship between man-made world and natural world. Anyhow AI generation is a current important subject in AI and there are two main generative models in computer science: 1) generative model that applies large language model into generating natural language texts understandable by human and 2) generative model that applies deep neural network into generating digital content such as sound, image, and video. This technical report focuses on deep generative model (DGM) for digital content generation, which is a short summary of approaches to implement DGMs. Researchers can read this work as an introduction to DGM with easily understandable explanations.

Following the adoption of the Kunming-Montreal Global Biodiversity Framework (KM-GBF), with the Convention on Biological Diversity serving as its guiding treaty, the European Union (EU) has just reached an agreement on an EU Nature Restoration Regulation. This study carries out a systematic cross-walk between the restoration regulation and the KM-GBF, focusing on their implications for forest ecosystems. This paper identifies areas of alignment, divergence, and potential gaps related to habitat restoration, species protection, climate resilience, and the sustainable use of natural resources. The methodology adopts a grounded approach, starting with the 23 targets outlined in the KM-GBF and proceeding to the 28 articles set out in the restoration regulation. The results highlight the need for better alignment between the KM-GBF, the restoration regulation, and other forest-relevant EU policy instruments. The study stresses the need for a coherent and integrated EU policy approach that can address the diverse challenges and policy objectives facing forests. It concludes that amendments to the restoration regulation have significantly diluted its potential impact, limiting the EU Members States accountability and ability to meet KM-GBF goals and targets. It further stresses the need for strategies that can reconcile divergent EU policy pathways, support forest management and restoration efforts, while aligning with global biodiversity objectives.

Despite global efforts to improve vaccination coverage, the number of zero dose and under immunized children has increased in the African continent, particularly in Nigeria where more than 2.3 million unvaccinated children currently reside, making it the country with highest number of unvaccinated children on the African continent. It is thus imperative to identity barriers and facilitators of immunization in Nigeria with a view to formulate and design targeted interventions that can effectively bridge the immunization gap in Nigeria. In this review, we systematically mapped and summarized existing literature and data on key barriers to vaccine uptake, and facilitators influencing immunization practices in Nigeria. The electronic databases were systematically searched and articles that reported determinants of routine immunization uptake in Nigeria from inception of the databases up till October 2023 were included. The eligible articles were evaluated using a set of pre-defined criteria and the extracted data were analysed and visualized. This scoping review identified barriers spanning logistical, socio-economic, cultural, and healthcare system-related aspects, highlighting the need for a multi-pronged approach to address these challenges. While considerable progress has been made over the years, persistent barriers like distance between area of residence of caregivers and immunization centers, vaccine shortages, poor interpersonal relationship between healthcare workers and caregivers and cultural/religious influences persist. Lastly, collaborative efforts among stakeholders and communities remain paramount in ensuring improved routine immunization coverage across the Nigeria’s diverse socio-cultural landscape.

Densely urbanized areas, with a low percentage of green vegetation, are highly exposed to Heat Waves (HW) which nowadays are increasing in terms of frequency and intensity also in the middle-latitude regions, due to ongoing Climate Change (CC). Their negative effects may combine with those of the UHI (Urban Heat Island), a local phenomenon where air temperatures in the compact built up cores of towns increase more than those in the surrounding rural areas, with significant impact on the quality of urban environment, on citizens health and energy consumption and transport, as it has occurred in the summer of 2003 on France and Italian central-northern areas. In this context this work aims at designing and developing a methodology based on aero-spatial remote sensing (EO) at medium-high resolution and most recent GIS techniques, for the extensive characterization of the urban fabric response to these climatic impacts related to the temperature within the general framework of supporting local and national strategies and policies of adaptation to CC. Due to its extension and variety of built-up typologies, the municipality of Rome was selected as test area for the methodology development and validation. First of all, we started by operating through photointerpretation of cartography at detailed scale (CTR 1: 5000) on a reference area consisting of a transect of about 5x20 km, extending from the downtown to the suburbs and including all the built-up classes of interest. The reference built-up vulnerability classes found inside the transect were then exploited as training areas to classify the entire territory of Rome municipality. To this end, the satellite EO HR (High Resolution) multispectral data, provided by the Landsat sensors were used within a on purpose developed "supervised" classification procedure, based on data mining and “object-classification” techniques. The classification results were then exploited for implementing a calibration method, based on a typical UHI temperature distribution, derived from MODIS satellite sensor LST (Land Surface Temperature) data of the summer 2003, to obtain an analytical expression of the vulnerability model, previously introduced on a semi-empirical basis.

Reducing CO2 emissions is urgently needed to slow down the impacts of climate change. CO2 capture using an amine solution has been developed and implemented on pilot and commercial scales. However, amine scrubbing, in particular, produces a lot of degraded solvent as waste and is energy intensive. Solid sorbents have been called to overcome those drawbacks. In this work, waste biomass-derived carbon materials were developed and tested for CO2 capture and conversion. Advanced thermal chemical processes i.e. hydrothermal and pyrolysis were applied to produce materials from agri-food waste such as soybean and okara. It was found that the number of functional groups (-C=O and -OH) in activated carbon appears in the synthesized materials, implying the generation of surface oxygenated groups. Preliminary results showed that the synthesized activated carbons are obtained with good yields, and a relatively high surface area, which may be applied as CO2 adsorption materials to solve the CO2 emission problems.

Human keratinocytes and melanoma can stick together to form clusters after eight days in co-culture. As in dynamic system concepts, one can consider cluster formation as the system attractor, cell seeding as the initial condition, and density change over time as a path within the basin of attraction. Herein, Cellular Automata, which is a class of Agent-Based Models, and Boolean Networks are discrete modeling methods used in population dynamics such that running the dynamics of cellular automata backward reveals an underlying network in terms of basin of attraction, also known as global dynamics. Thus, we hypothesize that one can estimate the local dynamic of an agent-based model from the basin of attraction of a boolean network. Here, we propose an approach to estimating these agent-based model rules, which consists of comparing the density states within each state transition and reaching a consensus among state transitions belonging to a basin of attraction of a boolean network. The objectives of this study are: (1) to infer a boolean network from the co-culture growth curve; (2) to estimate the rules of agent-based models; and (3) to implement spatial dynamics simulations. The binarization of the growth curve shows high population density after four days; the estimated agent-based model rules were compatible with cell proliferation and migration in agreement with literature, so we had to propose individual rules for survival and death. Spatial dynamics shows that (1) keratinocytes exhibit a higher density in neighborhoods where melanoma is present; (2) the chance of keratinocyte migration increases until the fourth day, then decreases, and the probability of survival increases substantially after four days; and (3) cells with low proliferation capacity die and free space for those with high ones. Our approach suggests that the attractor state of cell co-culture is induced mainly by an increase in keratinocyte migration and survival. Our approach has the potential to offer valuable clues about microenvironmental interactions or configurations that drive population dynamics.

With advancements in digital technologies such as 5G communications, big data, and cloud computing, the components of network operation systems have become increasingly complex, significantly complicating system monitoring and maintenance. Correspondingly, automated log anomaly detection has become a crucial means to ensure stable network operation and protect them from malicious attacks or failures. Conventional machine learning and deep learning methods assume consistent distributions between the training and testing data, adhering to a closed-set recognition paradigm. Nevertheless, in realistic scenarios, systems may encounter new anomalies that were not present in the training data, especially in log anomaly detection. Inspired by evidential learning, we propose a novel anomaly detector called LogEDL, which supervises training of the model through an evidential loss function. Unlike traditional loss functions, the evidential loss function not only focuses on correct classification, but also quantifies the uncertainty of predictions. This enhances the robustness and accuracy of the model in handling anomaly detection tasks, while achieving functionality similar to open-set recognition. To evaluate the proposed LogEDL method, we conduct extensive experiments on three datasets, i.e., HDFS, BGL, and Thunderbird, to detect anomalous log sequences. Experimental results demonstrate that our proposed LogEDL achieves state-of-the-art performance in anomaly detection.

Selecting road networks in cartographic generalization has consistently posed formidable challenges, driving research towards the application of intelligent models. Despite previous efforts, the accuracy and connectivity preservation in these studies, particularly when dealing with road types of similar sample sizes, still warrant improvement. To address these shortcomings, we introduce a Heterogeneous Graph Attention Network (HAN) for road selection, where feature masking method is initially utilized to assess the significance of road features. Concentrating on the most relevant features, two meta-paths are introduced within the HAN framework: one for aggregating features of the same road type within the first-order neighborhood, emphasizing local connectivity, and another extending this aggregation to the second-order neighborhood, capturing a broader spatial context. For a comprehensive evaluation, we introduce a set of metrics considering both quantitative and qualitative aspects of the road network. Compared to traditional neural networks, our HAN model demonstrates superior accuracy and F1-score, particularly for road types with comparable samples. Additionally, it enhances the overall connectivity of the selected network. In summary, our HAN-based method provides an advanced solution for road network selection, surpassing previous approaches in terms of accuracy and connectivity preservation.

The pollution of the bays in Abidjan is a major concern for the Ivorian policy makers. In fact, the pollution of the bays induce high costs to the society while impacting population health dramatically. As a result, pollution reduction management of production activities has been undertaken in the Cocody Bay area. To our knowledge, no study has yet proposed a model to evaluate the cost-effectiveness of these pollution management strategies. A cost-effectiveness model, based on Monte Carlo simulation, was developed to assess the economic and environmental impacts of various scenarios characterized by a set of production practices, both in the short term and in the long term. The authors discuss the steps and input parameters of the model presented. The proposed model may serve as the basis for identifying an optimal production scenario defined as the scenario with the best incremental cost-effectiveness ratio considering a willingness to pay (WTP) threshold. The WTP, to be estimated based on the gross domestic product of Côte d’Ivoire, represents the opportunity costs associated with selecting the optimal scenario. The current framework can also be applied to other settings facing similar challenge.

To achieve n-type doping in diamond, extensive investigations employing first principles have been conducted on various models of phosphorus doping and boron-phosphorus co-doping. The primary focus of this study is to comprehensively analyze the formation energy, band structure, density of states, and ionization energy of these structures. It is observed that within a diamond structure solely composed of phosphorus atoms, the formation energy of an individual carbon atom is excessively high. However, the substitution of two carbon atoms leads to the conversion of diamond into a p-type semiconductor. Upon examining the P-B co-doping structure, it is revealed that the doped impurities exhibit a tendency to form more stable cluster configurations. As the separation between the freely doped atoms and the cluster impurity structure increases, the overall stability of the structure diminishes, consequently resulting in an elevation of the ionization energy. Analysis of the electronic density of states demonstrates that the electron orbit of the B atom makes negligible contributions to the impurity levels during P-B doping.

As one of the important components of underwater propulsion systems, water lubricated bearings are often failure due to mechanical wearing and vibrating, especially under high loads and prolonged friction. In this paper, a dual-network co-crosslinking strategy based on HNBR is proposed, in which the epoxy network connects with the rubber network through Epoxidized Eommia ulmoides gum. The damping, tribological and mechanical properties of the prepared composite are systematically investigated. The results show that this material has excellent friction and vibration damping properties, with a water-lubricated coefficient of friction as low as 0.022 and a wear resistance as high as 3.87 × 10-6mm3/Nm. Preparing HNBR-based composite by dual network co-crosslinked is proved being a feasible solution to improve the reliability and service life of water-lubricated bearings.

The rebound behaviors of rubber balls based on ethylene-propylene-diene monomer (EPDM) / chlorinated butyl rubber (CIIR) blends were investigated systematically. A novel method was proposed to characterize the damping capacity of the EPDM/CIIR blends. The damping capacity can be represented by the rebound height of rubber balls, and lower rebound height corresponds to better damping capacity. A mathematical model expressed by an equation obtained through theoretical derivation has been proposed to predict the rebound height of the rubber balls. The energy dissipation rate (EDR) defined by the ratio of the height loss to the rebound time was proposed to further characterize the damping capacity. The EDR value shows the highest for the pure CIIR and lowest for the pure EPDM, showing a decrease trend with the increase of EPDM content in the rubber blends. By comparison one can obtain that the damping capacity of the EPDM/CIIR blends decreases with the decrease of external excitation, the conclusion of which plays a key role in the formulation design of viscoelastic damping rubber materials.

Open source software such as OpenDSS has given a lot of help to distribution network researchers and educators. With high penetration of distributed renewable energy resources into distribution network, tradition distribution steady state analysis software such as OpenDSS is faced with difficulty in handling distributed generators. Three-phase distributed generators are often modeled in sequence frame while unbalanced distribution network are usually modeled in phase frame. So a load flow in sequence-phase coupled frame is proposed to handle models described in both frames. Voltage controlled DGs which are difficult to cope with in OpenDSS are handled in proposed program. The steady state analysis platform is programmed with open source Modelica language and the main aim of this paper is to introduce an open source platform for active distribution network steady analysis include load flow and short circuit analysis which can be easily adopted and improved by other educators and researchers.

The commercial oxygen-free copper sheets were cold-rolled with reduction rates ranging from 20% to 87% reduction rate and annealed at 400, 500 and 600 °C. The microstructure and texture evolution during cold-rolling and annealing processes were studied using optical microscopy (OM), the scanning electron microscopy (SEM) and the electron back-scattered diffraction (EBSD). The results show that the deformation textures of {123} (S) , {112} (Copper) and {110} (Brass) were continuously enhanced with the increase of cold-rolling reduction. The orientations along the α-oriented fiber converged towards Brass and the orientation density of β fiber obviously increased when the rolling reduction exceeded 60%. The recrystallization texture was significantly affected by the cold-rolling reduction. After 60% cold-rolling reduction, Copper and S textures components gradually decreased and {011} recrystallization texture component formed with the increase of annealing temperature. After 87% cold-rolling reduction, a strong Cube texture formed and other textures were inhibited with the increase of annealing temperature. The strong Brass and S deformation texture was conducive to the formation of a strong Cube annealing texture. The density of annealing twin boundary decreased with the increase of annealing temperature, and more annealing twin boundaries formed in the oxygen-free copper sheets with the increase of cold rolling reduction.

In the famous medieval magical manual called Picatrix, the unknown author describes the phenomenon of magic with the term nigromantia. As is well known, the original meaning of the Greek term necromantia has a more concise meaning. It is used for a special kind of divination, i.e., divination through the parts of a cadaver and the conjured spirit of the dead. Seemingly in the Picatrix, no necromantic ritual can be found; moreover, the author stresses that his main goal is pious, i.e., to find the path leading to the ultimate source of the universe, the one and only God. In my paper, I show that on some pages of the Picatrix, there are traces of divinatory practices that may be connected to the original meaning of the term nigromantia. In the third book of the manual, descriptions of some interesting rituals attributed to the pagan Sabeans of Harran and their teacher, the god Hermes can be found. During the practices, the magician involved conjured spirits of the heavenly bodies and powers as well. Because of this, by looking closely at and analyzing the given text, it is possible to piece together a complex web of necromantic and demonic divinatory rituals.

This project designed a set of network security mechanisms for cloud applications in VANETs. At present, cloud computing is one of the government’s development priorities in the industry. We divide cloud into public, private, and hybrid clouds in this project. Vehicles or passengers can access road conditions or public transport information through the public cloud; public transportation can access current driving records and users can access relevant enterprise information on the private cloud and hybrid cloud is a combination of public and private clouds. Both cloud computing or VANETs require network and information security. At present, many researches on VANETs security only focus on message communication, and neglect information storage. Researches on cloud computing security only focus on information protection, and neglect user privacy and anonymity. This project designed a set of network and information security mechanisms in line with the requirements of Confidentiality, Authentication, Non-repudiation, Conditional Anonymity, and Conditional Untraceability. This project primarily needs to achieve the following: 1.an authentication mechanism to verify the identity of each other between the passenger and the vehicle and to verify the identity with Single Sign-On; 2. vehicle or user privacy and anonymity, which need to be able to replace the anonymous ID and related parameters for the vehicle or the user; 3. a private communication mechanism, which enables any vehicle or user to communicate privately; and 4. an information security encryption method, which can encrypt the information on a cloud server to avoid unauthorized access by internal personnel or hackers.

Civic participation is of great significance to urban management decision-making. In order to facilitate citizens to participate in city management decision-making, this paper proposes a large-scale group decision-making (LSGDM) method based on multi-granular probabilistic linguistic preference relations (MG-PLPRs). First, each decision maker selects a language terms set from the multi-granularity language terms set to represent individual preference relations and the MG-PLPRs are obtained by statistical calculation to represent sub-group’s preferences information. Then, an optimization model based on the expected consistency of PLPR and consensus measure of groups is established to achieving consensus reaching processes, which can ensure satisfactory individual consistency and group consensus. Finally, the validity and applicability of the proposed method is verified by a case of a city "shared garden" site selection with the participation of citizens.

Materials engineering based on metal oxides for manipulating the solar spectrum and producing solar energy have been under intense investigation over the last years. In this work, we present NiO thin films double-doped with niobium (Nb) and nitrogen (N) as cation and anion dopants (NiO:(Nb,N)) to be used as p-type layers in all oxide transparent solar cells. The films were grown by sputtering a composite Ni-Nb target on room-temperature substrates in plasma containing 50% Ar, 25% O2 and 25% N2 gases. The existence of Nb and N dopants in the NiO structure was confirmed by the Energy Dispersive X-Ray and X-Ray Photoelectron Spectroscopy techniques. The nominally undoped NiO film, which was deposited by sputtering a Ni target and used as the reference film, was oxygen-rich, single-phase cubic NiO, having a visible transmittance of less than 20%. Upon double doping with Nb and N the visible transmittance of NiO:(Nb,N) film increased to 60%, which was further improved after thermal treatment to around 85%. The respective values of the direct band gap in the undoped and double-doped films were 3.28 eV and 3.73 eV just after deposition and 3.67 eV and 3.76 eV after thermal treatment. The changes in the properties of the films such as structural disorder, direct and indirect energy band gaps, Urbach tail states and resistivity were correlated with the incorporation of Nb and N in their structure. The thermally treated NiO:(Nb,N) film was used to form a diode with a spin-coated two-layer, mesoporous on top of a compact, TiO2 film. The NiO:(Nb,N)/TiO2 heterojunction exhibited visible transparency of around 80%, showed rectifying characteristics and the diode’s parameters were deduced using the I-V method. The diode revealed photovoltaic behavior upon illumination with UV light exhibiting a short circuit current density of 0.2 mA/cm2 and open-circuit voltage of 500 mV. Improvements of the output characteristics of the NiO:(Nb,N)/TiO2 UV-photovoltaic by proper engineering of the individual layers and device processing procedures are addressed. Transparent NiO:(Nb,N) films can be potential candidates in all-oxide ultraviolet photovoltaics for tandem solar cells, smart windows and other optoelectronic devices.

The purpose of this study is to explore conceptual approaches in co-production studies and to examine current research trends of the study. The conceptual paper includes research articles related to co-production in public administration field. By thoroughly scrutinizing 32 research works of co-production, this study highlights major loopholes in the field of the study. The contributions of the study are: (1) identifying two common characteristics of co-production, (2) categorising three types of co-producing by end-users, and (3) finding that goals and success of co-production are more beneficial for service providers though its initial approach is citizen-centric approach. We suggest that future studies should be (1) to focus on reasons for co-production failures or success, (2) to discover further hindrances for co-production in service production, (3) to examine influencing factors on service providers as well as institutional impacts on co-production process, and (4) to include practical assessment in co-production study.

In this paper, the transmutation operator method is used to construct an exact solution to the initial boundary value problem for the equation of multidimensional free transverse vibration of a thin elastic plate with a singular Bessel operator acting on geometric variables. We emphasize that multidimensional Erdélyi–Kober operators of fractional order have the property of a transmutation operator, allowing one to transform more complex multidimensional partial differential equations with singular coefficients acting over all variables into simpler ones. If formulas for solutions are known for a simple equation, then we also obtain representations for solutions to the first complex partial differential equation with singular coefficients. In particular, it is successfully applied to the singular differential equations, especially with Bessel-type operators. Using this operator, the considered problem is reduced to a similar problem without the Bessel operator. Based on the solution to the auxiliary problem, an exact solution to the original problem is constructed and analyzed.

Gestational diabetes mellitus (GDM) triggers alterations in the maternal gut microbiome. Alongside metabolic shifts, microbial products may impact clinical factors and influence pregnancy outcomes. Here, we performed two separate analyses to integrate the maternal microbiome-metabolomic differences from a subset of the “Choosing Healthy Options in Carbohydrate Energy” (CHOICE) study. Women diagnosed with GDM were randomized to a diet higher in complex carbohydrates (CHOICE, n=18, 60% complex carbohydrate/25% fat/15% protein) or a conventional GDM diet (CONV, n=16, 40% carbohydrate/45% fat/15% protein). All meals were provided, diets were eucaloric, and fiber content was similar. CHOICE diet was associated with increases in TMAO, lysophosphotidylcholines, indoxyl sulfate, and several triglyceride (TG) species, while CONV diet was associated with higher plasma hippuric acid, aspartic acid, betaine, glutamic acid, indole propionic acid, and indoleacetic acid, suggesting that differences in lipid metabolism and tryptophan utilization pathways are linked to specific maternal diets. We also identified two latent metabolic groups not associated with either diet treatment: one metabolome associated with higher fasting glucose, and the other associated with higher fasting TGs after 7 weeks on either diet. Our study underscores the importance of utilizing dietary approaches to optimize maternal-fetal health outcomes in GDM treatment.

Co-integration and Causality was built to conduct studies on causality relation between carbon intensity and coal consumption leading to providing important basis for the transition to a low carbon economy. The EG two-step method was performed to study the relation between carbon intensity and coal consumption of China during 1990-2015 and the co-integration and Granger test was constructed to build up the co-integration and error correction models for analysis of the interaction between carbon intensity and coal consumption. The results showed that in long term there is a stable co-integration relation and a positive correlation between carbon intensity and coal consumption; whereas fluctuations exist in short term and there is a one-way Granger causality of carbon intensity with respect to coal consumption.

The formation and development of potato tissues and organs is a complex process regulated by a variety of genes and environmental factors. However, the regulatory mechanisms underlying the growth and development are still unclear. In this study, we used autotetraploid potato JC14 as experimental subject to analyze the transcriptome of root, stem and leaf at seedling, tuber formation and tuber expansion stages to explore the spatio-temporal expression pattern of genes and genetic development characteristics. The results identified thousands of differentially expressed genes and KEGG pathway enrichment analysis showed that these genes were mainly involved in defense response and carbohydrate metabolism pathways. A total of 12 co-expressed Gene modules were identified by Weighted Gene Co-expression Network Analysis (WGCNA), and 4 modules were screened out with the highest correlation with potato stem developmental traits. Core genes in the network were further investigated and functionally annotated by computing the connectivity of genes within the module. The results unveiled number of hub genes in stems at different developmental stages, including carbohydrate metabolism related genes, the defense response related genes, and transcription factors. These findings provide important leads for further understanding of the molecular regulation and genetic mechanisms of potato tissue development.

The objective of this study is to investigate the aquatic-toxic effects of glyoxal-containing cellulose ether with four different glyoxal concentrations (0, 1.4, 2.3 and 6.3%) in response to global chemical regulations, e.g., European Union Classification, Labeling and Packaging (EU CLP). Tests of the aquatic-toxic effects of glyoxal-containing cellulose ether on 11 freeze-dried microbial strains, Microcystis aeruginosa, Daphnia magna and zebrafish embryos were designed as an initial stage for toxicity screening, and were performed with the standardized toxicity test guidelines. Glyoxal-containing cellulose ether showed no significant toxic effects in the toxicity tests for the 11 freeze-dried microbial strains, Daphnia magna and zebrafish embryos. Alternatively, 6.3% glyoxal-containing cellulose ether led to more than a 60% reduction of Microcystis aeruginosa growth after 7 days of exposure. Approximately 10% developmental abnormalities (e.g., bent spine) in zebrafish embryos were also observed in the group exposed to 6.3% glyoxal-containing cellulose ether after 6 days of exposure. These results imply that <6.3% glyoxal-containing cellulose ether results in non-toxic effects on the acute toxicity of aquatic organisms. However, ≥6.3% glyoxal-containing cellulose ether may affect the health of aquatic organisms with long-term exposure. In order to better evaluate the eco-safety of cellulosic products contained in glyoxal, further studies regarding the toxic effects of glyoxal-containing cellulose ether with long-term exposure are required. The results from this study allow us to evaluate the auatic-toxic effects of glyoxal-containing cellulosic products, under EU chemical regulations, on the health of aquatic organisms.

This study presents the evaluation of an ion exchange resin-based installation designed for the recycling of engine coolants, specifically ethylene glycol (EG) and propylene glycol (PG). The recycling process aims to restore the coolant to meet the stringent ASTM standards for both new and recycled coolants. A combination of physical-chemical methods, gas chromatography-mass spectrometry (GC-MS), and inductively coupled plasma mass spectrometry (ICP-MS) was employed to analyze and validate the purity and performance of the recycled product. The experimental setup included performance tests to assess the efficacy of the recycled coolants in comparison to new coolant standards. The results demonstrate that the recycled EG coolants exhibit comparable quality to new coolants, with all critical parameters falling within the acceptable ASTM limits. This indicates that the ion exchange resin method is a viable and efficient solution for the recycling of engine coolants, offering an environmentally friendly alternative to the disposal of used coolants while ensuring compliance with industry standards.