There is currently an interest in “active” implantable biomedical devices that include mechanical stimulation as an integral part of their design. This paper reports the experimental use of a porous scaffold made of interconnected networks of slender ferromagnetic fibres that can be actuated in vivo by an external magnetic field applying strains to in-growing cells. Such scaffolds have been previously characterized in terms of their mechanical and cellular responses. In this study, it is shown that the shape changes induced in the scaffolds can be used to promote osteogenesis in vitro. In particular, immunofluorescence, gene and protein analyses reveal that the actuated networks exhibit higher mineralization and extracellular matrix production, and express higher levels of osteocalcin, alkaline phosphatase, collagen type 1a1, runt-related transcription factor 2 and bone morphogenetic protein 2 than the static controls at the 3-week time point. The results suggest that the cells filling the inter-fibre spaces are able to sense and react to the magneto-mechanically induced strains facilitating osteogenic differentiation and maturation. This work provides evidence in support of using this approach to stimulate bone ingrowth around a device implanted in bone and can pave the way for further applications in bone tissue engineering.

TThe locality condition of probabilities underpinning the derivation of Bell inequalities can be violated classically. The wave function collapse of an entangled state of single photons results in the factorization of quantum probabilities. It is possible to differentiate, locally, between ensemble probabilities of single detections with and without wave function collapse for the alleged quantum nonlocality. The theoretical concept of photonic quantum nonlocality cannot be implemented physically because of the quantum Rayleigh scattering of single photons. A distinction needs to be made between the correlation of individual, single measurements of pure states and the correlation of the measurement ensemble of the mixed states. The correlation operator of Pauli vector operators delivers the same probabilities of correlated detections of photons for both independent and multi-photon states as for ‘entangled’ states of photons. As single-photon sources are not needed, the design and implementation of quantum computing operations and other devices will be significantly streamlined. © 2024 The Author

The Axillary Seabream Pagellus acarne is of economic importance for recreational and commercial fisheries in the Atlantic and the Mediterranean, but a decline in total landings has been observed and raised some concern. Although biological information on the species has been provided for the Atlantic and Mediterranean, in the Portuguese continental slope data is restricted to the southern part of the country, which points to the relevance of the present work. A total of 520 individuals, between 7.8 cm and 39.9 cm TL, were monthly collected from September 2020 to August 2022 from commercial landings of fishing vessels operating off mainland Portugal, and used to study age and growth, the reproductive strategy and the annual sexual cycle. Age varied between 0 years (7.8 cm TL) and 15 years (34.2 cm TL) and the von Bertalanffy growth parameters were estimated from whole-otolith readings (L∞ = 35.2 cm, k = 0.133 yr–1 and t 0 = –2.78 yr). Pagellus acarne proved to be a protandric hermaphrodite species with males prevailing until 28.0 cm TL and females between 28.0 cm and 34.0 cm TL with a brief sex change phase. Females presented asynchronous oocyte development with a batch spawning pattern. The spawning season occurred from April to October with a spawning peak between June and August aas shown by histological analysis of the gonads and high levels of GSI during the period.

Quantum Rayleigh scattering of single photons rules out coincident detections of the originally entangled, paired photons Correlations between independent states of qubits can easily outperform those calculated with entangled photons. The quantum joint probability for a Bell state can be factorized enabling a local detection of the alleged quantum nonlocality, if it existed.

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

The locality condition of probabilities underpinning the derivation of Bell inequalities can be violated classically. The wave function collapse of an entangled state of single photons results in the factorization of quantum probabilities. It is possible to differentiate, locally, between ensemble probabilities of single detections with and without wave function collapse for the alleged quantum nonlocality. The theoretical concept of photonic quantum nonlocality cannot be implemented physically because of the quantum Rayleigh scattering of single photons. A distinction needs to be made between the correlation of individual, single measurements of pure states and the correlation of the measurement ensemble of the mixed states. The correlation operator of Pauli vector operators delivers the same probabilities of correlated detections of photons for both independent and multi-photon states as for ‘entangled’ states of photons. As single-photon sources are not needed, the design and implementation of quantum computing operations and other devices will be significantly streamlined.

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.

The AUC-ROC is a widespread measure to assess the performance of a binary classification model across various discrimination thresholds. Yet, it is not apt for unbalanced class problems and is misleading for early retrieval. For instance, link prediction in complex networks is an unbalanced early retrieval problem, whose goal is to offer a ranking of the nonobserved network links, prioritizing a small cohort of positive links (for which we are sure the labels are reliable) on top of a list largely populated by unlabeled links (for which the labels are uncertain). Differently from binary classification, here the evaluation focuses on how the predictor prioritizes the positive class, because the negative class does not exist and is replaced by an unlabeled class. For decades, scholars have been investigating the necessity to correct and to generalize the AUC-ROC to work for both unbalanced classification and early retrieval evaluation, this is requested in many applied domains of computational science. Here we propose the area under the magnified ROC (AUC-mROC), a new measure that adjusts the standard AUC-ROC to work for unbalanced early retrieval problems such as link prediction. Finally, we introduce the area under the generalized ROC (AUC-gROC), which unifies both AUC-mROC and AUC-ROC to provide a universal and reliable evaluation measure that can work for both classification and early retrieval problems, regardless of class proportion.This discovery represents a critical achievement, culminating in success after decades of contentious research. Its solution promises to significantly impact various domains within applied computational science.

An approximation-free and fully quantum optic formalism for parametric processes is presented. Phase-dependent gain coefficients and related phase-pulling effects are identified for quantum Rayleigh emission and the electro-optic conversion of photons providing parametric amplification in small scale integration of photonic devices. These mechanisms can be manipulated to deliver, simultaneously, sub-Poissonian distributions of photons as well as phase-dependent amplification in the same optical quadrature of a signal field.

Cyclin-dependent kinase 1 (Cdk1) is a master regulator of the G2-M transition between DNA replication and cell division. This study investigates the regulation of cardiomyocyte (CM) proliferation during the early neonatal period and following ischemic injury in adult mice. We analyzed cell cycle dynamics with the assessment of DNA synthesis, and cytokinesis in murine hearts during the first 15 days after birth. A distinct proliferative block was observed at 1 day, followed by a second wave of DNA synthesis at 4 days, leading to CM binucleation (CMBN) by day 5. Genome-wide mRNA profiling revealed differential expression of cell cycle regulatory genes during this period, with a downregulation of factors involved in cell division and mitosis. The loss of Cdk1 impaired CMBN but extended the neonatal CM proliferation window until day 10 post-birth. In adult hearts, cardiac-specific ablation of Cdk1 triggered CM proliferation post-myocardial infarction (MI) in specific zones, driven by the activation of EGFR1 signaling and suppression of the anti-proliferative p38 and p53 signalling. This was accompanied by restoration of fractional shortening, mitochondrial function, and decreased reactive oxygen species. Additionally, cardiac hypertrophy was mitigated, and survival rates post-MI were increased in Cdk1 knockout mice. These findings reveal a novel role of Cdk1 in regulating cell cycle exit and re-entry in differentiated CM and offer insights into potential strategies for cardiac repair.

Transition metal dichalcogenides (TMDs) monolayers exhibit unique valleytronics properties due to the dependency of the coupled valley and spin state at the hexagonal corner of the first Brillouin zone. Precisely controlling valley spin-polarization via manipulating the electron population enables its application in valley-based memory or quantum technologies. This study uncovered the uncompensated spins of the antiferromagnetic oxide (NiO) serving as the ferromagnetic (FM) order to induce valley spin-polarization in molybdenum disulfide (MoS₂) monolayers via the magnetic proximity effect (MPE). A spin-resolved photoluminescence spectroscopy (SR-PL) was employed to observe MoS₂, where the spin-polarized trions appear to be responsible for the MPE, leading to a valley magnetism. Results indicate that local FM order from the uncompensated surface of NiO could successfully induce significant valley spin-polarization in MoS₂ with the depolarization temperature approximately at 100 K, which is relatively high compared to related literature. This study reveals new perspectives and potential of AFM materials in the field of exchange-coupled van der Waals heterostructures.

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.

The oral microbiome, studied by sampling the saliva or oral rinse, has been long thought to have diagnostic capacity for head and neck cancers (HNC). However, previous reports on the HNC oral microbiome provide inconsistent results. The aim of this study is to consolidate these datasets and determine the oral microbial composition between HNC patients to healthy and premalignant individuals. We analyzed 16 published head and neck cancer (HNC) short-read 16S rRNA sequencing datasets, specifically targeting the V3V4, V4 and V4V5 regions. These datasets included saliva and oral rinse samples from donors with HNC, as well as from healthy and premalignant donors. Differences in diversities and microbial abundance were determined. HNC saliva displayed lower alpha diversity than healthy donors. In contrast, the opposite trend was observed for oral rinse samples. Beta diversity were largely similar across different patient types. Similar oral phyla were detected for all samples, but proportions were largely dependent on sample type (i.e. saliva or oral rinse) and primer set utilised for 16S rRNA sequencing. Neisseria, Leptotrichia and Megasphaera were elevated in healthy saliva, while Mycoplasma was elevated in HNC saliva. Oral rinse and saliva displayed similar enrichment for Fusobacterium, while Veillonella, Alloprevotella, and Campylobacter had conflicting results. The sparse partial least squares discriminant analysis model performed effectively in discriminating HNC from healthy or premalignant patients using V3V4 saliva (AUC = 0.888), and V3V4 oral rinse (AUC = 0.928), while poor discriminative capacity was observed for V4 saliva (AUC = 0.688). In conclusion, our meta-analysis highlighted the limitations of 16S rRNA sequencing, particularly due to variations across study batches, primer sets (i.e. V3V4, V4), and sample types. Hence, caution should be exercised when interpreting 16S rRNA sequencing results across studies, especially when different primer sets and sample types are used.

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.

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.

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.

Many studies utilize sensors to measure foot plantar pressure and analyze gait patterns, often focusing on assessing weight-bearing symmetry or asymmetry compared to control groups. However, a critical question arises: does the selection of the control group significantly impact study outcomes? Does chronic low back pain affect foot plantar pressure measurements? This experimental study involved 23 participants, including healthy individuals and those without foot disorders but with chronic low back pain. Dynamic foot pressure measurements were conducted using the Ortopiezometr, and data were analyzed using the "Steps" program. Participants were divided into two groups: Group 1 comprised completely healthy individuals, while Group 2 consisted of individuals without foot problems but with moderate chronic low back pain. The findings highlight a prevalence of energy release in the forefoot sensors compared to the hindfoot for both the right and left legs in most cases. Even among healthy volunteers, achieving perfect symmetry in the maximum values on the sensors for the right and left feet was a challenge. Results showed no statistically significant differences in energy released on specific sensors or duration of gait phases between the groups. Essentially, this suggests that foot pressure patterns are comparable between healthy individuals and those with low back pain. The use of sensor-equipped inserts for pedobarographic examinations indicates that, in cases of degenerative changes in the lumbar and sacral spine without accompanying acute pain, gait patterns resemble those of the healthy population.

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.

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

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.

(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.

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.

In order to increase the material throughput of aligned discontinuous fibre composites using technologies such as HiPerDiF, stability of the fibres in an aqueous solution needs to be achieved. Subsequently, a range of surfactants, typically employed to disperse carbon-based materials, have been assessed to determine the most appropriate for use in this regard. The optimum stability of the discontinuous fibres was observed when using the anionic surfactant, sodium dodecylbenzene sulfonate, which was superior to a range of other non-ionic and anionic surfactants and single-fibre fragmentation demonstrated that the employment of sodium dodecylbenzene sulfonate did not effect on the interfacial adhesion between fibres. The use of rheometry was used to complement the study to understand the potential mechanisms of the improved stability of discontinuous fibres in aqueous suspension and it led to the understanding that the increased viscosity was a significant factor. For the shear rates employed, fibre deformation was neither expected nor observed.

The electric network frequency (ENF), often referred to as the industrial heartbeat, plays a crucial role in the power system. In recent years, it has found applications in multimedia evidence identification for court proceedings and audio-visual temporal source identification. This paper introduces a ENF region classification model named UniTS-SinSpec within the UniTS framework. The model integrates the si-nusoidal activation function and spectral attention mechanism while also redesigning the model frame-work. Training is conducted using a public dataset on the open science framework platform (OSF), with final experimental results demonstrating that after parameter optimization, the UniTS-SinSpec model achieves an average validation accuracy of 97.47%, surpassing current state-of-the-art and baseline models. Accurate classification can significantly aid in ENF temporal source identification. Future re-search will focus on expanding dataset coverage and diversity to verify the model's generality and ro-bustness across different regions, time spans, and data sources. Additionally, it aims to explore extensive application potential of ENF region classification in preventing crimes such as telecommunications fraud, terrorism, and child pornography.

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.

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.

Polyolefin fibre reinforced concrete (PFRC) has become an attractive alternative to steel for the reinforcement of concrete elements mainly due to its chemical stability and the residual strengths that can be reached with lower weights. The use of polyolefin fibres can meet the requirements in the standards, although the main constitutive relations are based on the experience with steel fibres. Therefore, the structural contributions of the fibres should be assessed by inverse analysis. In this study, the fibre dosage has been fixed at 6kg/m³ and both self-compacting concrete and conventional concrete have been used to compare the influence of the positioning of the fibres. An idealized homogeneous distribution of the fibres with such fibres crossing from side to side of the specimen has been added to self-compacting concrete. The experimental results of three-point bending tests on notched specimens have been reproduced by using the cohesive crack approach. Hence, the constitutive relations were found. The significance of this research relies on the verification of the formulations found to build the constitutive relations. Moreover, with these results it is possible to establish the higher threshold of the performance of PFRC and the difficulties of limiting the first unloading branch typical of fracture tests of PFRC.

As a pioneering transdisciplinary field, innovationology transcends the traditional boundaries of academic disciplines and development practices to address persistent global challenges. This article provides a comprehensive exploration of innovationology, delving deep into its philosophical foundations, ontological commitments, and ethical imperatives. By unpacking the core tenets of this innovative approach, the paper positions it as a robust and holistic science capable of catalyzing transformative innovation and sustainable development worldwide. Innovationology represents a paradigm shift that moves beyond conventional innovation and development theories, embracing a systems-oriented, community-centric, and decolonial praxis. Drawing on a synthesis of theoretical perspectives and empirical insights, the article examines innovationology's ontological commitment to marginalized communities as active agents of change, its epistemological grounding in pluralistic knowledge systems, and its ethical imperatives of social justice, environmental sustainability, and inclusive innovation. This in-depth analysis strengthens the conceptual and theoretical foundations of innovationology, providing critical guidance for its practical implementation and continued refinement as a cutting-edge transdisciplinary science for global transformation.

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

A 3D graphene foam made of interconnected multilayer graphene flakes was produced on optical fibres (OF) by laser-induced transformation of a polyimide (PI) film coated on the OF cladding. This material, known as la-ser-induced graphene (LIG), was explored in the electrochemical detection and quantification of dopamine (DA) at physiologically relevant concentrations in the presence of the most relevant interfering molecules in biological fluids, ascorbic acid (AA) and uric acid (UA). The measured limit of detection is 100 nM, the linear range is 0.1 to 5 μM and a maximum sensitivity of 5.0 µA µM−1 cm−2 was obtained for LIG decorated with Pt nanoparticles (NPs). Moreover, immunity to AA and UA interference and to fouling was attained by decorating the LIG elec-trode with Pt NPs and coating with Nafion. These figures of merit underlines the potential of these sensors for the quantification of physiologically relevant concentrations of DA in biological fluids, paving the way for the development of hybrid electrochemical/optical sensing actuating platforms in a lab-on-fibre configuration, with relevant applications in biomedical engineering. The advantages of this hybrid arrangement include the possi-bility of in-situ counterproofing, extended measuring ranges, photoelectrochemical detection and the probing of inaccessible places. This elegant approach can also provide a simple and cost-effective way to fabricate biomed-ical devices with extended functionality, such as medical optical probes with added electrochemical capabilities, optogenetics combined with local electrochemical detection, among others.

Network Function Virtualization (NFV) allows for the dynamic provisioning of Virtualized Network Functions, adapting services to the complex and real-time network environment for enhanced network performance. Despite the benefits of NFV, virtual network functions (VNFs) migration and the energy consumption pose significant challenges due to the dynamic nature of the physical network, especially in the edge-cloud continuum network. Currently, people tackle this challenge by predicting the network traffic and then proactively migrating the VNFs using the predicted values. There are two challenges here. First, when SFCs belong to different network service providers, the historical network traffic data is held by the service providers, and they are reluctant to share the historical data due to privacy concerns. Without the historical network traffic data of each SFC, the resource provider that owns the underlying network cannot effectively predict the network traffic; in addition, without the feedback of the migration results of the underlying physical network, the network traffic prediction will be difficult to match the needs of the physical network. In this manuscript, we solve the problem of network traffic prediction in the NFV-based edge-cloud continuum network for multiple network service providers. We aim to improve migration performance and reduce energy consumption while ensuring resource and latency requirements. To protect the privacy of each network service provider, we apply an FL algorithm. After that, to improve the migration performance, we tailor the loss function. In the loss function, factors such as the number of migrations, the number of migration failures, and the energy consumption of the NFV-based edge-cloud continuum network are added. Additionally, to obtain the results of the SFC migration, we develop a mathematical model for the problem of multiple network service providers in the NFV-based network. The effectiveness of our algorithm is evaluated through extensive simulations, and the results show a significant reduction in the number of migrated nodes and energy consumption while improving the SFC acceptance ratio compared to state-of-the-art schemes which only use the difference between predicted and actual flows to define the loss function.

In the framework of mean field approximation, we consider a spin system consisting of two interacting sub-ensembles: spins interactions with in each sub-ensemble are ferromagnetic, while the inter-ensemble interactions are antiferromagnetic. We define the effective number of the nearest neighbors, and show that if the two sub-ensembles have the same effective number of the nearest neighbors, the classical form of critical exponents (, , , ) gives way to the non-classical form (, , , ), and the scaling function changes simultaneously. We demonstrate that this system allows for two second-order phase transitions and two first-order phase transitions. We observe that an external magnetic field does not destroy the phase transitions, but only shifts their critical points, allowing for control of the system’s parameters. We discuss the regime when the magnetization as a function of the magnetic field develops a low-magnetization plateau, and show that the height of this plateau abruptly rises to the value of one when the magnetic field reaches a critical value. Our analytical results are supported by a Monte Carlo simulation of a three-dimensional layered model.

In the history of high-performance polymer composite parts with continuous in-plane fibre reinforcement, the comparatively moderate structural-mechanical properties in their out-of- plane-z-direction and the limited load carrying capability between individual reinforcement layers within the laminated structures have always been one of the greatest challenges. Plenty of research work has been carried out to improve load transfer capability as well as fracture toughness for crack opening events between the reinforcement layers by means of out-of-plane fibre reinforcement implementation, for example by sewing or z-pinning technologies. However, the results have not become established across a wide range of applications so far, because the technologies used have led to considerable losses of in-plane strength properties. In this paper first results are presented for a new method of a low fraction z-fibre reinforcement by means of very thin diameter laser drilled holes filled with short carbon fibres, demonstrating potential to improve Mode II fracture toughness.

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.

The coexistence of ferroelectric and magnetic order parameters in multiferroic materials opens up a host of new collective properties. In particular, the magnetoelectric (ME) effect namely the induction of electric polarization by a magnetic field or magnetization by an electric field has been widely studied in multiferroics. PVDF is a ferroelectric polymer that has attracted considerable research interest for sensing applications. Compared to traditional ceramic composites, the polymer-ceramic nanocomposites offer inherent advantages, including easy processing, mechanical flexibility and the ability to be moulded into complicated configurations for advanced devices with reduced volume and weight. Additionally, polymer composites generally exhibit superior ME coefficients, attributable to the improved displacement transfer capability of the flexible polymer matrix. In this report, free-standing, flexible and lightweight polymer–ceramic nanocomposite thin films with a fixed weight percentage of ferromagnetic CoFe2O4 nanoparticles have been fabricated using a solution casting technique. The structural, microstructural, and electrical properties of the composite have been characterized by standard experimental techniques. The structural and chemical analyses prove a homogeneous dispersion of the fillers in the microstructure of the composite. The electrical response investigated by impedance spectroscopy reveals the contributions of grains and grain boundaries to the whole impedance of the composites. The ac conductivity as a function of frequency obeys Jonscher’s power law. The improved magnetoelectric properties suggest promising applications in multifunctional devices, including field sensor applications.

Sinkhole formation caused by leaking pipes in karst soluble rocks is a significant concern, leading to infrastructure damage and safety risks. In this paper, an experiment was conducted to investigate sinkhole formation in dense sand induced by a leaking pipe. Fibre Bragg gratings (FBGs) sensors were used to record the strain. A balloon was gradually deflated within a bed of wet silica sand to create an underground cavity. Eighteen FBG sensors, with a wavelength range between 1550 nm and 1560 nm, were embedded horizontally and vertically in the physical model at different levels to monitor deformation at various locations. A leaking pipe was installed to induce the collapse of the formed arch above the cavity. The strain measurements suggest four phases in the sinkhole formation process: (1) cavity formation, (2) progressive weathering and erosion, (3) catastrophic collapse, and (4) subsequent equilibrium conditions. The results showed differences in strain signatures and distributions between horizontal and vertical measurements. During the critical phase of sinkhole collapse, horizontal measurements primarily showed tension, while vertical measurements indicated compression. This investigation demonstrates the effectiveness of FBGs as advanced monitoring tools for sinkhole precursor identification. The study also suggests using FBGs in geotechnical monitoring applications to improve understanding and mitigation of sinkholes and related geohazards.

Structural deformations are one of the most significant factor that affects machine tool (MT) positioning accuracy. These induced errors are complex to be represented by a model, nevertheless they need to be evaluated and predicted in order to increase the machining performance. This paper presents a novel approach to calibrate a machine tool in real-time, analyzing the thermo-mechanical errors through Fibre Bragg Grating (FBG) sensors embedded in the MT frame. The proposed configuration consists of an adaptronic structure of passive materials, Carbon Fibre Reinforced Polymers (CFRP), equipped by FBG sensors that are able to measure in real-time the deformed conditions of the frame. By using a proper thermo-mechanical kinematic model, the displacement of the end effector may be predicted and corrected when it is subjected to external undesired factors. By starting from a set of FE simulations to develop a model able to describe the MT structure stresses, a prototype has been fabricated and tested. The scope was to compare the numerical model with the experimental tests using FBG sensors. The experimental campaign has been performed varying the structure temperature over time and measuring the tool tip point (TTP) positions. The obtained results showed a substantial matching between the real and the predicted position of TTP confirming the effectiveness of the proposed calibration system.

Climate adaptation is a major challenge. Chasing the sufficient amount of hay is getting in higher priority. Distant mass hay producers give favourable offers despite long distances. Quality is also gaining position and indicators like RFQ (Relative Forage Quality) is highlighting the marketing language. Hay market as we knew no longer exists in Hungary. Most farmers produce their own hay and do not spend extra cents to buy bales. Climate change however, force them to adapt and store more bales for the future. Horse owners and dairy farmers are the main driver to convince hay producers to provide high quality forage. We gathered Hungarian regional hay-price information and evaluated the trends in this sector. The demand-driven hay-price is in contradiction with premium quality timothy grass hay.

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.

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.

In this work, we demonstrate that cutting diamond crystals with a laser (532 nm wavelength, 0.5 mJ energy, 200 ns pulse duration at 15 kHz) produces a ≲20nm thick surface layer with magnetic order at room temperature. We have measured the magnetic moment with a SQUID magnetometer of six natural and six CVD diamond crystals of different size, nitrogen content and surface orientations. A robust ferromagnetic response at 300 K is observed only for crystals that were cut with the laser along the (100) surface orientation. The magnetic signals are much weaker for the (110) and negligible for the (111) orientations. We attribute the magnetic order to the disordered graphite layer produced by the laser at the diamond surface. The ferromagnetic signal vanished after chemical etching or after moderate temperature annealing. The obtained results indicate that laser treatment of diamond may pave the way to create ferromagnetic spots at its surface.

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.

In an era of relentless technological change, accelerating the pace of innovation has become a critical imperative for organizations seeking to maintain a competitive edge and drive meaningful societal progress. Yet the complex, multifaceted innovation process - from ideation to commercialization - is often plagued by bottlenecks and challenges that can significantly delay the time-to-market for groundbreaking new products, services, and business models. This pioneering article represents an outstanding contribution to the burgeoning field of innovationology, offering a comprehensive, evidence-based playbook for shortening the innovation lifecycle. Drawing on an extensive review of over 180 cutting-edge academic sources, the paper maps the key stages of the innovation process - idea generation, concept development, prototyping, testing, and launch - and illuminates cutting-edge strategies that innovators can deploy to overcome common barriers and drive transformative innovations to market more quickly. From advanced idea generation techniques like design thinking and AI-powered creativity to innovative approaches to rapid prototyping, virtual experimentation, and data-driven commercialization, this seminal work equips scholars, practitioners, and policymakers with a transformative, globally-relevant toolkit for accelerating the innovation lifecycle. By synthesizing insights from a diverse array of industries and disciplines, the article establishes a new benchmark for innovationology research and empowers organizations to cultivate the conditions needed to translate visionary ideas into game-changing realities with unprecedented speed and efficiency. As the world grapples with ever-more complex, interconnected challenges, the ability to rapidly develop and scale breakthrough innovations has never been more crucial. This paradigm-shifting contribution to the field of innovationology represents a vital resource for driving the bold, disruptive changes needed to shape a more sustainable, equitable future.

In an era of rapid technological advancements, complex global challenges, and intense market competition, the ability to generate and scale innovative solutions has become a critical imperative for organizations, policymakers, and societies worldwide. However, the existing academic landscape has lacked a cohesive, multidisciplinary framework for comprehensively understanding the multifaceted nature of innovation. Innovationology, a newly established scientific discipline, aims to address this gap by providing a unifying, transdisciplinary approach to the study and practice of transformative innovation. This comprehensive article introduces Innovationology as a cutting-edge science that integrates insights from diverse fields, including management, psychology, sociology, economics, and technology studies. Innovationology posits that innovation is a multilayered, context-dependent phenomenon, shaped by the intricate interplay of individual, team, organizational, and ecosystem-level factors. By synthesizing the latest theoretical advancements and empirical evidence, this article presents a holistic model of Innovationology that illuminates the key determinants of radical, game-changing innovations capable of disrupting existing industries and creating new market spaces. The article delves deep into the individual cognitive, behavioral, and motivational drivers of innovativeness, the team dynamics and organizational structures that foster collaborative innovation, and the ecosystem-level characteristics that catalyze the emergence and scaling of transformative innovations. Importantly, the article explores the crucial role of contextual factors, such as socio-cultural norms, institutional support, and resource availability, in shaping innovation outcomes. This article also establishes the epistemological foundations of Innovationology, grounding it in a transdisciplinary, holistic, and pragmatic approach to knowledge generation. Innovationology embraces a pluralistic epistemology that acknowledges the complexity and context-dependence of innovation, drawing on diverse methodological approaches to capture the multifaceted nature of this phenomenon. Furthermore, the article outlines the object of Innovationology, which is to provide a comprehensive, evidence-based understanding of the drivers, processes, and outcomes of transformative innovation. Innovationology seeks to elucidate the multilevel determinants of innovation, the dynamic interplay between various factors, and the contextual influences that shape innovation trajectories. By establishing a unifying, transdisciplinary framework, Innovationology aims to bridge the gap between innovation theory and practice, empowering a wide range of stakeholders to unlock the transformative potential of innovation. Importantly, this article outlines the practical applications of Innovationology, providing comprehensive strategies and evidence-based interventions for cultivating innovative mindsets, designing innovation-conducive organizational systems, and navigating the challenges of innovative ecosystems. The implications of Innovationology for entrepreneurs, corporate leaders, policymakers, and innovation scholars are discussed in detail. By establishing Innovationology as a distinct, authoritative scientific discipline, this article sets the foundation for a more holistic, context-sensitive understanding of innovation and its multifaceted drivers. The insights generated by this new science can empower global organizations, institutions, and policymakers to address the complex, interconnected challenges of the 21st century through the strategic deployment of transformative innovations.

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.

As both governments and customers push for sustainable and recyclable textile products, textile companies will have to change their linear business model to a circular one. The aim of the present work is to help these companies take action by providing an update of the state-of-the-art of textile recycling technologies. Extensive desktop research was performed in order to prepare an overview of existing textile recycling technologies, their current state and prognosis, including also facilitating technologies for sorting and disintegration of textile products as well as virtual platforms that connect stakeholders and inform consumers.

Decoherence with recurrences appear in the dynamics of the one-body density matrix of an $F = 1$ spinor Bose-Einstein condensate, initially prepared in coherent states, in the presence of an external uniform magnetic field and within the single mode approximation. The phenomenon emerges as a many-body effect of the interplay of the quadratic Zeeman effect, that breaks the rotational symmetry, and the spin-spin interactions. By performing full quantum diagonalizations very accurate time evolution of large condensates are analyzed, leading to heuristic analytic expressions for the time dependence of the density matrix, in the weak and strong interacting regimes. We are able to find accurate analytical expressions for both the decoherence and the recurrence times, in terms of the number of atoms and strength parameters, that show remarkable differences depending on the strength of the spin-spin interactions. We discuss the nature of these limits in the light of the thermodynamic limit.

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.

In recent years, solid state terahertz (THz) modulators have obtained rapid progress with the widespread use of two-dimensional (2D) materials in the field of THz; however, challenges remain in preparing flexible THz modulators. In this study, flexible ferromagnetic nematic materials were prepared by doping thermotropic nematic liquid crystals 5CB into magnetic fluids, and the influence of hydrogen bonding in water was reduced by a self-made cyclic olefin copolymer (COC) microfluidic chip. THz modulation characteristics of magnetic fluid and ferromagnetic nematic liquid crystal (FNLC) under the induction of external magnetic field were compared using a THz time domain spectroscopy system. Under the action of a 91 mT magnetic field, the magnetic fluid has a maximum modulation depth (MD) of 54%. Under the same magnetic field, the maximum MD of the ferromagnetic nematic liquid crystal materials increase to 78% because of the rearrangement of Fe3O4 nanoparticles induced by the topological defect of the liquid crystal. We demonstrate that the magneto-optical effect is significantly enhanced in the ferromagnetic nematic liquid crystal hybrid system. This strategy of doping thermotropic nematic liquid crystals to enhance the magneto-optical effect has great potential for THz filtering, modulation, and sensing applications.

The emerging wireless energy transfer technology enables sensor nodes to maintain perpetual operation. However, maximizing the network performance while preserving short charging delay is a great challenge. In this work, a Wireless Mobile Charger (WMC) and a Directional Charger (DC) are deployed to transmit wireless energy to the sensor node to improve the network's throughput. To the best of our knowledge, this is the first work to optimize the data sensing rate and charging delay by joint scheduling of MC and DC. We prove we can transmit maximum energy to each sensor node to obtain our optimization objective. In our proposed work, a DC selects a total horizon of equipped with DCs, which selects the horizon of each specific area with based on its antenna orientation. DC's orientation is scheduled for each time slot. Furthermore, multiple MCs are used to transmit energy for sensor nodes which cannot be covered by DCs. We divided the Rechargeable Wireless Sensor Network into several zones via the Voronoi diagram. We deployed a static DC and one MC charging location in each zone to provide wireless charging service jointly. We obtain the optimal charging location of MCs in each zone by solving Mix Integral Programming for energy transmission. The optimization objective of our proposed research is to sense maximum data from each sensor node with the help of maximum energy. The lifetime of each sensor network can increase, and the end delay can be maximized with joint energy transmission. Extensive simulation results demonstrate that our RWSNs are designed significantly to improve network lifetime over the baseline method.

Recent developments in the field of room temperature ferromagnetic metal oxide semiconductors (RTFMOS) have shown promising potential for improved photocatalytic performance. This review focuses on the combined study of photocatalytic and ferromagnetic properties at room temperature, with a particular emphasis on metal oxides, such as TiO2, which have emerged as a key area of interest in the domains of magnetism and environmental remediation. Despite extensive research over the years, the precise mechanism behind the interplay of ferromagnetism and photocatalysis in these materials remains incompletely understood. Several critical factors contributing to magnetism have been hinted at, including oxygen vacancies and various metal doping. Numerous reports have demonstrated that these factors play a primary role in room temperature ferromagnetism and photocatalysis in wide-band-gap metal oxides. However, establishing a direct correlation between magnetism, oxygen vacancies, dopant concentration, and photocatalysis has proven challenging. This review aims to provide a comprehensive overview of the recent progress in understanding the magnetism and photocatalytic behaviour of metal oxides. By examining the latest findings, this study sheds light on the potential of RTFMOS as effective photocatalysts, thereby contributing to advancements in environmental remediation and related applications.

Rare-earth tetraborides RB4 are of great interest due to the occurrence of geometric magnetic frustration and corresponding unusual magnetic properties. While the Gd3+ spins in GdB4 align along the ab plane, Er3+ spins in the isomorphic ErB4 are confined to the c–axis. The magnetization in the latter exhibits a plateau at the midpoint of the saturation magnetization. Therefore, solid solutions of (Gd, Er)B4 provide an excellent playground for exploring the intricate magnetic behavior in these compounds. Single crystals of Gd(1-x)Erx B4 (x = 0, 0.2, and 0.4) were grown in aluminum flux. X-ray diffraction scans revealed single phase materials, and a drop in the unit cell volume with increasing Er content, suggesting the partial substitution of Er at the Gd sites. Heat capacity measurements indicated a systematic decrease of the Néel temperature (TN) with increasing Er content. The effective magnetic moment determined from the magnetization measurement agreed with the calculated free-ion values for Gd3+ and Er3+, providing further evidence for the successful substitution of Er for Gd. The partial substitution resulted in an anomalous ferromagnetic phase below TN, exhibiting significant anisotropy, predominantly along the c-axis. This intriguing behavior merits further studies of the magnetism in the Gd(1-x)ErxB4 borides.

The Heisenberg {\it ab initio} theory of magnetization is developed to apply for multilayer nanoparticles. The theory is based on distribution and partition functions modification with account the difference between exchange integral and closest neighbour numbers, that change the system of resulting transcendental equation for magnetization and its reversal to form either a paramagnetic type curve or hysteresis loops patterns. The equations are obtained within the Heisenberg partition function construction by Heitler diagonalization of energy matrix via irreducible representations of permutation symmetry group. A combination with the Gauss distribution gives the explicit expression for the partition function in the asymptotic limit] at large spin range in terms of transcendent function. The exchange integral, as a parameter of the equation of state (material equation) is evaluated from Curie temperature value by means of a formula derived within the presented theory. Methods of data processing from the simultaneous solution of the material equation system are proposed. The multi-valued function of hysteresis loop is found by combination of graphical approach and special procedure for elimination of mistaken peaks and prolapses of the patterns. The theory and computation methods are applied to spherical particles with separate surface layers consideration. The contribution of the surface layers, that are specified by number of closest neighbors and exchange integrals into overall magnetization, is studied for two-layer and three-layer models, that are discussed and compared graphically.

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.

Tungsten fibre-reinforced tungsten composites (Wf/W) have been in development to overcome the inherent brittleness of tungsten as one of the most promising candidate for the first wall and divertor armour material in a future fusion power plant. As the development of Wf/W continues, the fracture toughness of the composite is one of the main design drivers. In this contribution the efforts on size upscaling of Wf/W based on Chemical Vapour Deposition (CVD) is shown together with fracture mechanical tests of two different size samples of Wf/W produced by CVD. Three-point bending tests according to ASTM E399 for brittle materials were used to get a first estimation of the toughness. A provisional fracture toughness value of up to 346MPam1/2 was calculated for the as-fabricated material. As the material does not show a brittle fracture in the as-fabricated state, the J-Integral approach based on the ASTM E1820 was additionally applied for this state. A maximum value of the J-integral of 41kJ/m2 (134,8MPam1/2) was determined for the largest samples. Post mortem investigations were employed to detail the active mechanisms and crack propagation.

The unique porous crystal structure of zeolite offers various important utilizations, it is one of the considerations in selecting zeolite at this study as component of composite for restraining noise. It so happens, previous experiments show that banana stem has porous structure, fibrous, high flexibility and can be applied as material for many various products including as component of acoustic material. The combination of both is alleged that it has capability in absorbing noise. This paper presents an investigation on the composite that it was synthesized of Activated Zeolite and Banana Stem Fibre in various weight for determining its sound absorption coefficient alpha (a). Activating natural zeolite was conducted by using 6M HCl in order for enlarging zeolite pores. The sound absorption coefficient was measured in the frequency range between 125 Hz up to 6000 Hz. The results show that the different weight of banana stem fibre as component of the synthesized composite affects the value of alpha and shifts the frequency area.

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.

Nanomagnetism and spintronics are currently active areas of research, with one of the main goals being the creation of low-energy-consuming magnetic memories based on nanomagnet switching. These types of devices could also be implemented in neuromorphic computing by crafting artificial neurons (ANs) that emulate the characteristics of biological neurons, through the implementation of neuron models such as the widely used leaky integrate-and-fire (LIF) with a refractory period. In this study, we have carried out numerical simulations of a 120 nm diameter, 250 nm length ferromagnetic nanowire (NW) with the aim of exploring the design of an artificial neuron based on the creation and destruction of a Bloch-point domain wall. To replicate signal integration, we applied pulsed trains of spin-currents to the opposite faces of the ferromagnetic NW. These pulsed currents (previously studied only in the continuous form) are responsible for inducing transitions between the stable single vortex (SV) state to the metastable Bloch point domain wall (BP-DW) state. To ensure the system exhibits the leak and refractory properties, the NW was placed in an homogeneous magnetic field of the order of mT in the axial direction. The suggested configuration fulfills the requirements and characteristics of a biological neuron, potentially leading to the future creation of Artificial Neural Networks (ANNs) based on reversible changes in the topology of magnetic NWs.

The central nervous system (CNS) is the final frontier in drug delivery because of the blood-brain barrier (BBB), which poses significant barriers to the access of most drugs to their targets. Kynurenic acid (KYNA), a tryptophan (Trp) metabolite, plays an important role in behavioral functions and abnormal KYNA levels have been observed in neuropsychiatric conditions. The current challenge lies in delivering KYNA to the CNS owing to its polar side chain. Recently, C-3 side chain modified KYNA analogs have been shown to cross the BBB; however, it is unclear whether they retain the biological functions of the parent molecule. This study examined the impact of KYNA analogs, specifically SZR-72, SZR-104, and the newly developed SZRG-21, on behavior. The analogs were administered intracerebroventricularly (i.c.v.), and their effects on the motor domain were compared with those of KYNA. Specifically, open field (OF) and rotarod (RR) tests were employed to assess motor activity and skills. SZR-104 increased horizontal exploratory activity in the OF test at a dose of 0.04 μmol/4 μL, while SZR-72 decreased vertical activity at doses of 0.04 and 0.1 μmol/4 μL. In the RR test, however, neither KYNA nor its analogs showed any significant differences in motor skills at either dose. Side chain modification affects affective motor performance and exploratory behavior, as the results show for the first time. In this study, we showed that KYNA analogs alter emotional components such as motor-associated curiosity and emotions. Consequently, drug design necessitates the development of precise strategies to traverse the BBB while paying close attention to modifications in their effects on behavior.

The production, processing of agricultural products results in massive generation of waste, which has an environmental impact. In current study on the node of valorisation of the processed waste, orange and banana peel waste are utilized for the production of biscuits. The fortified biscuits were analysed for proximate and sensory analysis. The fibre content of the fortified biscuits increases several folds. From the sensory score the proportion of biscuits with 7.5% and 10% has undesirable quality parameters, whereas up to 5% concentration all the parameters are more desirable. Fortifying biscuits with orange and banana peel powder may be an excellent source of dietary fibre and an enhanced valorisation technique.

A Tamoxifen (TAM) response varies significantly among individuals due to genetic variations in cytochrome P450 2D6 (CYP2D6), as well as other TAM pharmacokinetic and/or pharmacodynamic proteins. In this study, 40 ER+ breast cancer patients who received at least 6 months of TAM treatment were prospectively recruited. The study aimed to evaluate, using HPLC-MS/MS, plasma concentrations of TAM and its metabolites, and to study the association with genetic polymorphisms (CYP2D6*4, CYP3A4*1B, CYP3A5*3, UGT2B7*2, UGT2B15*2, SULT1A1*2, and ESR1V364E) and adverse reactions. Bivariable linear regression analyses showed that CYP3A4*1/*1B is significantly associated with an increase of 4-hydroxyTAM plasmatic concentration, a decrease of endoxifen/4-hydroxyTAM ratio and the elimination of 17β estradiol. It was found that the CYP3A4*1/1B genotype alone could explain part of the variability in [4OHTAM], [endoxifen]/[4OHTAM], and 17β-estradiol plasma levels. Similarly, SULT1A1*1/*2 genotype affects the [endoxifen]/[4OHTAM] plasma ratio. Multivariable predictive models, incorporating both polymorphisms and non-genetic variables, are proposed to explain [NdesMeTAM]/[TAM], [4OHTAM]/[TAM], [endoxifen]/[NdesMeTAM], [endoxifen]/[4OHTAM], and 17β-estradiol plasma levels, as well as for predicting hot flashes and cramps. This preliminary study suggests that the genetic variants studied may influence the bioactivation and elimination of TAM, the clinically observed adverse reactions, and potentially the treatment efficacy.

Hepatitis C virus (HCV) is a major public health threat associated with serious clinical consequences worldwide. The WHO reports an estimated 58 million people globally living with chronic hepatitis C virus (HCV) infection, with 1.5 million new cases and over 350,000 deaths annually due to HCV-related diseases. Despite the existence of multiple diagnostic methods, the accessibility of diagnosis and treatment is often hindered across the continent by limited resources, insufficient healthcare infrastructure, and low public awareness of Hepatitis C virus Hepatitis C virus infection is a major public health concern in African countries, which have the highest prevalence rates of HCV in the world (1–26%). Egypt, African country which had the highest prevalence of Hepatitis C virus infection in the world. Its national hepatitis C elimination initiative emerges as an exemplary case of success an having screened over 60 million individuals, and treated more than 4 million, thereby markedly diminishing both the prevalence and incidence of the disease. The strategies employed by Egypt offer critical insights and adaptable frameworks for other African Countries facing analogous challenges. The Aim of this Review is to highlight the triumphs and hurdles of Egypt's approach, while also providing strategic guidance for African Countries to accelerate the journey towards the eradication of hepatitis C.

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.

Measurements of complex magnetic permeability, μ(f,H)=μ′(f,H)-iμ″(f,H) and dielectric permittivity ε(f,H)=ε′(f,H)-iε″(f,H), in the frequency range, f of (0.4-7) GHz, and polarizing field, H of (0–135) kA/m, were performed, for a kerosene-based ferrofluid with magnetite nanoparticles. Based on these measurements, the phenomenon of ferromagnetic resonance was highlighted and some microwave propagation parameters of the ferrofluid were determined: the attenuation constant, α(f,H), and the reflection coefficient, R(f,H), at the air-material interface. Knowing these parameters we proposed a theoretical model establishing for the first time a new equation that allows the calculation of the overall reflection coefficient, Rw(f,H), of a ferrofluid with thickness d, deposited on a totally reflective support, following multiple internal reflections of the electromagnetic wave in the material. The results show that by increasing both, H, and d, the parameter, Rw(f,H) presents a minimum that decreases from 0.90 (for d=2mm) to 0.64 (for d=10 mm), at frequency f=5 GHz, which indicates an increase in the absorption of the electromagnetic wave by the ferrofluid. The obtained results are useful in the study of some materials that could be used as electromagnetic absorbers in the microwave range, by the determination of the overall reflection coefficient, Rw(f,H), controlled both by the thickness, d , of the absorber and by the external applied field, H.

Two mononuclear Ru(III) complexes of formula trans-[RuCl4(Hgua)(dmso)]·2H2O (1) and trans-[RuCl4(Hgua)(gua)]·3H2O (2) [Hgua = protonated guanine (gua), dmso = dimethyl sulfoxide] have been synthesized and characterized magnetostructurally. Compounds 1 and 2 crystallize in the monoclinic system with space groups P21/n and Pc, respectively. Each Ru(III) ion in 1 and 2 is six-coordinate and bonded to four chloride ions and one (1) or two (2) nitrogen atoms from guanine molecules and one sulfur atom (1) of a dmso solvent molecule, generating quasi regular octahedral geometries in both cases. In their crystal packing, the Ru(III) complexes are self-assembled mainly through an extended network of N-H⋯Cl hydrogen bonds and π⋯Cl type intermolecular interactions, forming novel supramolecular structures based on this paramagnetic 4d metal ion. Variable-temperature dc magnetic susceptibility measurements performed on microcrystalline samples of 1 and 2 show a different magnetic behavior. While 1 is a ferromagnetic compound at low temperature, 2 exhibits a behavior typical of noninteracting mononuclear Ru(III) complexes with S = 1/2. Ac magnetic susceptibility measurements reveal slow relaxation of the magnetization in the presence of external dc fields only for 2, hence indicating the occurrence of field-induced single-ion magnet (SIM) phenomenon in this mononuclear guanine-based Ru(III) complex.

Porous coatings on prosthetic implants encourage implant fixation. Enhanced fixation may be achieved using a magneto-active porous coating that can deform elastically in vivo on application of an external magnetic field, straining in-growing bone. Such coating, made of 444 ferritic stainless steel fibres, was previously characterised in terms of its mechanical and cellular responses. In this work, co-cultures of human osteoblasts and endothelial cells were seeded into a novel fibrin-based hydrogel embedded in a 444 ferritic stainless steel fibre network. Albumin was successfully incorporated into fibrin hydrogels improving the specific permeability and the diffusion of fluorescently-tagged dextrans without affecting their Young’s modulus. The beneficial effect of albumin was demonstrated by upregulation of osteogenic and angiogenic gene expression. Furthermore, mineralisation, extracellular matrix production and formation of vessel-like structures were enhanced in albumin-enriched fibrin hydrogels compared to fibrin hydrogels. Collectively, the results indicate that the albumin-enriched fibrin hydrogel is a promising bio-matrix for bone tissue engineering and orthopaedic applications.

The electroencephalogram (EEG) is a record of brain activity. Brain Computer Interface (BCI) technology formed by the EEG signal has become one of the hotspots at present. How to extract the feature signal of EEG is the most basic research of BCI technology. In this paper, A new method of recognizing fatigue, conscious, concentrated state of human brain is proposed by the combination of discrete wavelet transform and the neural network based on EEG signal. First of all, the law signal is preprocessed by the wavelet denoising method because the law EEG signal contains a large number of high frequency noise, which is decomposed into multi-layer high frequency signal and low frequency signal. thus, δ wave, θ wave, α wave, β wave are obtained by the wavelet transform. And then, frequency band energy of the different wave is regards as the feature signal of EEG. In the experiment, the feature signal is classified by radial basic function (RBF) and annealed chaotic competitive learning network (ACCLN). RBF and ACCLN networks are trained with 500 sets of sample data and are tested by 100 sets of samples in different mental states. The experimental results show that the average accuracy of RBF network under three conditions are 88.75%, 88.25%, 88.5%, respectively, and the correct rate of ACCLN network is 97%, 98%, 98%, respectively.

Based on the perspective of complex directed weighted network analysis, this paper constructs a static resilience evaluation framework for trade networks from two dimensions: structural resilience and node resilience. It studies the evolution of static resilience in the upstream, midstream, downstream, and recycling trade networks of global wood forest products from 2002 to 2021. The study finds that the total trade volume of global wood forest products has increased by 58.0%, 77.3%, 71.2%, and 77.2% in the upstream, midstream, downstream, and recycling sectors, respectively. The downstream trade network exhibits efficient information transmission and resource mobility capabilities. All four types of trade networks exhibit scale-free characteristics, with core countries such as China, the United States, and Germany playing a dominant role in the network and playing a crucial role in network robustness, but also increasing network vulnerability. In terms of unweighted assortativity, all four networks exhibit the characteristic of "small countries relying on hubs," while the weighted assortativity of the downstream network is positive, indicating enhanced network resilience. In terms of node resilience, countries exhibit significant differences and dynamic changes. China demonstrates strong resilience and adaptability, while the United States maintains its core position. China's influence in the downstream network has significantly increased, and India has rapidly emerged in the recycling network. To enhance network resilience, it is necessary to increase diversity and redundancy, establish effective emergency response mechanisms, and promote the diversification and green transformation of the supply chain.

Delve into the dynamic realms of neuroscience, neurology, and psychiatry with this groundbreaking editorial, unveiling the intricate mechanisms of brain function and dysfunction. Explore the latest advancements in molecular biology, genetics, epigenetics, pharmacology, and neuroimaging that are revolutionizing our understanding of neurological and psychiatric disorders. Uncover the challenges and gaps in translating cutting-edge research into practical clinical applications, aiming to enhance the lives of patients and their families. Discover the pivotal role of neuroplasticity in reshaping the brain's adaptability throughout life, alongside the promising potential of non-invasive brain stimulation techniques like transcranial magnetic stimulation and transcranial direct current stimulation. Embark on a journey of innovation and collaboration as researchers and clinicians strive to bridge the gap between bench and bedside, offering hope for improved diagnosis, prevention, and treatment of complex neurological and psychiatric conditions. Join us in this exploration of transformative ideas and novel interventions that hold the key to a brighter future for individuals and society.

Several methods of processing wood into strong, durable products for the construction industry provide transformative opportunities to substitute for less sustainable building materials. Carbon storage is a further advantage, with the added possibility of combustion for bioenergy at end of life. Intense research activity in this area is expected to open up new markets for wood fiber during the lifetime of trees now being planted. Here, wood-derived materials are classified according to the particle size, from metres to nanometres, into which the wood is fragmented before reconstitution. Materials made by densifying or chemically modifying solid wood with no fragmentation are already in production for exterior doors, window frames and cladding, with improved uniformity and stability compared with unmodified wood. Pre-commercial developments promise further gains in durability and strength. Emerging developments extend these process technologies to wood that has been chipped or stranded or pulped, retaining the above advantages over raw timber for weather-facing applications and adding processability by moulding or extrusion. Crucially, the raw material does not then need to be sawn timber but can be bioenergy-grade wood biomass. This will facilitate afforestation strategies that combine the aims of carbon sequestration and biodiversity.

The study of a flexible body immersed in a flowing medium is one of best way to find its aerodynamic shape. This Letter revisited the problem first studied by Alben, Shelley and Zhang (Nature 420, 479-481, 2002). The aerodynamic shape of the fibre is found by simpler approach and universal drag scaling laws of the flexible fibre in flowing medium are proposed by using dimensional analysis. The Alben scaling laws is being generalized and confirmed to be universal. Our study show that the Alben number is a measurement of maximum curvature of the fibre forced by dynamic pressure. A complete Maple code is provided for finding aerodynamic shape of the fibre in the flowing medium.

To investigate proposed ferromagnetic fluctuations in the single-layer Bi-2201 and La-214 high-Tc cuprates, we performed magnetization and electrical-resistivity measurements using single-layer Tl-2201 cuprates Tl2Ba2CuO6+d as well as La-214 La2-xSrxCuO4 in the heavily overdoped regime. Magnetization of Tl-2201 exhibited the tendency to be saturated in high magnetic fields at low temperatures, suggesting the precursor behavior toward the formation of a ferromagnetic order. It was found that the power of temperature n obtained from the temperature dependence of the electrical resistivity is ~ 4/3 and ~5/3 for Bi-2201 and La-214, respectively, and is ~ 4/3 at high temperatures and ~5/3 at low temperatures in Tl-2201. These results suggest that two-dimensional ferromagnetic fluctuations exist in Tl-2201 at high temperatures and Bi-2201 and that three-dimensional ferromagnetic fluctuations exist in Tl-2201 at low temperatures and La-214. The dimensionality of ferromagnetic fluctuations is understood in terms of the dimensionality of the crystal structure and the bonding of atoms in the blocking layer.

Color image binarization plays a pivotal role in image preprocessing work, significantly impacting subsequent tasks, particularly in text recognition. This paper concentrates on Document Image Binarization (DIB), aiming to separate a image into foreground (text) and background (non-text content). Through a thorough analysis of conventional and deep learning-based approaches, we conclude that prevailing DIB methods leverage deep learning technology. Furthermore, we explore the receptive fields pre- and post-network training to underscore the Transformer model's advantages. Subsequently, we introduce a lightweight model based on the U-Net structure, enhanced with the Mobile ViT module to better capture global information features in document images. Given its adeptness at learning both local and global features, our proposed model exhibits superior performance on two standard datasets (DIBCO2012 and DIBCO2017) compared to state-of-the-art methods. Notably, our proposed DIB method presents a straightforward end-to-end model devoid of additional image preprocessing or post-processing. Moreover, its parameter count is less than a quarter of the HIP'23 model, which achives best results on three datasets(DIBCO2012, DIBCO2017 and DIBCO2018). Finally, two sets of ablation experiments were conducted to verify the effectiveness of the proposed binarization model.

With the increasing demand for security surveillance, assisted driving, and remote sensing, target detection networks with rich environmental perception capabilities and high detection accuracy have become a research hotspot. However, detection techniques based on single-modality images face limitations in environmental adaptability, as they are easily affected by lighting conditions, as well as obstacles such as smoke, rain, and vegetation, leading to information loss and failing to meet the required accuracy. To address these issues, we propose a target detection network—IV-YOLO, which integrates features from both visible and infrared images. We built a dual-branch fusion network based on YOLOv8 (You Only Look Once v8) that combines infrared and visible images for target detection. On this foundation, we designed a Bidirectional Pyramid Feature Fusion structure (Bi-Fusion) to fully integrate complementary features from different modalities, reducing detection errors caused by feature redundancy and extracting fine-grained features of small targets through dual-modal fusion. Additionally, we developed the Shuffle-SPP structure, which combines channel and spatial attention mechanisms to enhance focus on deep features and further aggregate upsampling, extracting richer features. To improve the model’s expressive power, we designed a loss function tailored for multi-scale target detection boxes, accelerating the network’s convergence during training. Experimental results show that the proposed IV-YOLO network achieves an average precision (mAP) of 74.6% on the UAV remote sensing dataset, 75.4% on the KAIST pedestrian dataset, and 84.8% on the FLIR dataset. While ensuring detection accuracy, our model significantly reduces computational load, making it well-suited to meet real-time requirements. This architecture is expected to find broad application in fields such as autonomous driving, public safety, and others.

Management of sarcomas in specialist centers delivers significant benefits. In much of the world specialists are not available and development of expertise is identified as a major need. However the terms ‘specialist’ or ‘expert’ center are rarely defined. Our objective is to offer a definition for patient advocates and a tool for healthcare providers to underpin improving the care of people with sarcoma. SPAGN developed a discussion paper for a workshop at the SPAGN 2023 Conference, attended by 75 delegates. A presentation to the Connective Tissue Oncology Society (CTOS) and further discussion led to this paper. Core Principles were identified that underlie specialist sarcoma care. The primary principle is the multi-disciplinary team discussing every patient, at first diagnosis and during treatment. Principles for optimal sarcoma management include accurate diagnosis followed by safe, high-quality treatment, with curative intent. These Principles are supplemented by Features describing areas of healthcare, professional involvement, and service provision and identifying further research and development needs. These allow for variations because of national or local policies and budgets. We propose the term ‘Sarcoma Intelligent Specialist Network’ to recognize expertise wherever it is found in the world. This provides a base for further discussion and local refinement.

The use of biodegradable materials has a growing field of application due to environmental concerns, however, scientific research on incremental forming using biomaterials is scarce. Thus, this study focuses on the single point incremental forming (SPIF) process applied to a composite sheet that combines a biodegradable thermoplastic matrix (Solanyl) reinforced with natural fibres (flax). The influence of the process parameters on the final geometry is determined, evaluating the effect of the following factors: step depth, wall angle and temperature reached during the process. Additionally, a heated aqueous medium is incorporated which facilitates the formability of the composite sheets. This method is especially useful for materials that have poor formability at room temperature. The benefits of using controlled heat include the reduction of formation forces applied to the plate, improved accuracy due to the reduction of elastic recovery, and the manipulation of the samples remarkably close to the glass transition temperatures. Through this experimental study with the variables analysed, a maximum shaping depth of 310 mm is obtained. These results confirm that the single point shaping used with bioplastic materials is possible and has positive outcomes for incremental forming.

The effects of carbon fiber hybridisation on the thermal properties of woven kenaf reinforced epoxy composites kenaf fibre were studied. Woven kenaf hybrid composites at the different weave designs of plain and satin, and fabric count of 5 × 5 and 6 × 6 were manually prepared by vacuum infusion technique. Thermal properties of pure carbon fibre and hybrid composites were conducted by using thermogravimetric analyser (TGA) and differential scanning calorimeter (DSC). It was found that at high kenaf fibre content showed better thermal stability while the highest thermally stable was found in pure carbon fibre composite. The TG and DTG results showed that the amount of residue decreased in plain-designed hybrid composite compared to satin-designed hybrid composite. The DSC data revealed that the presence of woven kenaf increase decomposition temperature.

We propose a neural-network-based watermarking method that introduces the quantized activation function that approximates the quantization of JPEG compression. Many neural-network-based watermarking methods have been proposed. Conventional methods have acquired robustness against various attacks by introducing an attack simulation layer between the embedding network and the extraction network. The quantization process of JPEG compression was replaced by the noise addition process in the attack layer of the conventional methods. In this paper, we propose a quantized activation function that can simulate the JPEG quantization standard as it is in order to improve the robustness against the JPEG compression. Our quantized activation function consists of several hyperbolic tangent functions and is applied as an activation function for neural networks. Our network was introduced in the attack layer of ReDMark proposed by Ahmadi et al. to compare it with their method. That is, the embedding and extraction networks had the same structure. We compared the usual JPEG compressed images and the images applying the quantized activation function. The results showed that a network with quantized activation functions can approximate JPEG compression with high accuracy. We also compared the bit error rate (BER) of estimated watermarks generated by our network with those generated by ReDMark. We found that our network was able to produce estimated watermarks with lower BERs than those of ReDMark. Therefore, our network outperformed the conventional method with respect to image quality and BER.

The paper addresses the energy consumption minimization problem in wireless powered communication networks (WPCNs) and proposes a DT-SWIPT-assisted time allocation scheme, named DaTA, such that the wireless station can share the remaining energy after transmission to the HAP to those who have not transmitted to the HAP to minimize the energy consumption of the network. In addition to proposing a new frame structure, the paper also considers the SNR (Signal-to-Noise) constraint to guarantee that the HAP can successfully receive data from wireless stations. In the paper, the energy consumption minimization problem is formulated as a nonlinear programming model. We employ the SQP (Sequential Quadratic Programming) algorithm to figure out the optimal solution. Moreover, a heuristic method is proposed as well to obtain a near-optimal solution in a shorter time. Simulation results showed that the proposed scheme can have better performance than the related work in terms of energy consumption and energy efficiency.

Defectively manufactured and deliberately damaged composite laminates fabricated with different continuous reinforcing fibres (respectively, carbon and glass) and polymer matrices (respectively, thermoset and thermoplastic) were inspected in magnetic resonance imaging equipment. Two pulse sequences were evaluated during non-destructive examination conducted in saline solution-immersed samples to simulate load-bearing orthopaedic implants permanently in contact with biofluids. The orientation, positioning, shape, and especially the size of translaminar and delamination fractures were determined according to stringent structural assessment criteria. The spatial distribution, shape, and contours of water-filled voids were sufficiently delineated to infer the amount of absorbed water if thinner image slices than this study were used. The surface texture of composite specimens featuring roughness, waviness, indentation, crushing, and scratches was outlined, with fortuitous artefacts not impairing the image quality and interpretation. Low electromagnetic shielding glass fibres delivered the highest, while electrically conductive carbon fibres produced the poorest quality images, particularly when blended with thermoplastic polymer, though reliable image interpretation was still attainable.

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.

Currently there are fewer depth models applied to pepper picking detection, while the existing generalized neural networks have problems such as large model parameters, long training time, and low model accuracy.In order to solve the above problems, this paper proposes a Yolo-chili target detection algorithm for chili pepper detection. First, the classical target detection algorithm yolov5 is used as a benchmark model, and an adaptive spatial feature pyramid structure combining the attention mechanism and the idea of multi-scale prediction is introduced to improve the model's detection effect on occluded peppers and small target peppers. Secondly, a three-channel attention mechanism module is introduced to improve the algorithm's long-distance recognition ability and reduce the interference of redundant testers. Finally, the quantized pruning method is used to reduce the model parameters and realize the lightweight processing of the model. Applying the method to the homemade chili pepper dataset, the AP value of chili pepper reaches 93.11%; the accuracy rate is 93.51% and the recall rate is 92.55%.The experimental results show that yolo-chili is able to achieve accurate and real-time pepper detection under complex orchards.

ABSTRACT Purpose: Tongue image analysis for disease diagnosis is an ancient traditional non-invasive diagnosis technique widely used by traditional medicine practitioners. Deep Learning based multi-label disease detection models offer tremendous potential to clinical decision support systems, by facilitating preliminary diagnosis. Methods: In this work, we propose a multi-label disease detection pipeline, where in observation and analysis of tongue images captured and received via smartphones assist in predicting the health status of an individual. All images are voluntarily given by subjects consulting collaborating physicians. Images thus acquired are first and foremost classified either into a diseased or a normal category by a 5-fold cross-validation algorithm using a convolution neural network (MobileNetV2) model for binary classification. Once the diseased label is predicted, the image is used to diagnose multiple diseases using the prediction algorithm based on DenseNet121. Results: Average accuracy of 93 % was achieved in classifying diseased from normal healthy tongue by detection model with MobileNetV2 architecture. Multilabel disease classification produced more than 90% accurate results for the seven class labels considered. Conclusion: AI based image analysis shows promising results and an extensive dataset could provide further improvements to this approach. Rather than employing high-cost sophisticated image capturing setup, experimenting with smartphone images opens opportunity to provide preliminary health status to individuals on smartphone prior to further line of treatment and diagnosis.

Backscatter communication, which transmits information by passively reflecting radio frequency (RF) signals, has become a focal point of interest due to its potential to significantly enhance the energy efficiency of Wireless Power Mobile Edge Computing (WPMEC) networks and extend the operational lifespan of terminal devices. However, there is little research on the integration of user cooperation in WPMEC scenarios within volatile network environments. In this paper, we propose a dynamic task offloading scheme for a Backscatter-assisted WPMEC system, which involves two mobile devices (MDs) and a Hybrid Access Point (HAP) with user cooperation. We formulate the energy efficiency (EE) maximization problem as a stochastic programming problem, considering the randomness of task arrivals and time-varying wireless channels. By leveraging Dinkelbach’s method and stochastic network optimization technique, we transform the problem into a series of deterministic sub-problems for each time slot, and convert the non-convex sub-problem into convex ones. We propose a low-complex EE maximization algorithm to solve the convex problems efficiently. Extensive simulations are conducted to validate the performance of our algorithm under various system parameter settings. Experiment results demonstrate that our algorithm not only outperforms the benchmark algorithms by approximately 23%, but also stabilize all queues within the MEC system.

Background: Food waste has been a major social problem in recent years. Annually, 1.3 billion tons of food is wasted worldwide. It is recommended that food waste should be reduced at every stage of production. By-products from food processing have high nutritional value; therefore their use in the design of new products is advisable. Methods: By-products from tomato processing (tomato waste-TW) and pepper (defatted pepper seeds-DPS, pepper placenta-PP) were used at a level of 10-30% to produce extruded pasta. The farinographic characteristics, chemical composition, cooking quality, and color of the pasta were studied. Results: The results showed a significant up to 27% increase in the protein content in the TW30 samples, compared to the control. The TDF content increased over five times in DPS30 and TW30 (27.99% and 25.44%). The amino acid composition of the pasta improved with the fortification, but failed to achieve the complete protein by FAO. The DPS30, PP20, PP30, and all TW samples can be considered high-protein products according to the EU definition (min. 20% energy from protein). Conclusion: Vegetable waste can be a valuable additive for improvement of the nutritional value of food. Their addition is part of the zero-waste trend, which has a positive impact on the environment.

：Non-Euclidean data, such as social networks, and citation relationships between documents, has node information and structural information. Graph Convolutional Network(GCN) can automatically learn node features and association information between nodes. The core ideology of the graph convolutional network is to aggregate node information by using edge information, thereby generating a new node feature. In the process of updating node features, there are two core influencing factors. One is the number of neighboring nodes of the central node, the other is the contribution of the neighboring nodes to the central node. Due to the previous GCN methods not simultaneously considering the numbers and different contributions of neighboring nodes to the central node, we design the Adaptive Attention Mechanism(AAM). To further enhance the representational capability of the model, we utilize Multi-Head Graph Convolution(MHGC). Finally, we adopt cross-entropy(CE) loss function to describe the difference between the predicted results of node categories and the ground truth (GT). Combined with backpropagation, this ultimately achieves accurate node classification. Based on AAM, MHGC and CE, we contrive the novel Graph Adaptive Attention Network (GAAN). Experiments show that the classification accuracy has achieved outstanding performances on Cora, Citeseer and Pubmed datasets.

Process induced defects during thermoforming are widespread problems in laminate manufacturing. The aim of this study is to describe the effects of holding time and pressure on several properties of the manufactured laminate. A design of experiments is performed, followed by an analysis of variance to examine significant effects. Subsequently, a regression model is created to predict the laminate’s properties, which is also validated. The highest values of tensile strength and elongation at break are found for low settings of holding time and pressure. The fibre volume fraction is not affected by the process parameters. As holding time and pressure increase, significant fibre misalignment takes place, leading to a decrease of the mechanical properties. The regression model corresponds well with the validation and should be extended with further variables in subsequent studies.

As part of the efforts to reduce the environmental impacts caused by the aviation sector, the use of bio-based instead of fossil-based materials has been assessed as a possible mitigation option. Natural Fibre Polymer Composites have proven to have a higher environmental performance in the automotive sector and are emerging as an option for weight reduction in aircraft. This study quantifies, though Life Cycle Assessment, the environmental performance of specific flax-based composite panels intended for aircrafts as interior fitting elements (i.e. partition panels, tray tables, baggage compartments) compared to a glass fibre/epoxy composite with a honeycomb core. Through system expansion, the fate of co-products issued from the production of the flax fibre technical textile used as reinforcement in the biocomposite material were considered in the assessment. Results showed that for an application in the aeronautics sector, the weight of the panels is the upmost critical parameter shaping the overall environmental performance of panels. Focusing on the panel production only, the biocomposite panel showed a higher environmental performance in the categories of climate change and marine eutrophication compared to the conventional panel, and the fire suppressant agent was identified as the main contributor to the environmental impacts of the bio-based panel. Yet these gains were negligible when considering the full life cycle of the panels, due to the higher weight (14%) of the bio-based panels; which is linked to the bio-based panel being at a prototype stage.In order to improve the environmental performance of the biocomposite panel and thus reduce its weight, it was shown relevant to optimize geometry of the panel itself, especially its core, so less resin could be used.

The reinforcement of concrete by using polyolefin fibres may be considered in structural design to meet the requirements of the applicable code rules. In order to achieve a reliable use of such a composite material, use of full-scale real structures is needed. The conversion of lab testing data into real practice properties is challenging and significantly influenced by various aspects, among which the size effect is a key one. Given that the available literature does not report coinciding conclusions about such an effect on quasi-brittle materials reinforced with fibres, further research is justified. Therefore, this work studies the behaviour of notched beams with three proportional sizes by using self-compacting polyolefin reinforced concrete with a fibre volume fraction of 1.1%. Flexural testing was carried out according to the standard EN-14651, with the results revealing the existence of the size effect. In addition, a reduction of the residual strength identified in the larger specimens was observed in fracture surfaces with equal fibre content.

With the development of deep learning, several Graph Neural Networks (GNN)-based approaches have been utilized for text classification. However, GNNs encounter challenges in capturing contextual text information within a document sequence. To address this, a novel text classification model RB-GAT is proposed by combining RoBERTa-BiGRU embedding and a multi-head Graph ATtention Network (GAT). First, the pre-trained RoBERTa model is exploited to learn word and text embeddings in different contexts. Second, the Bidirectional Gated Recurrent Unit (BiGRU) is employed to capture long-term dependencies and bidirectional sentence information from the text context. Next, the multi-head graph attention network is applied to analyze this information, which serves as a node feature for the document. Finally, the classification results are generated through a Softmax layer. Experimental results on three benchmark datasets demonstrate that our method can achieve an accuracy of 71.48%, 98.45%, and 80.32% on Ohsumed, R8, and MR, which is superior to the existing nine text classification approaches.

This work presents a Fault-Tolerant Model Predictive Control (FTMPC) algorithm applied to a simulation model for sewer networks. The control system will aim to prevent contaminated water from escaping the network, especially during periods of heavy rainfall, that can cause combined sewer overflows (CSO), while maintaining the flow of water towards the wastewater treatment plant (WWTP) as close as possible to its nominal value. The main purpose of the work is to preserve the operation of the predictive controller as much as possible, in accordance with its operational objectives, when there may be anomalies affecting the elements of the control system, mainly sensors and actuators. Comparing the results obtained considering various types of faults, with situations of normal controlled operation and with the behavior of the sewer network when no control is applied, will allow some conclusions to be drawn at the end.

With prosperous development and successful application of deep learning technologies in the field of remote sensing, numerous deep-learning based methods have emerged for synthetic aperture radar (SAR) automatic target recognition (ATR) tasks over the past few years. Generally, most deep-learning based methods can achieve outstanding recognition performance in condition that an abundant of labeled samples are available to train the model. However, in real application scenarios, it is difficult and costly to acquire and to annotate abundant SAR images due to the imaging mechanism of SAR, which poses a big challenge to existing SAR ATR methods. Thereby, SAR target recognition under the situation of few-shot, where only a scarcely few of labeled samples are available, is a fundamental problem that needs to be solved. In this paper, we put forward a new method named enhanced prototypical network with customized region-aware convolution (CRCEPN) to specially tackle the few-shot SAR ATR tasks. To be specific, We first develop a feature extraction network based on a customized and region-aware convolution, which can adaptively adjust convolutional kernels and their receptive fields according to each SAR image’s own characteristics as well as the semantical similarity among spatial regions, thus augmenting its capability to extract more informative and discriminative features. To achieve accurate and robust target identity prediction under the few-shot condition, we then propose an enhanced prototypical network which can improve the representation ability of the class prototype by properly making use of training and test samples together, thus effectively raising the classification accuracy. Meanwhile, a new hybrid loss is designed to learn a feature space with both inter-class separability and intra-class tightness as much as possible, which can further upgrade the recognition performance of the proposed method. Experiments performed on the moving and stationary target acquisition and recognition (MSTAR) dataset and the OpenSARShip dataset demonstrate that the proposed method is competitive with some state-of-the-art methods for few-shot SAR ATR tasks.

M-polynomial of different molecular structures helps to calculate many topological indices. A topological index of graph G is a numerical parameter related to G which characterizes its molecular topology and is usually graph invariant. In the field of quantitative structure-activity (QSAR) quantitative structure-activity structure-property (QSPR) research, theoretical properties of the chemical compounds and their molecular topological indices such as the Zagreb indices, Randic index, Symmetric division index, Harmonic index, Inverse sum index, Augmented Zagreb index, multiple Zagreb indices etc. are correlated. In this report, we compute closed forms of M-polynomial, first Zagreb polynomial and second Zagreb polynomial of Octagonal network. From the M-polynomial we recover some degree-based topological indices for Octagonal network. Moreover, we give a graphical representation of our results.

The monocular structured light measurement system has low cost, simple algorithms, and a wide measurement range, making it widely applicable in multiple fields. However, in practical applications, the system may be affected by various factors including noise, non-linear intensity, changes in the reflectivity of the object being measured, and the calibration method and accuracy of the system, thereby reducing the measurement accuracy. In order to solve these issues, this paper proposes a new monocular structured light measurement method, mainly comprises the following two points: (a) A denoising algorithm for phase images based on DAE is proposed, which calculates the wrapped phase of the phase-shifted fringes after initial denoising by DAE. Then, a new phase-shifted fringe pattern is generated and input into DAE for iterative denoising, achieving high-performance image denoising and high-precision wrapped phase solution. (b) A new absolute phase height calibration algorithm is proposed, which introduces camera internal and external parameters and uses a two-layer feedforward network to directly establish the relationship between phase and the 3D coordinate system. Without the need for high-precision motion platforms, high-precision phase height calibration is attainable. In general, compared to conventional methods, the experimental results indicate the effectiveness of the proposed method for low-quality phase-shifted fringe 3D reconstruction. In addition, the measurement method described in this article also demonstrates effectiveness in high-dynamic scenes.

Neurodegeneration is a major problem in neuroscience and medicine as it causes and worsens many neurological and psychiatric disorders. It involves gradual loss of neurons at different levels of the brain, affecting cognition, emotion, behavior, consciousness, and attention. Understanding the causes and consequences of neurodegeneration is crucial for identifying the risk factors, biomarkers, and treatments. However, the current therapies are mostly symptomatic and ineffective. Therefore, new and innovative methods, such as noninvasive brain stimulation (NIBS), are needed to modulate brain activity and plasticity in a safe and reversible manner. This field is rapidly developing, with a focus on exploring new clinical applications, mechanisms, and combinations of NIBS and drugs. This special issue covers the latest progress and challenges in the study of neurodegeneration in cognitive impairment and mood disorders from the experimental, clinical, and translational perspectives. It addresses the complexity and diversity of these conditions, the lack of disease-modifying treatments, the ethical and practical issues of conducting research in vulnerable and diverse populations, the integration and interpretation of data from multiple sources and modalities, and the development and validation of novel methods such as NIBS, artificial intelligence, and drug repurposing. Collaborative efforts among various stakeholders are needed to improve our knowledge and treatment of neurodegeneration underlying cognitive impairment and mood disorders.

Artificial neural networks are highly efficient tools for numerous kinds of tasks, which include forecasting, regression, and recognizing patterns in classification and pattern recognition. An artificial neural network is a model for processing information using essential components called artificial neurons that are inspired by the human brain. Two significant aspects of delayed neural networks dynamic activity are their stability and the synchronization phenomenon. This work presents a bidirectional associative memory (BAM) incommensurate fractional-order delay neural network model. The incommensurate fractional orders are calculated within the stability region through the corresponding eigenvalues and their points of equilibrium. The involved neuron states are synchronized with each other for the calculated incommensurate stable fractional orders. The reduction of the time delay will give more relaxation time for the state variable $w_4(t)$ to be stable as compared to the state variable $w_3(t)$ due to the induction of extra activation functions. The theoretical findings of this research have significance for providing guidance and controlling the dynamic behavior of artificial neural networks.

Rice has become the second major food among rural people. It is one of the most nutrient and low-cost food available in Asia. Rice is composed of two major subspecies as Japonica and Indica and it belongs to the Poaceae family. The World Bank predicts that rice demand will grow 51% faster than population growth in 2025. It is high time to develop a model which predicts the type of infected paddy leaf and avoids the loss of grains. In this paper, the proposed hybrid CNN model, three CNN networks with different layer properties are trained parallel. The extracted feature from these CNN networks is combined to provide the classification of the type of infected leaf. Fieldwork was carried and about 340 samples are collected from five different types of classes. The proposed hybrid model was tested with both the fieldwork dataset and also with an available dataset on the internet. The experimental results show that the proposed model outperforms the other CNN network and yields an accuracy value of 98%, a precision value of 95%, and a misclassification rate of 4% respectively.

The purpose of this paper is to construct an evaluation system for AI painting software based on generative adversarial network technology, which optimizes the performance of the related software in terms of functionality, ease of use, system performance and safety. The results of the questionnaires are statistically analyzed. In addition, exploratory factor analysis was supported to extract the data of the study, which was ultimately used to calculate the weight and importance of each index through fuzzy hierarchical analysis method. The study constructed an evaluation system for AI painting software based on generative adversarial network technology, including 16 indicators of functionality, 16 indicators of ease of use, 7 indicators of system performance, and 8 indicators of safety respectively that their alpha coefficients were 0.882, 0.962, 0.932, 0.932, and 0.932, respectively. In addition, the accumulated explanatory variances of their coefficients were 84.405%, 84.897%, 84.013%, and 72.606%, respectively, 73.013%, and 72.606%, respectively. It is clear that the items included in each of the indicators are homogeneous, with a high degree of internal consistency. This paper suggests that the development of AI painting software focusing on functionality, ease of use, system performance and safety can enhance the market competitiveness of the software.