Dementia patients express a set of problematic and deteriorating symptoms, along self-care dependency. Overtime, the mental health of family caregivers of persons with dementia may suffer, putting them at a high risk for psychopathology, which may be associated with endangered wellbeing of demented people. This cross-sectional study examined the psychometric properties of the Depression Anxiety Stress Scale 8-items (DASS-8), DASS-12, DASS-21 in a convenient sample of 571 caregivers from northern Italy and southern Switzerland (Mean age = 53, SD = 12, range = 24–89 years). A bifactor structure of the three measures had the best fit; some items of the DASS-12/DASS-21 failed to load on their domain-specific factors. The three-factor structure was invariant across various groups (e.g., gender, education, etc.), expressed adequate reliability and convergent validity, and had strong positive correlation with the 3-item UCLA Loneliness Scale (UCLALS3). Dementia type had no effect on distress scores, which were higher among females, adult children caregivers, those caring for dependent patients, and those who received help with care. For 54.9 and 38.8% of the latter, care was provided by relatives and health professionals, respectively. Since the DASS-8 expresses adequate psychometrics comparable with the DASS-21, it may be used as a brief measure of distress in this population.

Liquid biopsies are an integral part of the diagnosis of cancer. Here, we have extended previous validation studies of a new targeted NGS panel to include the detection of copy number amplifications (CNAs), fusions, and exon skipping variants. Detection of these gene classes included specimens from clinical and healthy donors and cell lines (fusions: ROS1, EML4-ALK, NTRK1), (exon skipping: MET exon 14), and (CNAs: HER2, CDK6, EGFR, MYC and MET). The limit of detection (LOD) for fusion/skipping was 42 copies (QC threshold = 3 copies) and was verified using 3 additional fusion/skipping variants. LOD for CNAs was 1.40-fold-change (QC threshold = 1.15-fold change) and was verified with 3 additional CNAs. In precision studies, all fusion/skipping variants were detected in all runs and all days of testing (n=18/18; 100%); average CV for repeatability was 20.5% (range 8.7% - 34.8%), and for reproducibility was 20.8% (range 15.7% - 30.5%). For CNAs, 28/29 (96.6%) copy gains were detected. For CNAs, average CV of 1.85% (range 0% to 5.49%) for repeatability and 6.59% (range 1.65% to 9.22%) for reproducibility. The test panel meets the criteria for being highly sensitive and specific and extends its utility for the detection of clinically relevant variants in cancer.

The study of coherent vortices remains an urgent field of singular optics of vortex beams. In this paper, coherent properties of partially coherent vortex Bessel—Gaussian optical beams propagating through a uniform medium (free space) or a turbulent atmosphere are examined theoretically. The consideration is based on analytical solution of the equation for the transverse second-order mutual coherence function of the field of a partially coherent optical radiation in a turbulent atmosphere. For the partially coherent Bessel—Gaussian beam, the second-order mutual coherence function of the source field is taken as a Gaussian Shell-model. In this approximation, we analyze the behavior of the coherence degree and the integral coherence scale of these beams as a function of the propagation pathlength, propagation conditions, and beam parameters, such as the radius of the Gauss factor of the beam, parameter of the Bessel factor of the beam, topological charge, and correlation width of the source field of partially coherent radiation. In addition, the integral coherence scale of the partially coherent vortex Bessel—Gaussian beam is compared with that of the partially coherent Gaussian beam. It is found that as a partially coherent vortex Bessel—Gaussian beam propagates through a turbulent atmosphere, there appear not two (as might be expected: one due to atmospheric turbulence and another due to partial coherence of the source field), but only one ring dislocation of the coherence degree (due to the simultaneous effect of both these factors on the optical radiation). In addition, it is shown that the dislocation of the coherence degree that affects significantly the beam coherence level is formed only for beams, for which the coherence width of the source field is larger than the diameter of the first Fresnel zone.

We consider the Dirac electron as a nonperturbative particle-like solution consistent with its own Kerr-Newman (KN) gravitational and electromagnetic field. We develop the earlier models of the KN electron regularized by Israel and López, and consider the non-perturbative electron model as a bag model formed by Higgs mechanism of symmetry breaking. The López regularization determines the unique shape of the electron in the form of a thin disk with a Compton radius reduced by 4π. In our model this disk is coupled with a closed circular string which is placed on the border of the disk and creates the caused by gravitation frame-dragging string tension produced by the vector potential of the Wilson loop. Using remarkable features of the Kerr-Schild coordinate system, which linearizes the Dirac equation, we obtain solutions of the Dirac equation consistent with the KN gravitational and electromagnetic field, and show that this solution takes the form of a massless relativistic string. Parallelism of this model with quantum representations in Heisenberg and Schrodinger pictures explains remarkable properties of the stringy electron model in the relativistic scattering processes.

Radiation therapy (RT) is one of the main approaches of cancer therapy, including non-small cell lung cancer (NSCLC). Radiation-induced DNA double-stranded breaks (DSBs), single-stranded DNA breaks (SSBs) and oxidized nucleobases cause replication fork stalling, chromosomal instability or point mutagenesis and can lead to cell death. Translesion DNA synthesis (TLS) is one of the DNA damage tolerance (DDT) pathways promoting replication of damaged DNA. Here, we explored the effects of the knockout of primase-polymerase PRIMPOL (PRIMPOL −/−) and TLS polymerase POLI (POLI −/−) genes in two different monoclonal lines derived from human NSCLC A549 cells. Our study aimed to investigate the impact of their absence on the susceptibility of these cells to ionizing radiation (IR)-induced stress. Without IR, cells that do not have PRIMPOL or POLI experience a considerable rise in apoptosis by day 7 of continuous cultivation and a noteworthy decline in cell migration under spatial confinement when compared to the parental wild-type cells. Despite this, we have found that PRIMPOL −/− exhibited higher clonogenic survival compared to wild-type cell lines in response to IR stress. Both of these cell lines demonstrated remarkable resilience to radiation stress, surpassing the survival rate of the original A549 cells. POLI deficiency reduces cellular γH2AX foci accumulation, while the presence of highest number of γH2AX foci in PRIMPOL−/− cells was ATM-independent. We report for the first time that increased post-irradiation clonogenic survival of PRIMPOL −/− A549 cells might be associated with the increase of CD133+ but not CD44+ populations of cancer stem-like cells.

Motivation is essential in education, with highly motivated learners engaging more deeply with content and more ably transferring knowledge to new contexts. However, the validity of scales to measure motivation has been underexplored in pre-clerkship medical education. This study evaluates the validity of the Situational Motivation Scale (SIMS) for measuring motivation among pre-clerkship medical students in post-situational and short-term contexts. Using a sample of n = 156 students from the Frank H. Netter MD School of Medicine at Quinnipiac University, SIMS was tested for construct validity with attention paid to content, response process, internal structure, relationships to other variables, and consequences of use evidence. Small modifications from present to past tense in English were made for clarity following focus group feedback, and content validity was ensured via expert consultation. SIMS demonstrated strong internal consistency with a satisfactory Cronbach’s alpha for all subscales and anticipated patterns of correlations. Factor analysis confirmed appropriate factor loadings, with a stronger model fit for the short-term context, and no observed adverse effects on student engagement. These findings support the robustness of SIMS in capturing intrinsic, extrinsic, and amotivation in pre-clerkship medical students, highlighting its applicability for short-term and situational motivational assessment.

Solar radiation impacts diverse aspects of city life, such as harvesting energy with PV panels, passive heating of buildings in winter, cooling loads of air-conditioning systems in summer, and urban microclimate. Urban digital twins and 3D city models can support solar studies in the process of urban planning and provide valuable insights for data-driven decision support. This study examines the calculation of solar incident radiation at the city scale in Sofia using remote sensing data for the large shading context in a mountainous region and 3D vector building data. The building footprints and the terrain are preprocessed using GIS. The solar calculation uses a raytracing algorithm in 3D CAD at the city scale. To evaluate the performance, two cases were considered at the city and district scale, respectively. The total face count of meshes for the simulations constituted approximately 2 000 000 faces. 64379 roofs for the whole city and 4796 buildings for one district were selected. All calculations were performed in one batch and visualized in a 3D web platform. Using a 3D CAD environment establishes a seamless process of updating 3D models and simulations while preprocessing in GIS ensures working with large-scale datasets. The proposed method showed moderate computation time for both cases and could be extended to include reflected radiation and dense photogrammetric meshes in the future.

To conduct a precise impact assessment of marine diesel engines, a 200t floating impact platform was utilized to simulate realistic testing conditions. The testing generated the acceleration time curve and the impact response spectrum for the diesel engine. According to the applicable standards, the spectral velocity was chosen as the evaluation index, and an evaluation of the longitudinal, transverse, and vertical impact environment of the diesel engine was conducted. The shock factor interpolation method was corrected using the confidence interval based on normal distribution, and the interpolated confidence interval of the shock factor was determined. The findings reveal that: The 200t-class floating shock platform is capable of providing a reliable shock environment for diesel engine tests. The shock wave serves as the primary external force during the experiment, and the impact of bubble pulsation can be disregarded when assessing using the floating shock platform. To more accurately describe the experimental shock intensity when presented with dispersed data, the confidence interval can be utilized, and the resulting prediction formula provides a higher level of impact safety. The obtained conclusions can serve as a reference for predicting the shock environment of large shipboard equipment using the shock platform.

By the end of 2023, biowaste must be completely separated or recycled at source based on the EU legislation. Separate biowaste collection and valorisation towards biofuels could play an essential role in the biobased circular economy. In this context, the principal goal of this paper was to demonstrate in pilot scale the technological solution of bioethanol production via the utilisation of urban source-separated biowaste within the city context of Athens, Greece. More specifically, the main aim was the demonstration of a pilot system in continuous operation (over 10 operating cycles) with real feedstock – wet separately collected biowaste. From the 11 pilot trials performed with wet feedstock, the mean starch and cellulose degradation of the pilot trials amounted up to 80.69±16.27% and 79.41±10.37% respectively, while the bioethanol yield was 74.05±6.82%. The latter was comparable to that of more intensive pretreatment methods. Homogenization and shredding that was applied in this study, stand as promising and cost-effective pretreatment methods for bioethanol production from wet feedstock. Further research is needed to optimize conditions and evaluate scalability. Nevertheless, pilot scale testing is a crucial step in the development and deployment of this technology since it serves as a bridge between laboratory research and full-scale implementation, offering a practical and controlled environment to validate and optimize the technology while minimizing risks and uncertainties.

: Medial temporal lobe atrophy (MTA) remains an important imaging biomarker in brain magnetic resonance imaging (MRI) for diagnosing and evaluating Alzheimer's disease (AD). We compared the detection performance for determining significant MTA and the correlation with Scheltens' scale between two automatic volumetry software, NeuroQuant® and Aqua®, in non-AD and AD groups. Overall, 127 (non-AD, 54; AD, 73) individuals who underwent three-dimensional T1-weighted volumetric MRI in our hospital between January and August 2011 were enrolled and retrospectively analyzed using NeuroQuant® and Aqua®. The accuracy, sensitivity, and specificity for detecting significant MTA and the correlation for Scheltens' scale with the mean hippocampal volume of each software were evaluated. The study sample’s mean age was 78 (range, 59–97) years. Regarding accuracy and specificity, Aqua® was non-inferior to NeuroQuant®. Aqua® had significantly higher overall specificity than NeuroQuant® for detecting significant MTA. Additionally, the calculated mean hippocampal volume of Aqua® was more correlated with Scheltens' scale (Kendall's τ, - 0.48) than that of NeuroQuant® (Kendall's τ, - 0.20). The accuracy and specificity of Aqua® in detecting MTA are not inferior to those of NeuroQuant®. Additionally, Aqua® has a stronger correlation of mean hippocampal volume with Scheltens' scale than NeuroQuant®.

Underwater robotics equipped with visual detection systems have the function of detecting un-derwater artifacts, which is of great significance to deep-sea archaeology. Underwater artifacts are located in complex environments with poor imaging conditions, and the targets have chal-lenging problems such as breakage, stacking, and sediment burial, which causes the marine cul-tural target detection failures. To solve these problems, this paper proposes an underwater cul-tural target detection algorithm based on the deformable deep aggregation network model for underwater robotics exploration. To fully extract the target feature information of underwater targets in complex environments, this paper designs a multi-scale deep aggregation network with deformable convolutional layers. Besides, the BAM attention module is designed for fea-ture optimization, which enhances the potential feature information of the target while weak-ening the background interference information. Finally, the target prediction is achieved through feature fusion at different scales. The proposed algorithm has been extensively validat-ed and analyzed on the collected underwater artifact datasets, and the precision, recall, and mAP of the algorithm have reached 93.1%, 91.4%, and 92.8%, respectively. In addition, the present al-gorithm has been practically deployed on the underwater robotics. In the deep-sea tests, the ar-tifact detection frame rate reaches up to 19 fps, which satisfies the real-time target detection。

This paper presents a two-stage hierarchical neural network using image classification and object detection algorithms as key building blocks for a system that automatically detects a potential design right infringement. This neural network is trained to return the Top-N original design right records that highly resemble the input image of a counterfeit. Design rights specify the unique aesthetic characteristics of a product. Due to the rapid change of trends, new design rights are continuously generated. This work proposes an Ensemble Neural Network (ENN), an artificial neural network model that aims to deal with a large amount of counterfeit data and design right records that are frequently added and deleted. At first, we performed image classification and objection detection learning per design right using the existing models with a proven track record of high accuracy. The distributed models form the backbone of the ENN and yield intermediate results aggregated at a master neural network. This master neural network is a deep residual network paired with a fully connected network. This ensemble layer is trained to determine the sub-models that return the best result for a given input image of a product. In the final stage, the ENN model multiples the inferred similarity coefficients to the weighted input vectors produced by the individual sub-models to assess the similarity between the test input image and the existing product design rights to see any sign of violation. Given 84 design rights and the sample product images taken meticulously under various conditions, our ENN model achieved average Top-1 and Top-3 accuracies of 98.409% and 99.460%, respectively. Upon introducing new design rights data, a partial update of the inference model was done an order of magnitude faster than the single model. ENN maintained a high level of accuracy as it scaled out to handle more design rights. Therefore, the ENN model is expected to offer practical help to the inspectors in the field, such as the customs at the border that deal with a swarm of products.

Jabulile Ndlovu1 and Kebogile Mokwena1, 2 1 Department of Public Health, Sefako Makgatho Health Sciences University, Pretoria, South Africa 2 Research Chair in Substance abuse and population Mental Health, Sefako Makgatho Health Sciences University, Pretoria, South Africa Corresponding author: [email protected] Abstract An estimated 6% of the world population has serious mental illness, with one in four families having a member with some form of psychiatric disorder, who are mostly cared for by their relatives within a family setting. Although caregiving in a home setting is reported to be associated with significant mental distress, the burden of such distress is rarely measured. The purpose of this study was to quantify the burden of care among family caregivers of relatives with serious mental disorders, as well as to explore possible association between the caregiver burden of care and a range of caregiver and Mental Health Care User (MHCU) variables in a rural district in Kwa-Zulu Natal, South Africa. The Zarit Burden Interview (ZBI) scale was used to collect data from 357 caregivers, and STATA 14 was used to analyze data. The ages of the sample ranged from 18 to 65, with a mean of 50.29, and the majority (86%) were female and unemployed (83%). The ZBI scores ranged from 8 to 85, with a mean of 41.59. The majority (91%) tested positive for caregiver burden of care, which ranged from mild to severe. Using the Pearson Chi-square test of association (p=0.05), variables that were significantly associated with the burden of care were clinically related (caregiver self-reported depression, MHCU diagnosis, recent relapse of the MHCU), socio-economic (caregiver family monthly income, MHCU disability grant status and MHCU employment status) and socio-demographic (MHCU gender and MHCU level of education). The prevalence of burden of care is high and severe, and scarcity of resources in families and communities contribute to the high burden of care in these rural communities.

In response to reducing the energy cost of unmanned surface vehicles (USVs) while overcoming the low accuracy problem in surface target detection, a lightweight detection algorithm with multi-scale feature fusion is proposed. Based on the popular one-stage lightweight Yolov7-tiny target detection model, a lightweight extraction module is designed first by introducing the multiscale residual module to reduce the number of parameters and computational complexity while improving accuracy. The Mish and SiLU activation functions are used to enhance network feature extraction. Second, the path aggregation network employs coordinate convolution to strengthen spatial information perception. Finally, the dynamic head, which is based on the at-tention mechanism, improves the representation ability of object detection heads without any computational overhead. According to the experimental findings, the proposed model has 22.1% fewer parameters than the original model, 15% fewer GFLOPs, a 6.2% improvement in [email protected], a 4.3% rise in [email protected]:0.95, and it satisfies the real-time criteria. According to the research, the suggested lightweight water surface detection approach includes a lighter model, a simpler computational architecture, more accuracy, and a wide range of generalizability. It performs bet-ter in a variety of difficult water surface circumstances.

The sustainable metabolic engineering (SME) concept was defined by Stalidzans and Dace as an approach for the selection of most sustainable metabolic engineering designs taking the economic, environmental and social components of sustainability into account. In the centre of sustainability calculations is a genome scale metabolic model that provides full balance of all incoming and outgoing metabolic fluxes at steady state. Therefore, sustainability indicators are assigned for each exchange reaction enabling calculation of sustainability features of consumption or production of each metabolite. The further development of the SME concept depends on its implementation at the computational level to acquire applicable results – sustainable production strain designs. This study proposes for the first time a workflow and tools of SME implementation using constraint based stoichiometric modelling, genome scale metabolic models and growth coupled product synthesis approach. For SME application demonstration purposes, a relatively simple engineering task has been carried out. The most sustainable designs have been identified using Escherichia coli as chassis organism, glucose as a substrate and gene deletions as metabolic engineering tool. A growth coupled production design tool has been used to reduce the variability of sustainability. The 10 000 most sustainable designs were producing succinate as the main product with the number of deleted genes ranging from two to ten. A big number of similar designs has been identified due to the combinatorial explosion of different alternative combinations of gene deletion sets that result in interruption of the same metabolic pathways with the same impact on the metabolism.

To verify the authors’ mathematical model for water jet cooling of steel sheet, the previously performed experimental studies of temperature of the test plate in the roller quenching machine (RQM) were used. The calculated data of steel temperature evolution along the RQM length were compared with the readings of thermocouples inserted in the center of the test plate and at its surfaces. The core of the model is the dependence of the temperatures of film, transition and nucleate boiling regimes on the thickness of the oxide scale layer on the cooled surface. It was found that the model correctly takes into account the oxide scale on the sheet surface, flow rates and combinations of the RQM banks used, water temperature and other factors. For all experiments the calculated metal temperature corresponds well to the measured one. In the experiments with interrupted cooling, the calculated temperature plots repeat the characteristic changes in the experimental curves. The main uncertainty in the modeling of cooling in a wide temperature range can be contributed by the random nature of changes in the oxide scale thickness during water cooling. In this regard, the estimated thickness of the oxide scale layer should be considered as the main parameter for adapting the sheet temperature control process. The obtained data confirm the possibility of effective application of the model in the ACS of industrial TMCP (Thermo-Mechanical Controlled Process) systems.

Properties of spatially explicit data are often ignored or inadequately handled in machine learning for spatial domains of application. At the same time, resources that would identify these properties and investigate their influence and methods to handle them in machine learning applications are lagging behind. In this survey of the literature, we seek to identify and discuss spatial properties of data that influence the performance of machine learning. We review some of the best practices in handling such properties in spatial domains and discuss their advantages and disadvantages. We recognize two broad strands in this literature. In the first, the properties of spatial data are developed in the spatial observation matrix without amending the substance of the learning algorithm; in the other, spatial data properties are handled in the learning algorithm itself. While the latter have been far less explored, we argue they offer the most promising prospects for the future of spatial machine learning.

Objectives: This study aimed to investigate and compare sleep quality between patients with chronic temporomandibular disorder and healthy controls, and to analyze the association of sleep quality with disease characteristics, obstructive sleep apnea risk factors, and excessive daytime sleepiness. Methods: Chronic temporomandibular disorder patients (n=503) and 180 age- and sex-matched healthy controls were included, who completed well-organized clinical report and answered questions on sleep quality (Pittsburgh Sleep Quality Index), sleep apnea risk factors (STOP-Bang questionnaire), and excessive daytime sleepiness (Epworth sleepiness scale). Results: Mean global Pittsburgh Sleep Quality Index scores were significantly higher in the patients (6.25±2.77) than in healthy controls (6.25±2.77) (p<0.001). Poor sleep was significantly more prevalent in the patient group (56.9%) than in healthy controls (22.2%) (p<0.001). Compared with healthy controls, chronic temporomandibular disorder patients had a higher likelihood of obstructive sleep apnea. (STOP-Bang total score ≥3; 7.2% vs. 16.1%; p<0.01) and higher excessive daytime sleepiness (Epworth sleepiness scale score ≥10; 12.8% vs. 19.7%; p<0.05). Age (odds ratio=2.551; p<0.001), female sex (odds ratio=1.885; p=0.007), total Epworth sleepiness scale score (odds ratio=1.839; p=0.014), and headache attributed to temporomandibular disorder (odds ratio=1.519; p=0.049) were the most powerful predictors of poor sleep (global Pittsburgh Sleep Quality Index score ≥5) in chronic temporomandibular disorder patients. Conclusion: Chronic temporomandibular disorder patients had sleep quality impairment. Various factors, including peripheral and central factors, affect the patient's sleep quality. Therefore, in addition to sleep quality and sleep-related problems, the underlying central mechanism for poor sleep quality should be assessed when treating chronic temporomandibular disorder patients.

Enhancing the degree of functional multiplexing while assuring operational reliability and manufacturability at competitive costs are crucial ingredients for enabling comprehensive sample-to-answer automation, e.g., for use in common, decentralized “Point-of-Care” or “Point-of-Use” scenarios. This paper demonstrates a model-based ‘digital twin’ approach which efficiently supports the algorithmic design optimization of exemplary centrifugo-pneumatic (CP) dissolvable-film (DF) siphon valves towards larger-scale integration (LSI) of well-established “Lab-on-a-Disc” (LoaD) systems. Obviously, the spatial footprint of the valves and their upstream laboratory unit operations (LUOs) have to fit, at a given radial position prescribed by its occurrence in the assay protocol, into the locally accessible disc space. At the same time, the retention rate of a rotationally actuated CP-DF siphon valve and, most challenging, its band width related to unavoidable tolerances of experimental input parameters, need to slot into a defined interval of the practically allowed frequency envelope. To accomplish particular design goals, a set of parametrized metrics is defined, which are to be met within their practical boundaries while (numerically) minimizing the band width in the frequency domain. While each LSI scenario needs to be addressed individually on the basis of the digital twin, a suite of qualitative design rules and instructive showcases structures are presented.

The study of pain requires a balance between subjective methods that rely on self-reports and complementary objective biometrics that ascertain physical signals associated with subjective accounts. There are at present no objective scales that enable the personalized assessment of pain, as most work involving electrophysiology rely on summary statistics from a priori theoretical population assumptions. Along these lines, recent work has provided evidence of differences in pain sensations between participants with Sensory Over Responsivity (SOR) and controls. While these analyses are useful to understand pain across groups, there remains a need to quantify individual differences more precisely in a personalized manner. Here we offer new methods to characterize pain using the moment-by-moment standardized fluctuations in EEG brain activity centrally reflecting the person’s experiencing temperature-based stimulation at the periphery. This type of gross data is often disregarded as noise, yet here we show its utility to characterize the lingering sensation of discomfort raising to the level of pain, individually, for each participant. We show fundamental differences between the SOR group in relation to controls and provide an objective account of pain congruent with the subjective self-reported data. This offers the potential to build a standardized scale useful to profile pain levels in a personalized manner across the general population.

The Cosmic Ray Extremely Distributed Observatory (CREDO) is a newly formed, global collaboration dedicated to observing and studying cosmic rays (CR) and cosmic ray ensembles (CRE): groups of a minimum of two CR with a common primary interaction vertex or the same parent particle. The CREDO program embraces testing known CR and CRE scenarios, and preparing to observe unexpected physics, it is also suitable for multi-messenger and multi-mission applications. Perfectly matched to CREDO capabilities, CRE could be formed both within classical models (e.g. as products of photon-photon interactions), and exotic scenarios (e.g. as results of decay of Super Heavy Dark Matter particles), their fronts might be significantly extended in space and time, and they might include cosmic rays of energies spanning the whole cosmic ray energy spectrum. CRE are expected to be partially observable on Earth even if the initiating interaction or process occurs as far as ~1 Gpc away. They would have a footprint composed of at least two extensive air showers with correlated arrival directions and arrival times. Since CRE are mostly expected to be spread over large areas and, because of the expected wide energy range of the contributing particles, CRE detection might only be feasible when using available cosmic ray infrastructure collectively, i.e. as a globally extended network of detectors. Thus, with this review article, the CREDO Collaboration invites the astroparticle physics community to actively join or to contribute to the research dedicated to CRE, and in particular to share any cosmic ray data useful for the specific CRE detection strategies.

In common model testing practise, the measured values of the self propulsion test are split into the characteristics of the hull, the propeller and into the interaction factors. These coefficients are scaled separately to the respective full scale values and subsequently reassembled to give the power prediction. The accuracy of this power prediction depends {\em inter alia} on the accuracy of the measured values and the scaling procedure. An inherent problem of this approach is, that it is virtually impossible to verify each single step, because of the complex nature of the underlying problem. In recent years the scaling of the open-water characteristics of propeller model tests attracted a renewed interest, fuelled by competitive tests, which became the norm due to requests of the customer. This paper will show the influence of different scaling procedures on the predicted power. The prediction is compared to the measured trials data and the quality of the prediction will be judged. The procedures examined are the standard ITTC~1978 procedure plus derivatives of it: the Meyne, the strip method and the βᵢ-method.

In advanced world stroke is one of the disabling cause of death that can be managed with thrombolysis if presents early despite further risk of intracerebral haemorrhage. Secondary prevention is an important objective in ischaemic stroke where recurrence is very high with subsequent stroke. Carotid End Arterectomy impact a definitive and effective role for both symptomatic and asymptomatic carotid stenosis for secondary stroke prevention in selective cases. Thrombolysis is a potential primary management for certain group whereas carotid surgery employs secondary preventative measure in a specified ischaemic stroke group.

In the past decade, targeted therapies for solid tumors, including non-small cell lung cancer (NSCLC), have advanced significantly, offering tailored treatment options for patients. However, individuals without targetable mutations pose a clinical challenge, as they may not respond to standard treatments like immune-checkpoint inhibitors (ICIs) and novel targeted therapies. While the mechanism of action of ICIs seems promising, the lack of a robust response limits their widespread use. Although the expression levels of programmed death ligand 1 (PD-L1) on tumor cells are used to predict ICI response, identifying new biomarkers, particularly those associated with the tumor microenvironment (TME), is crucial to address this unmet need. Recently, inflammatory cytokines such as interleukin-1 beta (IL-1β) have emerged as a key area of focus and hold significant potential implications for future clinical practice. Combinatorial approaches of IL-1β inhibitors and ICIs may provide a potential therapeutic modality for NSCLC patients without targetable mutations. In this review, we discuss the role of IL-1β in NSCLC, its involvement in inflammatory pathways, and explore its potential role in the treatment of NSCLC.

Sublobar resection is a standard surgical procedure for small-sized non-small cell lung cancer (NSCLC). However, clinical role of adjuvant chemotherapy for small-sized NSCLC with pathological lymph node (LN) metastasis after sublobar resection is unknown. National Cancer Database was queried for NSCLC patients between 2004 and 2018. Eligibility included sublobar resection with pathological LN metastasis, R0 resection, Charlson comorbidity score = 0, clinical stage T1a-b, and tumor size ≤ 20 mm. Kaplan-Meier method with log-rank test and multivariable Cox proportional hazards analyses were used for assessing survival. Samples were evaluated before and after propensity score matching (PSM) with respect to age, sex, histologic type, and pathological LN status. Of 810 patients who met the eligibility criteria, 567 (70.0%) underwent adjuvant chemotherapy. After PSM, patients with adjuvant chemotherapy had a significantly longer survival than those without (median survival: 64.3 vs. 34.0 months, hazard ratio for death: 0.61, p < 0.0001). Multivariate analyses after PSM showed that younger age (p = 0.0206), female (p = 0.0005), and adjuvant chemotherapy (p < 0.0001) were independent prognostic factors for longer survival. Adjuvant chemotherapy has a prognostic impact in patients with small-sized NSCLC and pathological lymph node metastasis who undergo sublobar resection.

The RNA-binding protein Human antigen R (HuR) regulates stability, translation, and nucleus-to-cytoplasm shuttling of its target mRNAs. The protein has been progressively recognized as a relevant therapeutic target for several pathologies like cancer, neurodegeneration, as well as inflammation. Inhibitors of mRNA binding to HuR might thus be beneficial against a variety of diseases. Here we present the rational identification of structurally novel HuR inhibitors. In particular, by combining chemoinformatics approaches, high throughput virtual screening and RNA–protein pull-down assays, we show that the 4-(2-(2,4,6-trioxotetrahydropyrimidin-5(2H)-ylidene)hydrazineyl)benzoate ligand exhibits dose-dependent HuR inhibition in binding experiments. Importantly, the chemical scaffold is new with respect to the so-far known HuR inhibitors, opening up a new avenue for the design of pharmaceutical agents targeting this important protein.

Recently, SMR (Small Modular Reactor) is expected to be able to operate flexibly in conjunction with a high renewable energy penetration grid as a result of improved safety and easy power control compared to large nuclear power plants. SMRs such as South Korea’s System-integrated Modular Advanced ReacTor (SMART) are designed to use canned motors (CMs) for their reactor coolant pumps (RCPs) to enhance safety. The CMs passively enhance the reactor’s safety by preventing leakage of radioactive reactor coolant. However, with motor sizes designed to be as small as possible, and the increased air gap of CMs between stator and rotor, starting torque may be insufficiently small compared to that of similar sized induction motors (IMs). Thusly, CMs may require variable frequency drives (VFDs) to start. This paper compares the torque characteristics of CMs with those of IMs for SMART’s RCPs. ETAP is then used to perform motor starting analysis for CMs started with and without VFDs. The results are presented and analyzed to find out if the VFDs deal with the CM starting torque issue.

The selenoprotein glutathione peroxidase 4 (GPX4) is one of the main antioxidant mediators in the human body. Its central function involves the reduction of complex hydroperoxides into their respective alcohols often using reduced Glutathione (GSH) as a reducing agent. GPX4 has become a hotspot therapeutic target in biomedical research following its characterization as a chief regulator of ferroptosis, and its subsequent recognition as a specific pharmacological target for the treatment of an extensive variety of human diseases including cancers and neurodegenerative disorders. Several recent studies have provided insights into how GPX4 is distinguished from the rest of the glutathione peroxidase family, the unique biochemical properties of GPX4, how GPX4 is related to lipid peroxidation and ferroptosis, and how the enzyme may be modulated as a potential therapeutic target. This current report aims to review the literature underlying all these insights and present an up-to-date perspective on the current understanding of GPX4 as a potential therapeutic target.

Background: Real-world data on the molecular epidemiology of EGFR resistance mutations at or after progression with first- or second-generation EGFR-TKIs in patients with advanced NSCLC are lacking. Methods: This ongoing observational study was carried out by 23 hospital-based physicians in Greece. The decision to perform Cobas® EGFR Mutation Test v2 in tissue and/or plasma at disease progression was made before enrollment. For patients with negative/inconclusive T790M plasma-based results, tissue re-biopsy could be performed. Results: Ninety-six (96) eligible patients were consecutively enrolled (median age: 67.8 years) between July-2017 and September-2019. Of the patients, 98% were tested upon progression using plasma and 2% using tissue/cytology biopsy. The T790M mutation was detected in 16.0% of liquid biopsies. Tissue re-biopsy was performed in 22.8% of patients with a T790M-negative plasma result. In total, the T790M positivity rate was 21.9%, not differing between patients on first- or second-generation EGFR-TKI. Higher (≥2) ECOG performance status and longer (≥10 months) time to disease progression following EGFR-TKI treatment initiation were associated with T790M positivity. Conclusions: Results from plasma/tissue-cytology samples in a real-world setting, yielded a T790M positivity rate lower than previous reports. Fewer than one in four patients with negative plasma-based testing underwent tissue re-biopsy, indicating the challenges in routine care settings.

Previously, we demonstrated that IR triggers the invasion/migration of A549 cells via activation of an EGFR–p38/ERK–STAT3/CREB-1–EMT pathway. Here, we have demonstrated the involvement of a novel intracellular signaling mechanism in γ-ionizing radiation (IR)-induced migration/invasion. Expression of receptor-interacting protein (RIP) 1 was initially increased upon exposure of A549, a non-small cell lung cancer (NSCLC) cell line, to IR. IR-induced RIP1 is located downstream of EGFR and involved in the expression/activity of matrix metalloproteases (MMP-2 and MMP-9) and vimentin, suggesting a role in epithelial-mesenchymal transition (EMT). Our experiments showed that IR-induced RIP1 sequentially induces Src-STAT3-EMT to promote invasion/migration. Inhibition of RIP1 kinase activity and expression blocked induction of EMT by IR and suppressed the levels and activities of MMP-2, MMP-9, and vimentin. IR-induced RIP1 activation was additionally associated with stimulation of the transcriptional factor NF-κB. Specifically, exposure to IR triggered NF-κB activation and inhibition of NF-κB suppressed IR-induced RIP1 expression followed by a decrease in invasion/migration as well as EMT. Based on the collective results, we propose that IR concomitantly activates EGFR and NF-κB and subsequently triggers the RIP1–Src/STAT3–EMT pathway, ultimately promoting metastasis.

A numerical study for the effect of crest width, breaking parameter and trunk permeability on hydrodynamics and flow behavior in the vicinity of rubble-mound, permeable, zero-freeboard breakwaters (ZFBs) is presented. The modified two dimensional Navier-Stokes equations for two-phase flows in porous media with a Smagorinsky model for the subgrid scale stresses were solved numerically. An immersed-boundary/level-set method was used. The numerical model was validated for the cases of wave propagation over a submerged impermeable trapezoidal bar and over a low-crested permeable breakwater. Five cases of breakwaters were examined, and the main results are: (a) The size of the crest width, B, does not notably affect the wave reflection, vorticity and currents in the seaward region of ZFBs, while wave transmission, currents in the leeward side, and mean overtopping discharge, all decrease with increasing B. A non-monotonic behavior of the wave setup is also observed. (b) As the breaking parameter decreases, wave reflection, transmission, currents, mean overtopping discharge, and wave setup decrease. This observation is also verified by relevant empirical formulas. (c) As the ZFB trunk permeability decreases, an increase of the wave reflection, currents, wave setup, and a decrease of wave transmission and mean overtopping discharge is observed.

Heterogeneous distribution of delivery or treatment modalities within the tumor mass is a crucial limiting factor for a vast range of theranostic applications. Understanding the interactions between a nanomaterial and the tumor microenvironment will help to overcome challenges associated with tumor heterogeneity as well as the clinical translation of nanotheranostic materials. This study aims to evaluate the influence of protein surface adsorption on gold nanoparticle (GNP) biodistribution using high resolution Computed Tomography (CT) preclinical imaging in C57BL/6 mice harboring Lewis Lung Carcinoma (LLC) tumors. LLC provides a valuable model for study due to its highly heterogenous nature which makes drug delivery to the tumor challenging. By controlling the adsorption of proteins on the GNP surface, we hypothesize that we can influence the intratumoral distribution pattern and particle retention. We performed an in vitro study to evaluate the uptake of GNPs by LLC cells and an in vivo study to assess and quantify the GNP biodistribution by injecting concentrated GNPs citrate stabilized or passivated with Bovine Serum Albumin (BSA) intratumorally into LLC solid tumors. Quantitative CT and Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) results both confirm the presence of particles in the tumor 9 days post-injection (n=8 mice/group). Significant difference is highlighted between citrate-GNP and BSA-GNP groups (**p < 0.005, Tukey’s multiple comparisons test), confirming the protein corona of GNPs modifies intratumoral distribution and retention of the particles. In conclusion, our investigations show that the surface passivation of GNPs influences the mechanism of cellular uptake and intratumoral distribution in vivo highlighting the spatial heterogeneity of the solid tumor.

The emergence of acquired resistance to EGFR-TKIs is almost inevitable even after a remarkable clinical response. Secondary mutations such as T790M and C797S are responsible for the resistance to 1st/2nd generation (1/2G) TKIs and 3G TKIs, respectively. To overcome both the T790M and C797S mutations, novel 4G EGFR-TKIs are now under early clinical development. In this study, we evaluated the efficacy of a 4G EGFR-TKI in the treatment of lung cancer with EGFR mutation as well as explored resistance mechanisms to a 4G TKI. First, we compared the efficacies of seven TKIs including a 4G TKI, BI4020, against Ba/F3 cell models that simulate resistant tumors after frontline osimertinib treatment failure because of a secondary mutation. We also established acquired re-sistant cells to BI4020 by chronic drug exposure. Ba/F3 cells with an osimertinib-resistant secondary mutation were refractory to all 3G TKIs tested (alflutinib, lazertinib, rezivertinib, almonertinib, and befotertinib). BI4020 inhibited the growth of C797S-positive cells; however it was not effective against L718Q-positive cells. Erlotinib was active against all Ba/F3 cells tested. In the analysis of resistance mechanisms of BI4020 resistant (BIR) cells, none harbored secondary EGFR mutations. HCC827BIR cells had MET gene amplification and were sensitive to a combination of capmatinib (MET-TKI) and BI4020. HCC4006BIR and H1975BIR cells exhibited epithelial to mesenchymal transition. This study suggests that erlotinib may be more suitable than 4G TKIs to overcome sec-ondary mutations after front-line osimertinib. We found that off-target mechanisms that cause re-sistance to earlier generation TKIs will also cause resistance to 4G TKIs.

Keywords: spinal muscular atrophy (SMA); survival of motor neuron 1 (SMN1); SMN2; SMN protein; antisense oligonucleotide (ASO); nusinersen; gene therapy; onasemnogene; risdiplam; small molecule, combination therapy

Graphene was synthesized through a two-step pyrolysis method using waste candlenut (Aleurites moluccanus) shells as the precursor. Cerium (Ce)/graphene composites were prepared via an impregnation technique. The resulting graphene and Ce/graphene were characterized using various analytical methods, including Scanning Electron Microscopy with Energy-Dispersive Spectroscopy (SEM-EDS), X-ray Diffraction (XRD), X-ray Photoelectron Spectroscopy (XPS), Transmission Electron Microscopy (TEM), Thermo Gravimetric Analysis (TGA), Fourier Transform Infrared (FTIR) spectroscopy, Cyclic Voltammetry (CV), and Linear Sweep Voltammetry (LSV). The bio-carbon produced predominantly exhibited a graphene structure with flat carbon morphology and an interlayer distance of 0.33 nm. This structural information is supported by XRD data, which shows a broad and weak peak at 2θ = 26° corresponding to the C (002) plane, indicative of graphene presence. FTIR, XPS, and Raman spectroscopy further confirmed the presence of graphene through the detection of Csp2 aromatic bonds and the characteristic D, G, and 2D peaks. Notably, the performance of cerium can be enhanced by the incorporation of graphene, attributed to the large surface area and chemical interactions between Ce and graphene. Consequently, candlenut-derived graphene shows potential as a supportive material for modifying the properties of cerium, thereby opening avenues for various advanced applications, such as sustainable and high-performance energy storage systems.

In the rapidly evolving landscape of the AI era, the measurement of Computational Thinking ability among Computer Science (CS) University students is of paramount importance. This study introduces UniCTCheck, a novel method designed to assess the main components of Computational Thinking in CS students. Utilizing two key instruments, namely the web application CTScore and the psychometric scale CTProg, this research aims to precisely evaluate seven core components of Computational Thinking and six programming concepts skills essential for CS students. The study, conducted at Rey Juan Carlos University and Atlantic Technological University Galway, involved a diverse sample of students from different year levels and program specializations. Through a rigorous research design, including sampling strategies and data collection tools, this study seeks to address critical research questions related to measurement of variations in students' Computational Thinking and programming skills by gender, university level, and location. By shedding light on the significance of Computational Thinking and programming in the educational realm, this research contributes to the existing literature and underscores the essential role of computational skills in the modern era.

The Universe is treated as an unbound thermodynamic system in which two forces of Nature compete: Attractive gravity loss and repulsive entropic gain. Entropic gain, although not presently considered a force of Nature, is otherwise well known and can be expressed with the gas laws. The Universe at the time of last scattering was all gas. Its instant gain is expressed as gas pressure. When the Gibbs equation is properly applied to this unbound gas, the resulting atomic Hubble parameter is found to be exclusively dependent on baryon density, giving a constant and perpetual two-to-one gain/loss ratio. Instant loss to gravity cannot offset gas pressure in this “thermal equation”. By contrast, the Friedmann equation, a ΛCDM foundation, treats baryon mass as accreted, having little if any entropic gain. These two equations are actually equivalent. Expanding accreted matter at its comoving critical density is herein shown to behave exactly like a freely expanding gas. However, neither equation includes all of today’s Universal kinetic energy, and cannot by themselves fully account for observed stellar movement. The present paper proposes the plausible existence of highly energetic or “suprathermal” free electrons which comprise about half of the intergalactic medium’s kinetic total. It is this suprathermal energy which Λ expresses. The fluid equation, another ΛCDM foundation, eliminates entropic gain from the Gibbs equation. Additionally, rest mass’s Einstein energy E=Mc² is treated as a thermal variable. These two assumptions, isoentropy and energy conflation, led astronomers to populate Λ as they followed the prior art.

Background. Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous systemic syn-drome that often coexists with multiple comorbidities. In high-complex COPD patients the role of the Cumulative Illness Rating Scale (CIRS) as a risk predictor of COPD exacerbation is not known. Objective. To determine the effectiveness of the CIRS score in detecting the association of comorbidities and disease severity with the risk of acute exacerbations in COPD patients. Methods. 105 adults with COPD were included in this prospective study. All participants at baseline had at least 2 moderate exacerbations or 1 leading to hospitalization. The primary outcome was a composite of moderate or severe COPD exacerbation during 12-months follow-up. Results. CIRS indices (CIRS total score, Severity Index and Comorbidity Index) showed a positive correlation with mMRC, CAT and a negative correlation with FEV1, FVC, FEV1/FVC. The three CIRS indices were able to predict the 12-month rate of moderate or severe exacerbation (CIRS-Total Score: Hazard Ratio (HR) = 1.12 (95% CI: 1.08-1.21); CIRS-Severity Index: HR = 1.21 (95% CI: 1.12-1-31); CIRS-Comorbidity Index = 1.58 (95% CI: 1.33-1.89)). Conclusions. Among patients with COPD, comorbidity number and severity, assessed by the CIRS score, influence the risk in moderate to severe exacerbations. The CIRS score also correlates with the severity of respiratory symptoms and lung function

Deepfakes are manipulated or altered images, or video, that are created using deep learning models with high levels of photorealism. The two popular methods of producing a deepfake are based on either convolutional neural networks (CNN), or autoencoders. Deepfakes created using CNN comparatively show higher qualities of realism, yet oftentimes leave artifacts and distortions in the generated media that can be detected using machine learning and deep learning algorithms. In recent years, there has been an influx of periocular image and video data because of the increase usage of face masks. By wearing masks, much of what is used for facial recognition is hidden, leaving only the periocular region visible to an observer. This loss of vital information leads to easier misidentification of media, allowing deepfakes to less likely be identified as fake. In this work, feature extraction methods, such as Scale-Invariant Feature Transform (SIFT), Histogram of Oriented Gradients (HOG), and CNN, are used to train an ensemble deep learning model to detect deepfakes in videos on a frame-by-frame level based on the periocular region. Our proposed model is able to distinguish original and manipulated images with accuracies around 98.9 percent, which is an improvement to previous works by combining SIFT and HOG for deepfake detection in convolutional neural networks.

The shape of the knee is a chain-like structure of an ellipsoid. This suggests that, in the latest study, in addition to rolling, there is slippage in the motion of the knee. This paper selects a mechanical braking model for cattle based on two mechanical structures in different directions at the joints. The experimental results show that the modified dynamical system has strong chaotic properties. This can be one of the basis for judging various health states.

The central goal of few-shot scene classification is to learn a model that can generalize well to a novel scene category (UNSEEN) from only one or a few labeled examples. Recent works in the remote sensing (RS) community tackle this challenge by developing algorithms in a meta-learning manner. However, most prior approaches have either focused on rapidly optimizing a meta-learner or aimed at finding good similarity metrics while overlooking the embedding power. Here we propose a novel Task-Adaptive Embedding Learning (TAEL) framework that complements the existing methods by giving full play to feature embedding’s dual roles in few-shot scene classification - representing images and constructing classifiers in the embedding space. First, we design a lightweight network that enriches the diversity and expressive capacity of embeddings by dynamically fusing information from multiple kernels. Second, we present a task-adaptive strategy that helps to generate more discriminative representations by transforming the universal embeddings into task-specific embeddings via a self-attention mechanism. We evaluate our model in the standard few-shot learning setting on two challenging datasets: NWPU-RESISC4 and RSD46-WHU. Experimental results demonstrate that, on all tasks, our method achieves state-of-the-art performance by a significant margin.

Environmental conditions, such as temperature, carbon dioxide, and nutrient availability, are altered by urban conditions at regional scales with potential for impact on tree leaf structure. Our goal was to compare leaf morphological characteristics driven by physiological acclimation in red maple (Acer rubrum L.) trees in deciduous forests embedded in a small (Newark, DE) and a large (Philadelphia, PA) city. The study was conducted in six urban forests on eighteen mature red maple trees in a long-term urban forest network. We hypothesized that red maples in Philadelphia forests compared to Newark forests will have a thicker upper epidermal layer, spongy palisade and mesophyll layer, longer and wider stomates, and lower stomate density. Additionally, we hypothesized that red maples in Philadelphia forests compared to Newark forests will have lower leaf water content and specific leaf area, and greater leaf thickness, fresh leaf weight, dry leaf weight, and leaf dry matter content. Our results for stomate length and stomate width supported our predictions; red maple leaves had longer and wider stomates in Philadelphia forests than in Newark forests. The increased stomate size in red maple trees suggests potential altered gas exchange behavior and mutual abiotic stress mitigation responses in red maple to greater urbanization impacts in Philadelphia forests. This supports previous findings of possible physiological and biochemical acclimation of red maple trees to urban conditions. Furthermore, the findings from this study suggest red maple trees may be a good biomonitor of regional scale impacts in urban environments.

In this paper, we will revisit the derivation of Heisenberg's uncertainty principle. We will see how the Heisenberg principle collapses at the Planck scale by introducing a minor modification. The beauty of our suggested modification is that it does not change the main equations in quantum mechanics; it only gives them a Planck scale limit where uncertainty collapses. We suspect that Einstein could have been right after all, when he stated, ``God does not throw dice." His now-famous saying was an expression of his skepticism towards the concept that quantum randomness could be the ruling force, even at the deepest levels of reality. Here we will explore the quantum realm with a fresh perspective, by re-deriving the Heisenberg principle in relation to the Planck scale. We will show how this idea also leads to an upper boundary on uncertainty, in addition to the lower boundary. These upper and lower boundaries are identical for the Planck mass particle; in fact, they are zero, and this highlights the truly unique nature of the Planck mass particle. Further, there may be a close connection between light and the Planck mass particle: In our model, the standard relativistic energy momentum relation also seems to apply to light, while in modern physics light generally stands outside the standard relativistic momentum energy relation. We will also suggest a new way to look at elementary particles, where mass and time are closely related, consistent with some of the recent work in experimental physics. Our model leads to a new time operator that does not appear to be in conflict with the Pauli objection. This indicates that both mass and momentum come in quanta, which are perfectly correlated to an internal Compton `clock' frequency in elementary particles.

Small cell lung cancer (SCLC) and large cell neuroendocrine carcinoma (LCNEC) have recently been grouped as lung neuroendocrine carcinomas (NECs). Because these lung NECs are clinically malignant and their treatment strategies differ from those of non-SCLC, the quality of diagnosis has a significant prognostic impact. The diagnosis of LCNEC requires positive immunohistochemical staining with chromogranin A, synaptophysin, and CD56, along with morphological diagnosis, and insulinoma-associated protein 1 (INSM1) has been proposed as an additional marker but sometimes experience difficulty in making the diagnosis. We investigated Musashi-1 as a novel immunohistochemical marker in 42 patients with SCLCs and 44 with LCNECs who underwent lung resection between 1998 and 2020 at our institution. We found Musashi-1 expression in 41 (98%) of 42 SCLCs and 40 (90%) of 44 LCNECs. These findings were similar to CD56 expression and superior to synaptophysin, chromogranin A, and INSM1. Musashi-1 also tended to show more diffuse and intense staining, especially in LCNEC, with more cases staining &gt;10% than any other existing markers (Musashi-1, 77%; INSM1, 45%; chromogranin A, 34%; synaptophysin, 41%; and CD56, 66%). In conclusion, we identified Musashi-1 as a novel immunohistochemical staining marker to aid in the diagnosis of lung NEC.

The majority of EGFR mutations (85–90%) are exon 19 deletions and L858R point mutation of exon 21, characterized by high sensitivity to EGFR-tyrosine kinase inhibitors (TKIs). Less is known about uncommon mutations (10-15% of EGFR mutations). Predominant mutation types in this category include exon 18 point mutations, exon 21 L861X, exon 20 insertions and exon 20 S768I. This group presents a heterogeneous prevalence, partly due to the different testing methods and to the presence of compound mutation, which in some cases leads to shorter overall survival and different sensitivity to different TKIs than simple mutations. EGFR-TKI sensitivity may also vary depending on the specific mutation and the tertiary structure of the protein. The best strategy remains uncertain and the data of TKIs efficacy are founded of few prospective and some retrospective series. Newer investigational agents are still under study and there are no other approved specific treatment targeting uncommon EGFR mutations. Defining the best treatment option for this patient population remain un unmet medical need. The objective of this review is to evaluate existing data on outcomes, epidemiology and clinical characteristics of lung cancer patients with rare EGFR mutations, with a focus on intracranial activity and response to immunotherapy.

Under the oxidative stress condition, the small RNA (sRNA) Oxys that acts as essential post-transcriptional regulators of gene expression is produced and plays a regulatory function with the assistance of the RNA chaperone Hfq protein. Interestingly, experimental studies found that the N48A mutation of Hfq protein could enhance the binding affinity with OxyS while resulting in defection of gene regulation. But, how the Hfq protein interacts with sRNA Oxys and the origin of the stronger affinity of N48A mutation are both unclear. In this paper, molecular dynamics (MD) simulations were performed on the complex structure of Hfq and OxyS to explore their binding mechanism. The molecular mechanics generalized Born surface area (MM/GBSA) and interaction entropy (IE) method were combined to calculate the binding free energy between Hfq and OxyS sRNA, and the computational result is in excellent correlation with the experimental result. Per-residue decomposition of the binding free energy revealed that the enhanced binding ability of the N48A mutation mainly comes from the increased van der Waals interactions (vdW). This research explores the binding mechanism between Oxys and chaperone protein Hfq, and revealed the origin of the strong binding affinity of N48A mutation. The results provided important insights on the mechanism of gene expression regulation affected by protein mutations.

Lung cancer remains the leading cause of cancer-related death and it is usually diagnosed in advanced stages (stage III or IV). Recently, the availability of targeted strategies and of immunotherapy with checkpoint inhibitors (ICI) has favorably changed patient prognosis. Treatment outcome is closely related to tumor biology and interaction with the host immune microenvironment (TME). Whether for targeted therapies the response relies on the presence of specific genetic alterations in tumor cells, for ICI accurate biomarkers of response are lacking and clinical outcome likely depends on multiple factors, host and tumor-related. This paper is an overview of the ongoing research on predictive factors both from in-vitro/ex-vivo analysis (ranging from conventional pathology to molecular biology) and in-vivo analysis, where molecular imaging is showing an exponential growth and use due to the technological advancement and to the new bioinformatics approaches applied to image analyses that allow specific features recovery in specific tumor subclones.

Transfer RNA (tRNA) molecules contain various post-transcriptional modifications that are crucial for tRNA stability, translation efficiency, and fidelity. Besides their canonical roles in translation, tRNAs also originate tRNA-derived small RNAs (tsRNAs), a class of small non-coding RNAs with regulatory functions ranging from translation regulation to gene expression control and cellular stress response. Recent evidence indicates that tsRNAs are also modified, however, the impact of tRNA epitranscriptome deregulation on tsRNAs generation is only now beginning to be uncovered. The 5-methyluridine (m5U) modification at position 54 of cytosolic tRNAs is one of the most common and conserved tRNA modifications among species. The tRNA methyltransferase TRMT2A catalyzes this modification, but its biological role remains mostly unexplored. Here, we show that TRMT2A knockdown in human cells induces m5U54 tRNA hypomodification, resulting in angiogenin (ANG) dependent tsRNA formation. More specifically, m5U54 hypomodification is followed by ANG overexpression and tRNA cleavage near the anticodon, resulting in accumulation of 5’tRNA-derived stress-induced RNAs (5’tiRNAs), in particular 5’tiRNA-GlyGCC and 5’tiRNA-GluCTC. Additionally, transcriptomic analysis confirms that down-regulation of TRMT2A and consequently m5U54 hypomodification impacts the cellular stress response and RNA stability, which is often correlated with tsRNA generation. Accordingly, exposure to oxidative stress conditions induces TRMT2A down-regulation and tsRNA formation in mammalian cells. These results establish a link between tRNA demethylation and ANG-dependent tsRNAs formation and unravel m5U54 as a tRNA cleavage protective mark.

To contribute to small and medium enterprises (SMEs) sustainable transition into the circular economy, the study proposes the activation of organizational learning (OL) processes – denoted here as multi-level knowledge creation, transfer, and retention processes – as a key phase in introducing circular business models (CBMs) at SME and supply chain (SC) level. The research employs a mixed-method approach, using the focus group methodology to identify contextual elements impacting on CBM-related OL processes, and a survey-based evaluation to single out the most frequently used OL processes inside Italian construction SMEs. As main result, a CBM-oriented OL multi-level model offers a fine-grained understanding of contextual elements acting mutually as barriers and drivers for OL processes, as possible OL dynamics among them. The multi-level culture construct – composed of external stakeholders’, SC stakeholders’, and organizational culture – identify the key element to activate CBM-oriented OL processes. Main implications are related to the identification of cultural, structural, regulatory, and process contextual elements across the external, SC, and organizational levels, and their interrelation with applicable intraorganizational and interorganizational learning processes. The proposed model would contribute to an improved implementation of transitioning into the circular economy utilizing sustainable business models in the construction SMEs.

The signal transduction pathways of estrogen receptors (ER) mainly includes gene pathway and non-gene pathway. Studies have shown that the gene pathway of ER is related with the expression of nuclear proteins, and this is the key issue for our current research. With the GEO database analysis, Human centromere protein F (CENPF) is highly expressed in adenocarcinoma of lung (LUAD), and the co-expression of CENPF and ERβ was found in the nucleus of LUAD cells. Meanwhile, CENPF and ERβ2/5 were related with T stage and poor prognosis (P<0.05). Knockdown of CENPF gene significantly inhibited the biological effects of LUAD cells, the tumor growth of mice and the expression of ERβ2/5 (P<0.05). Further, group experiments showed that knockdown CENPF inhibits biological effects of LUAD cells mediated by ERβ pathway. All the results indicated that both CENPF and ERβ2/5 play important roles in the progression of LUAD, and knockdown of CENPF can inhibit the progression of LUAD by inhibiting the expression of ER2/5. Thus, the development of inhibitors against ERβ2/5 subtype and CENPF remained more effective in improving the therapeutic effect of LUAD.

Rice grassy stunt virus (RGSV) a member of Tenuivirus family, is very potent and destructive which effects rice crop in many countries, particularly China. Non coding RNAs have important functions in development and epigenetic regulation of gene expression in numerous organsisms. There is three type of small non coding RNAs have been found in eukaryotes, which are small interferring RNAs (siRNAs), microRNAs (miRNAs) and piwi interacting RNAs (piRNAs). Small RNAs (sRNAs) origination is from the infecting virus which is known as virus-derived small interfering RNAs (vsiRNAs), has responsibility for RNA silencing in plants. Virus-induced gene silencing (VIGS) is mainly dependent on RNA silencing (RNAi). Interestingly, RNA silencing happens in plants during viral infections. RNAi technique showed significant results in Nephotettix cincticeps. RNAi technique demonstrated the gene silencing of planthopper Nilaparvata lugens. The proteins P5, pcf4, Dnj, psn5, and pn6 act as potential movement proteins and serve as silencing suppressors for RGSV. VsiRNAs originate from dsRNA molecules which require Dicer-like (DCL) proteins, RNA dependent RNA polymerase (RdRP) proteins, and Argonaute (AGO) proteins. RdRP uses ssRNA for perfect RNA amplification process and can also be used for DCL dependent secondary vsiRNA formation. VSRs interfere with the movement of signals during silencing mechanism. Moreover, intercellular movement of viruses is facilitated by virus-encoded movement protein. RNAi is found in many eukaryotes which are related to transcriptional or post-transcriptional regulation by gene suppression. Transcription is bidirectional in ssDNA viruses which are originated from dsRNA molecules. In this review, we highlighted the biology of Rice grassy stunt virus and its insect vector and its silencing suppressors. This work will be helpful for plant virologists to understand the whole biogenesis mechanism for rice viruses especially RGSV.

The present study aimed to evaluate the psychometric properties of the Greek version of the Jefferson Scale of Empathy—Student version (JSE-S) and its association with potential predictors among Greek-speaking undergraduate medical students. This study adopted a cross-sectional, comparative–descriptive research design. The study was conducted during October and November 2023. Cronbach’s α for the JSE-S was 0.846. Cronbach’s α values for the factors “perspective taking”, “compassionate care”, and “standing in the patient’s shoes” were 0.800, 0.715, and 0.748, respectively. The intraclass correlation coefficient for the JSE-S score in the test–retest study was 0.827 (95% CI = 0.647 to 0.915, p-value &lt; 0.001), indicating a high level of reliability. The participants showed moderate empathy levels. Females scored higher than males on the Greek version of the JSE-S (108.8 vs. 102.8, p-value &lt; 0.001). Moreover, students enrolled in the fourth academic year showed higher empathy mean scores than those enrolled in the first year (108.6 vs. 103.6, p-value = 0.012). Statistically significant empathy differences by specialty preferences or faith in God/supreme power were not found. The present study provided satisfactory evidence that the Greek JSE-S is a psychometrically sound measurement instrument. Empathy differences by gender were found in line with prior literature.

Abstract: Although the pathophysiology of fibromyalgia syndrome has been better understood in recent decades, a unified model of its pathogenesis and an effective therapeutic approach are still far from being realized. The main aim of this article will be to delve into the fundamental mechanisms of the pathophysiology of fibromyalgia conceptualized as stress intolerance syndrome. Using the biopsychosocial model of chronic pain syndromes, we will describe the potential role of the attachment system, C tactile fibers, and oxytocinergic system dysfunction in the pathophysiology of fibromyalgia syndrome and other central sensitivity syndromes. At the end of the article, the therapeutic implications of this new global and translational pathophysiological model will be briefly discussed.

Alzheimer’s Disease (AD) has no cure. Earlier, we showed that partial inhibition of mitochondrial complex I (MCI) with small molecule CP2 induces adaptive stress response activating multiple neuroprotective mechanisms. Chronic treatment reduced inflammation, improved synaptic and mitochondrial functions, and blocked neurodegeneration in symptomatic APP/PS1 mice, a translational model of AD. Here, using serial block-face scanning electron microscopy (SBFSEM) and three-dimensional (3D) EM reconstructions combined with Western blot analysis and next-generation RNA sequencing, we demonstrate that CP2 treatment also restores mitochondrial morphology and mitochondria-endoplasmic reticulum (ER) communication in the APP/PS1 mouse brain. Using 3D EM volume reconstructions, we show that mitochondria in AD dendrites exist primarily as mitochondria-on-a-string (MOAS). Compared to other morphological phenotypes, MOAS are extensively enveloped in the ER membranes forming multiple mitochondria-ER contact sites (MERCS) known to contribute to abnormal lipid and calcium homeostasis. CP2 treatment specifically reduced MOAS formation, consistent with improved energy homeostasis in the brain, with concomitant reduction in MERCS, ER stress, and improved lipid homeostasis. These data provide novel information on the role MOAS play in AD and additional support for further development of partial MCI inhibitors as disease modifying strategy for AD.

With the rise of long-read sequencers and long-range technologies, delivering high-quality plant genome assemblies is no longer reserved to large consortia. Indeed, sequencing techniques but also computer algorithms have reached a point where the reconstruction of assemblies at the chromosome-scale is now feasible at the laboratory scale. Current technologies, and especially long-range technologies, are numerous and selecting the most promising one for the genome of interest is crucial to obtain optimal results. In this study, we resequenced the genome of the yellow sarson, Brassica rapa cv. Z1, using the Oxford Nanopore PromethION sequencer and assembled the sequenced data using current assemblers. To reconstruct complete chromosomes, we used and compared three long-range techniques, optical mapping, Omni-C and Pore-C sequencing libraries commercialized by Bionano Genomics, Dovetail Genomics and Oxford Nanopore Technologies respectively, or a combination of the three, in order to evaluate the capability of each technology.

This paper aims to demonstrate how the detection of communities for epidemiological networks can be used to reduce the dimensionality of optimal control problems. A range of planted partition stochastic blockmodels are generated, where the underlying communities in the model vary in detectability. A high-performance computing workflow is then used to simulate and estimate reduced model dynamics for the networks, which in turn is used to find optimal control solutions.

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with themselves, their environment, and hosts via metabolites and signaling molecules. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metagenomics methods, and modeling formalisms. Furthermore, the review explains model building, examples of microbiome model applications for prediction, optimization, and control. It concludes with guidelines, software, and repositories for modeling. Each section provides entry-level information, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

Leveraging the open-world understanding capacity of large-scale visual-language pre-trained models has become a hot-spot in point cloud classification. Recent approaches rely on transferable visual-language pre-trained models, classifying point clouds by projecting them into 2D images and evaluating consistency with textual prompts. These methods benefit from the robust open-world understanding capabilities of visual-language pre-trained models and require no additional training. However, they face several challenges summarized as prompt ambiguity, image domain gap, view weights confusion, and feature deviation. In response to these challenges, we propose PointBLIP, a zero-training point cloud classification network based on the recently introduced BLIP-2 visual-language model. PointBLIP is adept at processing similarities between multi-images and multi-prompts. We separately introduce a novel method for point cloud zero-shot and few-shot classification, which involves comparing multiple features to achieve effective classification. Simultaneously, we enhance the input data quality for both the image and text sides of PointBLIP. In point cloud zero-shot classification tasks, we outperform state-of-the-art methods on three benchmark datasets. For few-shot classification tasks, to the best of our knowledge, we present the first zero-training few-shot point cloud method, surpassing previous works under the same conditions and showcasing comparable performance to full-training methods.

Interferometric synthetic aperture radar (InSAR) and tomographic SAR measurement techniques are commonly used for the three-dimensional (3D) reconstruction of complex areas, while the effectiveness of these methods relies on the interferometric coherence among SAR images with minimal angular disparities. Radargrammetry exploits stereo image matching to determine the spatial coordinates of corresponding points in two SAR images, and acquire their 3D properties. The performance of the image matching process directly impacts the quality of the resulting digital surface model (DSM). However, the presence of speckle noise, along with dissimilar geometric and radiometric distortions, poses considerable challenges to achieve accurate stereo SAR image matching. To address the aforementioned challenges, this paper proposes a radargrammetric method based on composite registration of multi-aspect SAR images. The proposed method combines coarse registration using scale invariant feature transform (SIFT) with precise registration using normalized cross-correlation (NCC) to achieve accurate registration between multi-aspect SAR images with large disparities. Furthermore, the multi-aspect 3D point clouds are merged by the proposed radargrammetric 3D imaging method, resulting in 3D imaging of target scenes based on multi-aspect SAR images. For validation purpose, this paper presents a comprehensive 3D reconstruction of the Five-hundred-meter Aperture Spherical radio Telescope (FAST) by using Ka-band airborne SAR images. It does not necessitate prior knowledge of the target and is applicable to the detailed 3D imaging of large-scale areas with complex structures. In comparison to other SAR 3D imaging techniques, it reduces the requirements for orbit control and radar system parameters. To sum up, the proposed 3D imaging method with composite registration guarantees imaging efficiency while enhances the imaging accuracy of crucial areas with limited data.

Traveling salesman problems (TSPs) are well-known combinatorial optimization problems, and most existing algorithms are challenging for solving TSPs when its scale is large. To improve the efficiency of solving large-scale TSPs, this work presents a novel adaptive layered clustering framework with improved genetic algorithm (ALC\_IGA). The primary idea behind ALC\_IGA is to break down a large-scale problem into a series of small-scale problems. First, the $k$-means and improved genetic algorithm are used to segment the large-scale TSPs layer by layer and generate the initial solution. Then, the developed two phases simplified $2$-opt algorithm is applied to further improve the quality of the initial solution. The analysis reveals that the computational complexity of the ALC\_IGA is between $O(n\log n)$ and $O(n^2)$. The results of numerical experiments on various TSP instances indicate that, in most situations, the ALC\_IGA surpasses the state-of-the-art algorithms in convergence speed, stability, and solution quality. Specifically, the ALC\_IGA can solve instances with $2 \times 10^5$ nodes within 0.15h, $1.4 \times 10^6$ nodes within 1h, and $2 \times 10^6$ nodes in three dimensions within 1.5h.

Bonding agents (BA) are key compounding ingredients for the correct formulation of composite solid rocket propellants (CSRP). In particular, the addition of BA is essential to achieve suitable mechanical properties of CSRP in terms of adequate tensile strength and elongation at break. It is shown that the polarity of each conventional BA as well as new potential BA can be measured through the Reichardt’s ET(30) polarity scale. Using this criteria it was possible to find a substitute for MAPO (tris-(methylaziridinyl)-phosphine oxide), a conventional BA with the drawback of high toxicity and high reactivity, with TTPT (tris-(pyrrolidine)-phosphine oxide), a completely safe and effective BA. In this work, many other potential BA were evaluated through the Reichardt’s ET(30) polarity scale but only a selection of the potential BA were effectively tested in a standard CSRP. Particular interesting was the evaluation of TTPT vs MAPO showing the ability of the former BA to match the mechanical properties of the latter BA.

A multiscale mathematical model is proposed seeking to study the propagation dynamics of the Human Immunodeficiency Virus (HIV) in a group of young people between 15 and 24 years of age, through sexual contact without protection, considering the use of antiretroviral therapy (ART) and therapeutic failure. The model consists in a scale-free complex network that follows a power law, coupled with the immunological dynamics of each individual, that is, it considers the infection by the virus in the immune system of each HIV carrier, through a system of non-linear differential equations that govern the infection’s behavior in the immune system. Propagation of the virus in the network is modelled by taking into account information from the immunological status of each person. The study found that for a population to have high HIV prevalence, it is not necessary at the beginning of the simulation time for the virus to propagate rapidly. In addition, the study proves that with a higher number of sexual partners, there will be greater prevalence of HIV in the population and that the use of ART helps to control the propagation of the infection in the population. As an interesting result, it was also found that there is a higher number of HIV carriers who abandon ART than those who have access to it.

Full-scale/model test comparison studies to validate the traditional ABL wind tunnel simulation technique are reviewed. According to the literature review, notable discrepancies between full-scale measurement results and model test results were observed by most performed comparison studies, but the causes of the observed discrepancies were not revealed in a scientific way by those studies. In this regard, a new research scheme for future full-scale/model test comparison studies is proposed in this article, which utilizes the multiple-fan actively controlled wind tunnel simulation technique. With the new research scheme, future full-scale/model test comparison studies are expected to reasonably disclose the main problems with the traditional ABL wind tunnel simulation technique, and the technique can be improved correspondingly.

“White matter disease” identifies series of different conditions and pathological mechanisms: autoimmune, infectious, toxic-metabolic and vascular. Each of these leads to a global impairment of the neural myelination process through the secondary destruction of previously myelinated structures. To date, the imaging spectrum represents an irreplaceable tool to detect these lesions, describe their distribution patterns and stage them over time. This study aims presents a pictorial review of white matter disease, from pathology to imaging spectrum, reporting current main staging systems with greater emphasis to relationship between cerebrovascular disease and white matter hyper-intensity appearance, and the newest advances in this field.

This research aimed to assess the influence of red dragon fruit peels ratio (RDF-PR) from two species, Hylocereus hybridum (HH) and Hylocereus undatus (HU), and particle size (PS) on quality parameters of red dragon fruit peel powder (RDF-PP), and its further application in emulsified alpaca-based sausages as partial substitutes of pork-back fat. A three-level full factorial design (nine treatments) was employed to evaluate the effect of: (i) RDF-PR [HH(0%):HU(100%), HH(50%):HU(50%), and HH(100%):HU(0%)] and (ii) PS (499–297, 296–177, and &lt;177 µm) on the dependent variables: L*, a*, b*, C, h°, water-holding capacity, oil-holding capacity, swelling capacity, pectin yield, degree of esterification (analysed through FT-IR), and crude fibre content. The data analysed through a response surface methodology showed that treatment one (T1) is the best with the optimised conditions at 100% HU RDF-PR and PS of &lt;177 µm. The statistical validation of T1 exhibited the highest water holding capacity (32.1 g H2O/g peel), oil holding capacity (2.20 g oil/g peel), and pectin yield (27.1%). A completely randomised design (four formulations) was then used to assess the effect of partial replacement of pork-back fat by T1 in emulsified alpaca-based sausages. The results revealed that 65.7% of pork-back fat content was successfully replaced as compared with a control formulation.

Craters are the most prominent geomorphological features on the surface of celestial bodies, which is playing a crucial role in studying the formation and evolution of celestial bodies, as well as in landing and planning for surface exploration. Currently, the main automatic crater detection models and datasets focus on the detection of large and medium craters. In this paper, we created 23 small lunar crater datasets for model training based on the Chang’E-2 (CE-2) DOM, DEM, Slope, and integrated data with 7 kinds of visualization stretching methods. And then, we proposed the YOLO-Crater model for Lunar and Martian small crater detection by replacing EioU and VariFocal loss to solve the crater samples imbalance problem and introducing a CBAM attention mechanism to mitigate interference from the complex extraterrestrial environment. The results show that the accuracy (P = 87.86%, R = 66.04%, and F1 = 75.41%) of the Lunar YOLO-Crater model based on the DOM-MMS (Maximum-Minimum Stretching) dataset is the highest and better than that of YOLOX model. And the Martian YOLO-Crater, trained by the Martian dataset from the 2022 GeoAI Martian Challenge, achieves good performance with P = 88.37%, R = 69.25%, and F1 = 77.65%. It indicates that the YOLO-Crater model has a strong transferability and generalization capability, which can be applied to detect small craters on the Moon and other celestial bodies.

Dynamic contrast-enhanced MRI (DCE-MRI) is increasingly used to quantify and map the spatial distribution of blood-brain barrier (BBB) leakage in neurodegenerative disease, including cerebral small vessel disease and dementia. However, the subtle nature of leakage and resulting small signal changes make quantification challenging. While simplified one-dimensional simulations have probed the impact of noise, scanner drift, and model assumptions, the impact of spatio-temporal effects such as gross motion, k-space sampling and motion artefacts on parametric leakage maps has been overlooked. Moreover, evidence on which to base the design of imaging protocols is lacking due to practical difficulties and the lack of a reference method. To address these problems, we present an open-source computational model of the DCE-MRI acquisition process for generating four-dimensional Digital Reference Objects (DROs), using a high-resolution brain atlas and incorporating realistic patient motion, extra-cerebral signals, noise and k-space sampling. Simulations using the DROs demonstrated a dominant influence of spatio-temporal effects on both the visual appearance of parameter maps and on measured tissue leakage rates. The computational model permits a greater understanding of the sensitivity and limitations of subtle BBB leakage measurement and provides a non-invasive means of testing and optimising imaging protocols for future studies.

The reported study was devoted to the investigation of viscoelastic behavior for solid and porous ultra-high-molecular-weight polyethylene (UHMWPE) under compression. The obtained experimental stress curves were interpreted using a two-term Prony series to represent the superposition of two coexisting activation processes corresponding to long molecular (~160 s) and short structural (~20 s) time scales, respectively, leading to good statistical correlation with the observations. In the case of porous polymer, the internal strain redistribution during relaxation was quantified using Digital Image Correlation (DIC) analysis. The strongly inhomogeneous deformation of the porous polymer was found not to affect the relaxation times. In order to generalize the results to three dimensions, X-ray tomography was used to examine the porous structure at the macro- and micro-scale levels. DIC analysis revealed positive correlation between the applied force and relative density. The apparent stiffness variation for UHMWPE foams with mixed open and closed cells was described using a newly proposed three-term expression. Furthermore, the in situ tensile loading and X-ray scattering study was applied for isotropic solid UHMWPE specimens to investigate their parameters of internal structure during orientation and stress relaxation process at another mode.

Sanitation, water supply and their governance remain major challenges in many Pacific Island Countries. National sustainable development strategies (NSDSs) are promoted throughout the Pacific as overarching improved governance instruments to identify priorities, plan solutions and fulfill commitments to sustainable development. Their relevance to local village-level development priorities is uncertain. The Kingdom of Tonga provides opportunities to compare both. Tonga’s Strategic Development Frameworks (TSDFI 2011-2014 and TSDFII 2015-2025) were developed to focus government and its agencies on national outcomes. From 2007 to 2016, 136 villages throughout Tonga’s five Island Divisions (IDs) formulated Community Development Plans (CDPs) involving separately 80% of women, youth and men in each village. Censuses in 2006 and 2016 show linked improvements in water supply and sanitation systems but reveal IDs with continuing challenges. Sanitation and water are prominent in TSDFI but absent from the current TSDFII. In contrast, CDPs show in one ID, 53% of villages ranked sanitation as a priority with marked differences between IDs and between women, youth and men. CDPs’ sanitation priorities in IDs mostly correspond to sanitation and water metrics in the Censuses, but some reflect impacts of natural disasters. Explanations for differences in sanitation priorities between national and local development plans, as well as suggestions for improving NSDS processes in island countries, are advanced.

The antimicrobial properties of the new strains of microorganisms isolated from natural sources of various ecological niches in the Moscow Region and the Republic of Tatarstan were studied. An antifungal activity of isolates was established in a test culture of toxin-producing microscopic fungi that cause various plant diseases: Fusarium oxysporum, Aspergillus flavus, Penicillium spp. and Candida albicans. Of the 46 studied microorganisms, 4 isolates (Bacillus subtilis, Propionibacterium freudenreichii, Lactobacillus plantarum and Streptomyces spp.) showed an ability to produce biologically active metabolites with a pronounced antimicrobial potential. This is promising in the growth and protection of the Scots pine, pedunculate oak and small-leaved linden forest crops from diseases caused by fungi Lophodermium pinastri Chev. and Microsphaera alphitoides stimulation. Based on selected isolates, a Bacterial product LRV composition has been developed. A Scots pine, pedunculate oak and small-leaved linden seedlings single and double foliar treatment with Bacterial product LRV at a concentration of 10 ml/l led to an increase in the growth of an above-ground part by 31.8, 51.9 and 25.4 %, respectively, and an underground part – by 25.0, 37.2 and 25.7 %, respectively, compared with the control. The weight of seedling at the end of the study exceeded the control variant by an average of 26.0, 44.0 and 78.0 %, respectively. A use of a plant protection Bacterial product LRV did not have a significant effect on a powdery mildew and Schütte disease damage to forest crops. A Biogeosystem Technique methodology has been developed to improve a long-term forest growing.

Sign Language Recognition (SLR) aims to bridge speech-impaired and general communities by recognizing signs from given videos. Researchers still face challenges developing efficient SLR systems because of the video’s complex background, light illumination, and subject structures. Recently many researchers developed a skeleton-based sign language recognition system to overcome the subject and background variation of hand gesture signs. However, skeleton-based SLR is still under exploration due to the lack of information and annotations on hand key points. More recently, researchers included body and face information with the hand gesture for the SLR, but their performance and efficiency are unsatisfactory. We proposed a Multi-Stream Graph-based Deep Neural Network (SL-GDN) for a skeleton-based SLR system to overcome the problems. The main purpose of the proposed SL-GDN approach is to improve the efficiency and performance of the SLR system with a low computational cost based on the human body pose in the form of 2D landmark locations. In the procedure, firstly, we constructed a skeleton graph based on the selected 27 whole-body key points among 67 key points to solve the inefficiency problems. Then we proposed multi-stream SL-GDN to extract features from the whole-body skeleton graph for four streams. Finally, we concatenated the four different features and applied a classification module to refine the feature and recognize corresponding sign classes. Our data-driven and graph construction method increases the system’s flexibility and brings high generability to adapt various data samples. We used three large-scale benchmark SLR datasets to evaluate the proposed model: WLASL, AUTSL and CSL. The demonstrated performance accuracy table proved the superiority of the proposed model, and we believe this will be considered a great invention in the SLR domain.

There exists an implicit potential limitation in every physical discoveries that has been implemented and understood. However, the limitations can be bounded within a safe limit to prevent any constructing theory to be free from errors. As, it’s the inert nature of the humans, to go far beyond the scope of experimental findings in order to pursue any studies with the sole help of logical reasoning and mathematics, the argument can be prevailed in the form of WEAK Clampdown Effect & STRONG Clampdown Effect. More, the theories are constructed out of physical nature, more the theory gets hypothetical without any finding evidence, but that does or doesn’t actually justify the phenomenon, that too with the more increment of KARDASHEV Scale, more moderate ways of experimentation got developed curbing down the limitations within the human limit of ‘ERRORS’, that does can be neglected by approximation. Relationship being cross-judgmental on the basis of the computational limits and calculation accuracy, leading to a soft singularity, as a warning, that if computer powers cannot be checked on the basis of error approximations, then this may lead to the hitting of a hard singularity, that in phase with the forbidden gap (or after the optimum limit that arises at the core constraints of nature) to prevent the computation being carried off with respect to super-intelligence machines that are cognitive capability oriented future computers responsible for self growth & reproduction with more improvement algorithm, restricting all forms of humanity & constraints the human growth by virtue of limiting capacities of the humans as compared to computers.

Despite numerous studies in statistical downscaling methodology, there remains a lack of methods that can downscale from precipitation modeled in global climate models to regional level high resolution gridded precipitation. This paper reports a novel downscaling method using a Generative Adversarial Network (GAN), CliGAN, which can downscale large-scale annual maximum precipitation given by simulation of multiple atmosphere-ocean global climate models (AOGCM) from Coupled Model Inter-comparison Project 6 (CMIP6) to regional-level gridded annual maximum precipitation data. This framework utilizes a convolution encoder-dense decoder network to create a generative network and a similar network to create a critic network. The model is trained using an adversarial training approach. The critic uses the Wasserstein distance loss function and the generator is trained using a combination of adversarial loss Wasserstein distance, structural loss with the multi-scale structural similarity index (MSSIM), and content loss with the Nash-Sutcliff Model Efficiency (NS). The MSSIM index allowed us to gain insight into the model’s regional characteristics and shows that relying exclusively on point-based error functions, widely used in statistical downscaling, may not be enough to reliably simulate regional precipitation characteristics. Further use of structural loss functions within CNN-based downscaling methods may lead to higher quality downscaled climate model products.

Recently, the relationship between emotional arousal and depression has been studied. Focusing on this relationship, we first developed an arousal level voice index (ALVI) to measure arousal levels using the Interactive Emotional Dyadic Motion Capture database. Then, we calculated ALVI from the voices of depressed patients from two hospitals (Ginza Taimei Clinic [GTC] and National Defense Medical College hospital [NDMC]) and compared them with the severity of depression as measured by the Hamilton Rating Scale for Depression (HAM-D). Depending on the HAM-D score, the datasets were classified into a no depression (HAM-D&lt;8) and a depression group (HAM-D≥8) for each hospital. A comparison of the mean ALVI between the groups was performed using the Wilcoxon rank-sum test and a significant difference at the level of 10% (p = 0.094) at GTC and 1% (p = 0.0038) at NDMC was determined. The area under the curve (AUC) of the receiver operating characteristic was 0.66 when categorizing between the two groups for GTC, and the AUC for NDMC was 0.70. The relationship between arousal level and depression severity was indirectly suggested via ALVI.

With reference to Planck scale, Mach's relation and by introducing two new parameters Gamma and Beta, right from the beginning of Planck scale, we make an attempt to estimate ordinary matter density ratio, dark matter density ratio, mass, radius, temperature, age and expansion velocity (from and about the Planck mass in all directions). In analogy with currently believed cosmic acceleration, with a decreasing trend of total matter density ratio, cosmic expansion velocity can be shown to be increasing. By considering $H_0\cong 70$ km/sec/Mpc, estimated current cosmic mass, radius, total matter density, expansion velocity, temperature and age are: $4.3352\times {10}^{53}\mathrm{kg},$ $3.207\times {10}^{26}\mathrm{m},$ $3.138\times {10}^{-27}\mathrm{kg}{.\mathrm{m}}^{-3},$ $2.43c,$ $2.721\mathrm{K}$ and $19.78\mathrm{Billion}\mathrm{years}$ respectively. Point to be noted is that, with reference to Planck scale, cosmic temperature seems to be redshifted by a factor ${\left(Z_T\right)}_t\cong \sqrt{1+\ln \left(H_{pl}/H_t\right)}-1$ where $\left(H_{pl},H_t\right)$ represent Planck scale and time dependent Hubble parameters respectively. As a peculiar case, considering the equality of current Hubble parameter and current angular velocity, current cosmic rotational kinetic energy can be estimated to be 0.667 times the current critical energy. It needs further study.

Light and heat resources are ample in Xinjiang plain oasis region. Planting structure is single in irrigation area, which leads to severe seasonal water shortages due to high concentration of water utilization. Therefore, how to make full use of light and heat resources to develop oasis agriculture without expanding planting scale has become the focus of research. The biannual cropping patterns are possible solutions to the problem. Manas River Irrigation area, which is located on the northern slope of Tianshan Mountain in Xinjiang in China is regarded as a typical case study to analyze the suitable planting structure and planting scale for biannual cropping pattern. The effects of optimizing planting patterns on alleviating the contradiction between water supply and water need，and the efficiency of water resources utilization was also studied. The results of the study show that：(1) the suitable planting scales of Manas River irrigation area in wet year (P = 25%), normal year (P = 50%), and low flow year (P = 75%) are about 72.66%, 78.73% and 83.91% of the current planting scale. (2) After the planting structure was optimized, the water use process in the irrigation area extends from May to August at present to April to November. The water use ratio at the peak period of water use decreased from 0.847 to 0.601, and the water use peak was significantly dispersed. (3) Economic benefits per cubic metre of water of the biannual cropping patterns increased from 8.65 yuan·m⁻³ at present to 9.45 yuan·m⁻³, 10.31 yuan·m⁻³ and 11.43 yuan·m⁻³ in wet year (P = 25%), in normal year (P = 50%) and low flow year (P = 75%) respectively, which proved that replanting crops could improve the efficiency of water resources utilization in irrigated areas. Therefore, the optimal planting pattern can be implemented to improve the utilization efficiency of soil and water resources in irrigation area in the arid oasis with single planting pattern with excessive concentration of water use peak and serious water shortage.

Insufficient mixing in large-scale bioreactors, provokes gradient zones of substrate, dissolved oxygen, pH and other parameters. E. coli responds to a high glucose, low oxygen feeding zone with the accumulation of mixed acid fermentation products, especially formate, but also with the synthesis of non-canonical amino acids, such as norvaline, norleucine and -methyl-norleucine. These amino acids can be mis-incorporated into recombinant products, which causes a problem for pharmaceutical production whose solution is not trivial. While these effects can also be observed in scale down bioreactor systems, these are challenging to operate. Especially the high-throughput screening of clone libraries is not easy, as fed-batch cultivations would need to be controlled via repeated glucose pulses with simultaneous oxygen limitation, as has been demonstrated in well controlled robotic systems. Here we show that not only glucose pulses in combination with oxygen limitation can provoke the synthesis of these non-canonical branched-chain amino acids, but also that pyruvate pulses produce the same effect. Therefore we combined the enzyme based glucose delivery method Enbase® in a PALL24 mini-bioreactor system and combined repeated pyruvate pulses with simultaneous reduction of the aeration rate. These cultivation conditions, produced an increase in the non-canonical branched chain amino acids norvaline and norleucine in both the intracellular soluble protein and inclusion body fractions with mini-proinsulin as an example product, and this effect was verified in a 15 L stirred tank bioreactor. To our opinion this cultivation strategy is easy to apply for the screening of strain libraries under standard laboratory conditions if no complex robotic and well controlled parallel cultivation devices are available.

Credit rationing, especially prevalent for smaller firms, impedes economic growth. A Central Bank aligned “not for profit” managed business to business “Stable-Coin” (“Synthetic Central Bank Digital Currency”) providing trade debt liquidity can lower small firm credit rationing. It raises growth by reducing monetary transmission imperfections consequent upon asymmetric information, commercial bank underwriting restrictions, market power dynamics and regulatory distortion. A simple framework is developed to contextualise small firm credit rationing and associated monetary transmission imperfections with broader credit flows into both the real and monetary sectors. Evidence is presented regarding US monetary transmission efficacy to firms, paving the way to proposing a trade debt business to business focused stable-coin to help close credit rationing gaps. Designed to minimise “moral hazard”, the proposed stable-coin provides an additional monetary transmission channel that supplies interest free trade debt funding to firms to support economic activity and raise growth. In addition to helping close the small firm financing gap, the stable-coin provides policy makers with an additional contra-cyclical monetary transmission instrument to minimise real economic disruption consequent upon financial system crises and liquidity events.

This paper presents an optimal design for the inner current control loop of the continuous current conduction mode (CCM) power factor correction (PFC) stage, which it can be used as the front stage of the two stages alternating current-direct current (AC-DC) telecom power supply. Conventional single-phase CCM-PFC boost converter usually implemented with using of the proportional-integral (PI) controllers in both of the voltage and current control loops, to regulate the output DC voltage to the specified value, moreover to maintains the input current follows the input voltage which offers converter with high power factor (P.F) and low current total harmonic distortion (THD). However, due to the slow dynamic response of the PI controller at the zero-crossing point of the input supply current, input current can’t fully follow the input voltage which leads to high THD. Digitally controlled PFC converter with an optimal design of the inner current control loop using doubly control loops IP controller to reduce the THD and to offer input current with unity P.F was performed in this paper. Furthermore, for the economic design of the digitally control PFC converter, two isolated AC and DC voltage sensors are proposed and designed for the interfacing with the microcontroller unit (MCU). PSIM software was used to test the converter performance with using the proposed designed current controllers and isolated voltage sensors. High power density digitally controlled telecom PFC stage with P.F of about 99.93%, full load efficiency of about 98.70% and THD less 5.50% is achieved in this work.

Electrical properties of the thin films of poly(arylene ether ketone) copolymers (co-PAEKs) with the fraction of phthalide-containing units of 3, 5 and 50 mol% in the main chain were investigated by using radiation induced conductivity (RIC) measurements. Transient current signals and current-voltage (I-V) characteristics were obtained under exposing 20÷25 thick films of the co-PAEKs to monoenergetic electron pulses of the energy ranged from 3 to 50 keV in the electric field ranged from 5 to 40 V/μm. The Rose-Fowler-Vaisberg semi-empirical model based on a multiple trapping formalism was used for an analysis of the RIC data and the parameters of the highly dispersive charge carrier transport were evaluated. The analysis revealed that charge carriers moved in isolation from each other and the applied electric fields were below the threshold field triggering the switching effect (a reversible high-to-low resistivity transition) in the co-PAEK films. It was also found that the co-PAEK films due to the super-linear I-V characteristics are highly resistant to electrostatic discharges arising from the effects of ionizing radiation. This property is important for the development of protective coatings for electronic devices.

This study demonstrates a global, non-parametric, non-iterative optimization of time-mean value of a kind of index vibrated by time-varying forcing. It is based on the fact that the (steady) forced vibration of non-autonomous ordinary differential equation systems is well approximated by an analytical solution when the amplitude of forcing is sufficiently small and its base state without forcing is linearly stable and steady. It is applied to optimize a time-averaged heat-transfer rate on a two-dimensional thermal convection field in a square cavity with horizontal temperature difference, and the globally optimal way of vibrational forcing, i.e. the globally optimal, spatial distribution of vibrational heat and vorticity sources, is first obtained. The maximized vibrational thermal convection corresponds well to the state of internal gravity wave resonance. In contrast, the minimized thermal convection is weak, keeping the boundary layers on both sidewalls thick.

Compressive residual stresses in thin films can inhibit crack propagation under normal or sliding contact loading, with associated enhancement of the coating apparent toughness, load bearing capacity and wear resistance. This study investigates the influence of residual stress distributions on the thin film/substrate adhesion using a nanoindenter coupled with scanning electron microscope (SEM) investigations of indentation induced failure modes. Reactive and un-reactive magnetron sputtering with ion plating was used to coat a (100) silicon substrate with aluminum nitride (AlN) with and without an aluminum (Al) adhesion layer. The presence of an Al bond layer gives additional interfacial tensile stress because of the difference in thermal expansion coefficient. Additionally, a different magnitude of residual stresses in the AlN coating was achieved by changing the applied bias voltage onto the substrate. Wafer curvature method and incremental focused ion beam (FIB) milling, combined with high-resolution in situ scanning electron microscopy (SEM) imaging and full field strain analysis by digital image correlation (DIC), were used to measure the average and in-depth stress residual stress distribution in the produced coatings. The adhesion energy was then quantified by using a nanoindentation based model. Results demonstrate that the additional tensile residual stress in the aluminum adhesion layer decrease significantly the coating adhesion, even in presence of a higher compressive stress state in the AlN top-layer. Therefore, the coatings without Al-layer showed better adhesion because of a more homogeneous compressive residual stress in comparison with the coating having Al layer, even though both groups of coatings are produced under same bias voltage. Results are discussed, and some general suggestions are made on the correlation between coating/substrate property combination and the adhesion energy of multilayer stacks. The results suggested that Al bond-layer and inhomogeneous residual stresses affected the adhesion of AlN negatively to a substrate like silicon.

Keywords: bariatric surgery, sleeve gastrectomy, Roux-en-y gastric bypass, obesity, microbiota, gut-brain axis, beck scale, depression, diet, probiotics

Cancer cells are adept at reprogramming energy metabolism and the precise manifestation of this metabolic reprogramming exhibits heterogeneity across individuals (and from cell to cell). In this study, we analyzed the metabolic differences between interpersonal heterogeneous cancer phenotypes. We used divergence analysis on gene expression data of 1156 breast normal and tumor samples from The Cancer Genome Atlas (TCGA) and integrated this information with a genome-scale reconstruction of human metabolism to generate personalized, context-specific metabolic networks. Using this approach, we classified the samples into four distinct groups based on their metabolic profiles. Enrichment analysis of the subsystems indicated that amino acid metabolism, fatty acid oxidation, citric acid cycle, androgen and estrogen metabolism and ROS detoxification distinguished these four groups. Additionally, we developed a workflow to identify potential drugs that can selectively target genes associated with the reactions of interest. MG-132 (a proteasome inhibitor) and OSU-03012 (a celecoxib derivative) were the top-ranking drugs identified from our analysis and known to have anti-tumor activity. Our approach has the potential to provide mechanistic insights into cancer-specific metabolic dependencies, ultimately enabling the identification of potential drug targets for each patient independently, contributing to a rational personalized medicine approach.

Many complex behaviours in biological systems emerge from large populations of interacting molecules or cells, generating functions that go beyond the capabilities of the individual parts. Such collective phenomena are of great interest to bioengineers due to their robustness and scalability. However, engineering emergent collective functions is difficult because they arise as a consequence of complex multi-level feedback, which often spans multiple length-scales. Here, we present a perspective on how some of these challenges could be overcome by using multi-agent modelling as a design framework within synthetic biology. Using case studies covering the construction of synthetic ecologies to biological computation and synthetic cellularity, we show how multi-agent modelling can capture the core features of complex multi-scale systems and provide novel insights into the underlying mechanisms which guide emergent functionalities across scales. The ability to unravel design rules underpinning these behaviours offers a means to take synthetic biology beyond single molecules or cells and towards the creation of systems with functions that can only emerge from collectives at multiple scales.

This study provides designs for a low-cost, easily replicable open source lab-grade digital scale that can be used as a precision balance. The design is such that it can be manufactured for use in most labs throughout the world with open source RepRap-class material extrusion-based 3-D printers for the mechanical components and readily available open source electronics including the Arduino Nano. Several versions of the design were fabricated and tested for precision and accuracy for a range of load cells. The results showed the open source scale was found to be repeatable within 0.1g with multiple load cells, with even better precision (0.01g) depending on load cell range and style. The scale tracks linearly with proprietary lab-grade scales, meeting the performance specified in the load cell data sheets, indicating that it is accurate across the range of the load cell installed. The smallest loadcell tested(100g) offers precision on the order of a commercial digital mass balance. The scale can be produced at significant cost savings compared to scales of comparable range and precision when serial capability is present. The cost savings increase significantly as the range of the scale increases and are particularly well-suited for resource-constrained medical and scientific facilities.

In the Netherlands, the assessment of existing prestressed concrete slab-between-girder bridges showed that the thin, transversely prestressed slabs may be critical for static and fatigue punching when evaluated using the recently introduced Eurocodes. On the other hand, compressive membrane action increases the capacity of these slabs and changes the failure mode from bending to punching shear. To improve the assessment of the existing prestressed slab-between-girder bridges in the Netherlands, two 1:2 scale models of an existing bridge, the Van Brienenoord Bridge, were built in the laboratory and tested monotonically as well as under cycles of loading. The result of these experiments is: 1) the static strength of the decks, showing that compressive membrane action significantly enhances the punching capacity, and 2) the Wöhler curve of the decks, showing that compressive membrane action remains under fatigue loading. The experimental results can then be used for the assessment of the most critical existing slab-between-girder bridge. The outcome is that the bridge has sufficient punching capacity for static and fatigue loads, and thus that the existing slab-between-girder bridges in the Netherlands fulfil the code requirements for static and fatigue punching.

The DNS of film boiling requires strong computational resources that are difficult to obtain for a daily CFD use by the practitioners of the industrial R&amp;D experts. On the other hand, the film boiling experiments are associated with the usage of the expensive and highly sophisticated apparatus, and the research to this end is found to be relatively difficult due to high heat flow rates that are present in the process itself. When combined with a transient heat conduction in a solid, the problem becomes significantly difficult. Therefore, a novel method in computation of conjugate heat transfer during film boiling in a quiescent liquid has been proposed in this paper. The method relies on the solution of mass, momentum and energy conservation equations in a two-fluid framework, supplemented with the appropriate closures. Furthermore, the turbulent flow has been found as an important parameter in obtaining the accurate solution of the temperature field evolution in a solid specimen, via the proper modeling of turbulent kinetic energy (TKE) value, that has been imposed as a constant value, i.e., the frozen turbulence approach. It was found, in addition, that the appropriate TKE value can be obtained by use of Kelvin-Helmholtz instability theory in conjunction with the boundary layer theory. The obtained results show excellent agreement with the experimental data within the first 15 s of the experiment, i.e., the first ca. 10 % of the total duration of the film boiling mode of heat transfer. Furthermore, the heat transfer coefficient has matched the error bands prescribed by the authors of the paper that has presented the correlations, whilst the averaged values are far beyond this band, i.e., are slightly more than 30 % higher. The further inspection has revealed a measure of similarity between the computational result of the volume fraction field distribution and the experiment, thus confirming the capability of the method to obtain realistic interface evolution in time. The method has shown full capability for further pursuing the industrial-scale film boiling problems that involve turbulent flow and the conjugate heat transfer approach.

Mechanization is an important factor to improve the green total factor productivity of planting industry, which is the key way to realize the sustainable development and high-quality development of agriculture. Using the panel data of 30 provinces in China from 2001 to 2019, this paper uses the stochastic frontier analysis method of output oriented distance function to measure the green total factor productivity of planting industry based on net carbon sink, and empirically studies the impact of mechanization on the planting green total factor productivity. The empirical analysis finds that mechanization can significantly promote the planting green total factor productivity, and this basic conclusion is still robust after using instrumental variables, sub sample regression. Further research found that the path of mechanization on planting green total factor productivity is mainly reflected in technology progress and spatial spillover. The mechanism of operation scale expansion, factor allocation optimization and technical efficiency change is not significant. Given these findings, the paper adds considerable value to the empirical literature and also provides various policy- and practical implications.

Due to its spatio-temporal variability, the mismatch between the weather and demand patterns challenges the design of highly renewable energy systems. A principal component analysis is applied to a simplified networked European electricity system with a high share of wind and solar power generation. It reveals a small number of important mismatch patterns, which explain most of the system's required backup and transmission infrastructure. Whereas the first principal component is already able to reproduce most of the temporal mismatch variability for a solar dominated system, a few more principal components are needed for a wind dominated system. Due to its monopole structure the first principal component causes most of the system's backup infrastructure. The next few principal components have a dipole structure and dominate the transmission infrastructure of the renewable electricity network.

Currently, Korea is experiencing localized extreme rainfall, which accounts for more than 80% of flood-related disasters, and is increasing in small river basins, where more than 60% of flood-related casualties occur. These events are caused by climate change and geological factors and their impact is becoming more severe. As a result, an effective measurement system is required to mitigate their impact, particularly in small stream basins that are especially vulnerable due to their steep slopes, small catchment areas, and lack of maintenance and management capacity. In addition, a Flood Early Warning Framework (FEWF) that forecasts discharge and depth during flood events is crucial for reducing casualties. Therefore, this research is focused on developing the FEWF using the nomograph and rating curve methods, which are established by the robust constrained nonlinear equation solver and are suitable for small streams. The FEWF is evaluated using real-time data observed over 7-years period from the Closed-circuit Television-based Automatic Discharge Measurement Technology (CADMT), and the results show that the FEWF is effective in forecasting discharge and depth during flood events. The use of CADMT technology for real-time data can develop an accurate and reliable FEWF, which can help mitigate the impacts of extreme rainfall events and reduce the number of flood-related casualties in small stream basins.

This paper aims to provide the smart grid research community with an open and accessible general mathematical framework to develop and implement optimal flexibility mechanisms in large-scale network applications. The motivation of this paper is twofold. On the one hand, flexibility mechanisms are currently a hot topic of research, which is aimed to mitigate variation and uncertainty of electricity demand and supply in decentralised grids with a high aggregated share of renewables. On the other hand, a large part of such related research is performed by heuristic methods, which are generally inefficient (such methods do not guarantee optimality) and difficult to extrapolate for different use cases. Alternatively, this paper presents an MPC-based (Model Predictive Control) framework explicitly including a generic flexibility mechanism which is easy to particularise to specific strategies such as Demand Response, Flexible Production and Energy Efficiency Services. The proposed framework is benchmarked with other non-optimal control configurations to better show the advantages it provides. The work of this paper is completed by the implementation of a generic use case which aims to further clarify the use of the framework and thus, to ease its adoption by other researchers in their specific flexibility mechanisms applications.

Crapemyrtle (Lagerstroemia indica L.: Lythraceae) is the most popular summer flowering tree in the U.S. Its total value sold has almost doubled since 1998. Consumers prize crapemyrtles for their beauty and pest resistance. However, current crapemyrtle production and use is being threatened by crapemyrtle bark scale (Acanthococcus lagerstroemiae (Kuwana, 1907)) (CMBS), which has been confirmed in 12 U.S. states after its first sighting in Texas in 2004. Our survey results indicate that producers anticipate a significant decrease in the value of crapemyrtle due to CMBS, in the magnitude of 29.93% and 33.79%, in our 2018 and 2019 surveys respectively. Our findings indicate industry demand for CMBS control. We used a non-parametric test to compare the producers’ responses to several questions regarding CMBS-control, among the different producer categories included in our sample. Incorporated businesses showed the most support, followed by part-nerships, and family/individual operations were the least supportive of science-based CMBS control research. Large businesses predicted a more serious decrease in crapemyrtles’ value as compared to smaller businesses. More businesses with high volume of crapemyrtle-related business considered the benefits of CMBS-control to be higher than its cost, as compared to other businesses. We also used a relative importance index to illustrate the ranking of different attrib-utes of crapemyrtles that producers consider while making decisions about growing/purchasing the plants. Flower color was found to be the most important attribute, followed by disease re-sistance. If the issue of CMBS gets out of control, the industry might need to find potential re-placements to crapemyrtle. The most popular landscape plants that can potentially replace crapemyrtle, in the opinion of producers we surveyed, are vitex (Texas lilac) and magnolia.

Peripheral and autonomic neuropathy are common disease manifestations in systemic amyloidosis. Neurofilament light chain (NfL), a neuron-specific biomarker, is released into the blood and cerebrospinal fluid after neuronal damage. This systematic review provides an overview on the value of NfL in early detection of neuropathy, central nervous system involvement, monitoring of neuropathy progression, and treatment effect in systemic amyloidosis. A literature search in PubMed, Embase and Web of Science was performed on 14-02-2024 for studies investigating NfL levels in patients with systemic amyloidosis and transthyretin gene variant (TTRv) carriers. Only studies containing original data were included. Included were twelve full-text articles and six abstracts describing 1604 participants: 298 controls and 1306 TTRv carriers or patients with or without polyneuropathy. Patients with polyneuropathy demonstrated higher NfL levels compared to healthy controls and asymptomatic carriers. Disease onset was marked by rising NfL levels. Following initiation of transthyretin gene-silencer treatment, NfL levels decreased and remained stable over an extended period. NfL is not an outcome biomarker, but an early and sensitive disease process biomarker for neuropathy in systemic amyloidosis. Therefore, NfL has potential to be used for early detection of neuropathy, monitoring treatment effect, and monitoring disease progression in patients with systemic amyloidosis.

Recently, the Integrated Air Defense System (IADS) has become vital for the defense system as the military defense system is vital for national security. Placing Integrated Air Defense System batteries among locations to protect locations assets is a crucial problem because optimal solutions are needed for interceptor missiles to intercept attacker missiles for maximizing protection of assets across locations or places. In this research, the procedures of using network algorithms along with developing several network algorithms are going to be demonstrated to develop a model for sequential development of seven network securing strategies of placing Surface to Air Missile (SAM) batteries to maximize the protection of assets across locations (based on given asset values) by generating optimal solutions through computation to destroy maximum attacker missiles by using minimum interceptor missiles with given intercept probability. This network securing strategies can be implemented not only for Integrated Air Defense System (IADS) planning but also Counter Air (CA) planning as Integrated Air Defense System (IADS) is conducted with defensive counter air supported by attack operations in offensive counter air.

This paper proposes and demonstrates, in full scale, a novel type of energy geostructure (“the Climate Road”) that combines a ground source heat pump (GSHP) with a sustainable urban drainage system (SUDS) by utilizing the gravel roadbed simultaneously as energy source and rainwater retarding basin. The Climate Road measures 50m x 8m x 1m (length, width, depth) and has 800 m of geothermal piping embedded in the roadbed, serving as the heat collector for a GSHP that supplies a nearby kindergarten with domestic hot water and space heating. Model analysis of operational data from 2018-2021 indicates sustainable annual heat production levels around 0.6 MWh per meter road, with a COP of 2.9-3.1. The continued infiltration of rainwater to the roadbed increases the amount of extractable heat by an estimated 17% compared to the case of zero infiltration. Using the developed model for scenario analysis we find that draining rainwater from three single family houses and storing 30% of the annual heating consumption in the roadbed, increases the predicted extractable energy by 56% compared to zero infiltration with no seasonal energy storage. The Climate Road is capable of supplying three single family houses with heating, cooling and rainwater management year-round.

This paper introduces a model-free, "on-the-fly" learning control strategy for arrays of energy converters with adjustable generator damping. The devices are arranged so that they are affected simultaneously by the energy medium. Each device uses a different control strategy, of which at least one has to be the machine learning approach presented in this paper. During operation all energy converters record the absorbed power and control output; the machine learning device gets the data from the converter with the highest power absorption and so learns the best performing control strategy for each state. Consequently, the overall network has a better overall performance than each individual strategy. This concept is evaluated for wave energy converter (WEC) with numerical simulations and experiments with physical scale models in a wave tank. In the first of two numerical simulations, the learnable WEC works in an array with four WECs applying a constant damping factor. In the second simulation, two learnable WECs were learning with each other. It showed that in the first test the WEC was able to absorb as much as the best constant damping WEC, while in the second run it could absorb even slightly more. During the physical model test, the ANN showed its ability to select the better of two possible damping coefficients based on real world input data.

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI–TOF MS) is a commonly used technique for analyzing large biomolecules. However, the utilization of organic matrices limits the small-molecule analysis because of the interferences in the low-mass region and reproducibility issues. To overcome these limitations, a surface-assisted laser desorption/ionization (SALDI), which utilizes nanostructured metallic surfaces, has been developed. Herein, a novel approach for SALDI–MS was proposed using nanoengineered gold shell with nanogaps on the silica core (SiO2@Au NGS), which is an emerging material due to its excellent heat-generating capabilities. The gold shell thickness was controlled by adjusting the concentration of gold precursor for the growth of gold nanoparticles. SALDI-MS measurements were performed on a layer formed by drop-casting a mixture of SiO2@Au NGS and analytes. At the optimized process, the gold shell thickness was observed to be 17.2 nm, which showed the highest absorbance. The ion desorption efficiency was confirmed with a survival yield upon fragmentation. Based on the enhanced SALDI capability, SiO2@Au NGS was utilized to detect various small molecules including amino acids, sugars, and flavonoids. The limits of detection, reproducibility, and salt tolerance of SiO2@Au NGS demonstrate its potential as an effective and reliable SALDI material for small-molecule analyses.

Understanding surfactant self-assembly in deep eutectic solvents (DES) is important to their potential use in industrial formulations. We have recently reported the formation of a fracto-eutectogel comprising SDS fractal aggregates at a concentration as low as 1.6 wt% in glyceline (a DES comprising glycerol and choline chloride) at room temperature. The building units of the fractals consisted of multilayers of self-assembled SDS lamellae arranged in a dendritic pattern. Here we report that this fractal phase transitions into a fluid phase above a critical gelation temperature, TCG ~ 45 oC, evident from polarized light microscopy (PLM) observations. Small-angle neutron scattering (SANS) reveals that this phase transition is underpinned by the nanoscopic morphological transformation of the SDS lamellae into cylindrical micelles at T &gt; TGC. Fitting SANS profiles confirms that the morphology of the micelles is SDS-concentration (cSDS) dependent at T &gt; TGC: cylindrical at cSDS &gt; 0.6 wt% and spherical at cSDS = 0.6 wt%. At cSDS &lt; 0.6 wt%, only isotropic scattering was observed in the SANS profiles. Such SDS self-assembly behaviors contrast with those we have previously observed in glycerol, which we attribute to the presence of ions (i.e. choline chloride) in glyceline. Our findings have general implications to surfactant self-assembly in DES, solvents that are rich in hydrogen bonding and ions.

Posttraumatic stress disorder (PTSD) and/or specific PTSD symptoms may evoke disordered eating and unhealthy lifestyles, resulting in adverse cardiometabolic events (e.g., hypertension and obesity) in certain groups, which may implicate the treatment of this complex condition. The diagnostic criteria of PTSD have lately expanded beyond the three common symptoms (intrusion, avoidance, and hyperarousal). Simultaneously, four-, five-, and six-dimensional structures of the Impact of Event Scale-Revised (IES-R), a popular PTSD measure, seem to be more robust than the original three-dimension structure. Within the context of COVID-19, this instrumental study used a convenience sample of 58 dental healthcare workers (HCWs) from Russia (mean age = 44.1±12.2 years, 82.8% females) to examine the criterion and predictive validity of two IES-R structures: the IES-R3 and the IES-R6 (with the added symptoms of numbing, sleep disturbance, and irritability). The subscales of the two IES-R structures expressed good internal consistency, strong correlations with the PTSD Symptom Scale (PSS), hypertension diagnosis, and high body mass index (BMI). In receiver-operating characteristic (ROC) curve analysis, all IES-R models perfectly predicted the PSS (all area under the curve (AUC) >0.9, p values <0.001). The IES-R, both hyperarousal subscales, and the IES-R3 intrusion subscale significantly predicted high BMI. Both avoidance subscales and the IES-R3 intrusion subscale, not the IES-R, significantly predicted hypertension. In conclusion, both IES-R structures can reliably measure PTSD symptoms. The IES-R, hyperarousal, and intrusion may be credible criterion variables for predicting high BMI within PTSD while the intrusion and avoidance subscales may reflect the cardiovascular consequences associated with PTSD more reliably than the IES-R itself. The IES-R subscales may predict cardiometabolic adversities in PTSD, signifying a need for proper assessment of lifestyle and the application of dietary and exercise interventions in order to lower physical morbidity in PTSD. Replicating the study in larger samples, which comprise different physical and mental conditions from heterogenous cultural contexts is pivotal to validate the results (e.g., in specific groups such as those with confirmed traumatic exposure and comorbid mood dysfunction).