Distinct Levels Research Articles

Modified CD15/CD16-CLL1 inhibitory CAR-T cells for mitigating granulocytopenia toxicities in the treatment of acute myeloid leukemia

CLL1 Chimeric antigen receptor T-cell (CAR-T) therapy, as a promising immunotherapeutic approach, has demonstrated its potential to enhance the prognosis of patients diagnosed with acute myeloid leukemia (AML). However, due to the overexpression of CLL1 on neutrophils, CAR-T cells not only eliminated tumor cells but also eradicated neutrophils simultaneously, resulting in severe granulocytopenia and subsequent infections. Considering the distinct expression levels of CD15/CD16 on neutrophils and AML blasts, we have devised novel modified CD15 /CD16-CLL1 iCAR structures incorporating diverse inhibitory elements. Through extensive screening of structural optimization, we have successfully identified CD16-CLL1 iCAR-T cells that combine PD1 and 2B4 blockade, as well as a single VHH fragment replacing the entire CD16 scFv recognition domain. These modified cells demonstrate enhanced cytotoxicity against blasts while minimizing neutrophil elimination. Furthermore, their functionality has been effectively validated through both in vitro and in vivo experiments. In conclusion, we have successfully engineered innovative CD16-CLL1 iCAR-T cells, which preserves the cytotoxicity against tumor cells while preventing elimination of neutrophils, thereby significantly reducing the incidence of granulocytopenia during CAR-T therapy. Furthermore, our future objectives encompass the meticulous validation of both the efficacy and safety profile of this groundbreaking CAR-T therapy in clinical trials, as well as a comprehensive assessment of its potential to enhance the prognosis of patients diagnosed with AML.

Multitiered Proteome Analysis Displays the Hyperpermeability of the Rheumatoid Synovial Compartment for Plasma Proteins.

Rheumatoid arthritis (RA) is characterized by synovial hyperplasia and cartilage/bone destruction. RA affects the synovial joints, the synovial lining, and the permeability of the synovium. As the latter is of central relevance for the distribution of systemically delivered therapeutics into synovial fluid (SF), we here assessed the protein composition of paired plasma and SF of patients diagnosed with RA at three distinct levels of depth using mass spectrometric approaches: the "total" proteome, the "total" immunoglobulin G1 (IgG1) antibody repertoire, and the RA-specific anticitrullinated protein IgG1 autoantibody repertoire. The SF proteome was found to be dominated in numbers and concentration by plasma proteins, although we additionally detected several cartilage- and neutrophil-derived proteins of lower abundance. Strikingly, the plasma proteins were not only qualitatively reflected in SF but also quantitatively, independent of their size and/or other biochemical features. Also, the synovial "total" IgG1 and autoreactive anticitrullinated protein antibody IgG1 repertoire highly resembled the IgG1 repertoires detected in plasma within the same patient. Our comprehensive multilayer data thus reveals that the proteome, including the dominant, most abundant (auto)antibody clones, present in SF of RA patients is a direct reflection of the proteome present in blood, spiked by the local (immune) processes within the RA joint. We thus conclude that proteins directly pass from blood into SF of these joints without substantial bias. These findings thereby not only exemplify the use of in-depth multilayer proteome analyses to revisit basic concepts underlying RA pathology and to monitor the local (immune) processes destructive to cartilage but also provide evidence indicating that (protein-based) therapeutics may equally enter SF of swollen joints and that pharmacokinetic analyses of such therapeutics in blood are directly relevant to the synovial compartment.

Predicting terrain deformation patterns in off-road vehicle-soil interactions using TRR algorithm

Soil deformation is one of the parameters affecting the performance of off-road vehicles, including traction, mobility, and steering. This study offers an examination of soil deformation resulting from interactions with pneumatic and track wheels. Experiments were conducted using a soil bin with a single-wheel test rig, equipped with both a standard agricultural tire and a customized track wheel. Three distinct levels of vertical loads (2, 3, and 4kN) and forward velocities (1, 2, and 3 km/h) were applied using the wheel tester. The displacement and deformation of the soil layers, visualized as a vertical cross-section along the motion path, were consistently prepared and photographed for all experiments. Image analysis was undertaken with MATLAB software to scale images and extract graphical data. The highest deformation, with a value of 60.86 mm, is associated with the interaction of a pneumatic wheel with a force of 4 kN, while the lowest deformation occurs when the soil interacts with a track wheel with a force of 2 kN, with a value of 25.05 mm. Furthermore, the fitted surfaces obtained using the optimization algorithm showed good convergence with the experimental data, with R2 values of 0.9783 and 0.9516 for the pneumatic tire and tracked tire, respectively. The results demonstrated that the TRR model performs well in accurately predicting soil deformation induced by various types of wheels. A comparison between soil deformations caused by track wheels and pneumatic wheels revealed that track wheels result in less deformation and disturbance, particularly in the upper soil layers. These findings underscore the importance of considering the type of traction device and loading conditions when assessing soil deformation in agricultural environments.

P-358. Infection Prevention and Control Practice in Tertiary Care Military Hospitals in Bangladesh during COVID-19 Pandemic: Results from WHO IPCAF Assessment Tools

Abstract Background WHO estimates a high burden of the MDR organism in hospital settings in South Asian countries, especially in Bangladesh. Infection prevention and control (IPC) in healthcare settings is critical to limit the healthcare-associated infections, especially during pandemics like COVID-19. Military hospitals in Bangladesh are known for better compliance of health system practice. To measure current IPC level, activities, resources, and gaps, WHO developed the IPC Assessment Framework (IPCAF) assessment tools. We aimed to assess the IPC practices level in Military hospitals of Bangladesh to reduce the gaps.Table of WHO IPCAF score of Dhaka and Jashore CMH Methods From Oct to Dec 2019, using IPCAF tools, we conducted a cross-sectional survey at two tertiary level Combined Military hospitals (CMH) in Bangladesh to collect the information. The hospital administrator, IPC focal person, or relevant person who had better knowledge about the overall IPC situation was interviewed. The tool comprises eight sections assessing the eight IPC core components. Based on the scores ranging from 0 to 100, the hospital was categorized into four distinct IPC levels - Advanced, Intermediate, Basic and Inadequate. Descriptive statistics were used to summarize and describe the key findings. Results The mean IPCAF score was 421.75 out of 800.The Dhaka CMH (1100 bedded) was categorized as ‘intermediate’ with the score of 497.5 and Jashore CMH (500 bedded) scored 346 which was characterized ‘basic’ level. Both the hospitals scored very high in adherence of IPC guideline 87.5 and 77.5 respectively. Scoring of Dhaka CMH in IPC program (77.5), Workload, staffing & bed occupancy (75) and Built environment, materials and equipment (82.5) is higher than the same components of Jashore CMH. IPC education & training score of both hospitals (50 and 25 respectively) was not satisfactory. None of the hospitals had an IPC surveillance system with standard surveillance case definitions to track HAIs. Conclusion Military hospitals needs to attain advanced level IPC practice. To achieve that organized quality improvement programs and feedback mechanisms is required. To strengthen all IPC core components, particularly IPC surveillance, monitoring, education & training is needed to improve for healthcare safety and resilience. Disclosures All Authors: No reported disclosures

Distinct TYRO3 and PROS1 expression levels contribute to preeclampsia pathogenesis.

Preeclampsia (PE) is a severe placental complication occurring after the 20thweek of pregnancy. PE is associated with inflammation and an increased immune reaction against the fetus. TYRO3 and PROS1 suppress inflammation by clearing apoptotic cells. Disruptions in TYRO3/PROS1 signaling may increase the risk of PE. This study investigated the role of TYRO3/PROS1 signaling in the development of PE using healthy placentae (HP) and preeclamptic placentae (PP) of six pregnant women each. Tissue morphology using hematoxylin and eosin (H&E), TYRO3, MERTK, PROS1, and GAS6 mRNA levels using qPCR and localization and expression levels of TYRO3 and PROS1 using immunohistochemical staining (IHC) were evaluated. The study results show that the levels of TYRO3, MERTK, PROS1 and GAS6 mRNA, as well as TYRO3 protein, increased in PE. TYRO3 expression was observed in extravillous trophoblast (EVTs) and syncytiotrophoblast cells (SCTs). PROS1 was observed in HP fetal vessels through IHC while absent in PP. The reduced presence of PROS1 in the cytotrophoblast layer in PE may indicate a compromised blood-placental barrier. The absence of PROS1 in fetal vessels may suggest potential complement activation and thrombosis. TYRO3, MERTK, PROS1 and GAS6 may help balance impaired inflammation, apoptosis, thrombosis, complement activation and the blood-placental barrier in PE.

Could Urinary Amino Acids be as New Biomarkers for Detection of Sarcopenia?

Abstract Background Sarcopenia is a syndrome that cause falls, fractures, and morbidity in geriatric patients. Early diagnosis of sarcopenia is important as it is known that muscle functions can improve with early intervention. We aimed to investigate whether urinary amino acid levels could serve as biomarkers of sarcopenia. Methods The study included ninety-one patients aged 45–65 who visited to our outpatient clinic. The patients underwent physical examinations, blood tests and sixteen distinct urinary amino acid levels were analyzed. Anthropometric measurements were performed. Physical performances were evaluated. Muscle strengths were measured. Muscle masses were analyzed. Patients were divided into 4 groups: pre-sarcopenic, sarcopenic, severe sarcopenic and non-sarcopenic. Statistical significance level was determined as p < 0.05. Results A total of ninety-one patients, 53 females and 38 males, were included in the study. Three patients had pre-sarcopenia, eleven had sarcopenia, and two met the criteria for severe sarcopenia, while seventy-five patients were non sarcopenic. A significant difference was found between the sarcopenia and non-sarcopenia groups in terms of glutamine and valine levels (p < 0.001 for both). In the ROC analysis, the cut-off value of glutamine and valine levels in detecting sarcopenia was determined as 492 µmol/L and 209 µmol/L (AUC: 0.875 and 0.968). Correlation analysis revealed a negative relationship between urinary leucine levels and both muscle strength and muscle mass Conclusions We found that patients with sarcopenia had high urinary glutamine and valine levels and that urinary leucine levels were associated with both muscle strength and muscle mass.

Item-level measurement properties of the pediatric awareness and sensory motor assessment in children with medical complexity

IntroductionChildren with medical complexity (CMC) are medically fragile with severe brain damage and chronic conditions, necessitating daily care. Their neurological impairments often limit participation in childhood activities, affecting quality of life. Current assessment tools fail to detect subtle abilities in CMC, hindering development of effective rehabilitation goals and interventions. The Pediatric Awareness and Sensory Motor Assessment (PASMA) was created to fill this gap, providing sensitive measurement of sensory awareness and motor response across five domains (i.e., olfactory, visual, auditory, gustatory, and tactile).MethodsIn this retrospective study, a Rasch analysis was conducted on PASMA data for CMC. The PASMA was administered five times over ten weekdays to each child, reflecting its intended clinical use to gain a reliable sense of each child's awareness.ResultsAnalysis of data from 36 CMC revealed that the PASMA is sufficiently unidimensional, effectively measuring sensory awareness and motor response as a single construct. Its rating scale structure was validated without modifications, and the item hierarchy matched clinical expectations. High item reliability (0.97) was observed, with one item (V2 blink in response to light) slightly misfitting, but without affecting overall measures. Adequate person reliability was observed (0.81), with 15% person misfit. Person misfit did not degrade item measures or model statistics. Differential item functioning (DIF) was noted for the three easiest items on specific days. The PASMA successfully stratified participants into three distinct awareness levels (low, medium, and high awareness), without floor or ceiling effects.DiscussionThe PASMA is a valid unidimensional measure of sensory awareness and motor response in CMC. Rating scale characteristics, item hierarchy, and person separation measures all support the PASMA's measurement properties within this heterogeneous sample of CMC. DIF findings support a potential reduction in the recommended number of PASMA administrations per individual. Future research will focus on establishing rater reliability and external validity. Additional efforts will support health professionals to utilize the PASMA for baseline assessments, guiding personalized interventions, and tracking progress.ConclusionClinical use of the PASMA could provide new opportunities to detect subtle abilities, preferences, and changes in CMC, to promote meaningful participation and improve quality of life.

Identification of Mango Cultivars’ Resistance Against Red Spider Mite: Impact of Climate Elements on Resistance Performance

The use of resistant plants is recognized as an environmentally friendly measure for mite control. Oligonychus mangiferus, known as the mango red spider mite (MRSM), is a dangerous pest for mango production. To date, the resistance levels of the mango germplasms against the MRSM remain largely unknown. Furthermore, the environmental factors potentially influencing resistance performance have been seldom discussed. To fill those knowledge gaps, this study aimed to identify the resistance level of twelve mango cultivars against the MRSM. Based on three rounds of greenhouse and five seasons of field tests, cultivars with distinct resistant levels were identified. When exploring the climate impact, we found that for the susceptible cultivars, precipitation is the primary external environment factor altering the resistance performance, while temperature presents a secondary effect, and air humidity did not show a significant impact on MRSM resistance. By contrast, MRSM-resistant cultivars were not prone to be affected by changing climate conditions. Furthermore, yield tests indicated that the resistant cultivars can better reduce the yield losses compared with the susceptible ones. This study illustrated the climate element-driven effect on mango tree resistance performance against the MRSM, which can provide insight into insect pest management under changing climate conditions.

The immunological landscape of CCL26High invasive oral squamous cell carcinoma

BackgroundOur previous study demonstrated that CCL26 secreted by cancer-associated fibroblasts (CAF) promoted the invasive phenotype of oral squamous cell carcinoma (OSCC), however, more comprehensive clinical expression patterns of CCL26 and its role in immunotherapy remains ambiguous.MethodsCCL26 levels in different cancer and normal tissues were analyzed and validated in 67 OSCC patients through immunohistochemical staining (IHC). The clinical spatial distribution pattern of CCL26 in tumor microenvironment was determined, and its clinical outcomes were investigated. We also determined the invasive phenotype of tumor cells with distinct CCL26 level and explored its immune checkpoint and immunocytes relevance by differentially expressed gene (DEG) analysis, GSEA, and GO analysis. We collected peripheral blood from 28 OSCC patients to assess the percentage and absolute number of lymphocytes by flow cytometry.ResultsCCL26 was upregulated in HNSC and preferentially high-expressed on CAFs and tumor cells in OSCC patients, which exhibits a trend toward decreased overall survival. CCL26high OSCC had a characteristic of tumor invasive phenotype with upregulated CLDN8/20 and reduced keratin KRT36, which was significantly associated with EMT markers (CDH1, CDH2, VIM, SNAI2). In addition, CCL26high OSCC was found to be associated with immunoglobulin mediated immune response, B cell mediated immunity et al. Indeed, immune checkpoint molecules (PD-L1, PD-L2, et al.) also decreased in CCL26high OSCC. However, CCL26 did not affect T/B/NK lymphocytes in peripheral blood of OSCC patients.ConclusionCCL26 could regulate Immune balance and promote invasiveness of OSCC, which gave a new insight into a potential immunotherapy strategy.

Complex and Non-sequential Current Signatures of a β-Hairpin Peptide Confined in a Nanopore.

Nanopore sensing is widely used for single-molecule detection, originally applied to nucleic acids and now extended to protein sensing. Our study focuses on the complex conformational changes of peptides in nanopores, which may have implications for peptide fingerprinting and protein identification. Specifically, we investigated the interaction of a β-hairpin peptide (SV28) within an α-hemolysin (αHL) nanopore. Our experiments revealed that SV28 is captured via dielectrophoresis and exhibits long dwell times within the nanopore, leading to multiple current blockade levels. Unlike DNA hairpins, the peptide showed non-sequential transitions among four distinct blockade levels. This complex behavior indicates that the peptide dynamics in nanopores cannot be simply modeled along a single reaction coordinate. Our findings provide insights into peptide-nanopore interactions, which are potentially useful for developing nanopore-based peptide identification technologies.

Assessing bnAb potency in the context of HIV-1 envelope conformational plasticity.

For use in prevention and treatment, HIV-1 broadly neutralizing antibodies (bnAbs) have to overcome Env conformational heterogeneity of viral quasispecies and neutralize with constant high potency. Comparative analysis of neutralization data from the CATNAP database revealed a nuanced relationship between bnAb activity and Env conformational flexibility, with substantial epitope-specific variation of bnAb potency ranging from increased to decreased activity against open, neutralization-sensitive Env. To systematically investigate the impact of variability in Env conformation on bnAb potency we screened 126 JR-CSF point mutants for generalized neutralization sensitivity to weakly neutralizing antibodies (weak-nAbs) depending on trimer opening and plasma from people with chronic HIV-1 infection. 23 mutations resulted in a highly neutralization sensitive phenotype, which was associated with de-stabilization of the closed, prefusion conformation. Including 19 of these mutants into a Sensitivity Env mutant panel (SENSE-19), we classified bnAbs according to potency variations in response to trimer opening. To verify that these sensitivity patterns are independent of the in vitro assay system, replication-competent SENSE-19 mutant viruses were tested on primary CD4 T cells. While loss of potency on SENSE-19 was registered for bnAbs from several classes recognizing quaternary epitopes on pre-triggered Env, structural destabilization benefitted MPER bnAbs and other inhibitors known to have post-CD4 attachment neutralization activity. Importantly, for a subset of CD4bs bnAbs, and the interface bnAb PGT151, particularly low potency variation was noted, suggesting that Env conformational tolerance can be achieved but is not the rule. In summary, SENSE-19 screens revealed distinct tolerance levels to Env conformational intermediates between bnAbs that provide mechanistic insights in their function and broaden current neutralization breadth assessments.

How do foreign and domestic institutional investors drive the market value? The influence of family ownership

PurposeThis paper aims to investigate and compare the impact of foreign and domestic institutional investors on the market value of family and non-family companies. Subsequently, it examines how different degrees of family ownership influence foreign and domestic institutional investors and their value impacts.Design/methodology/approachThe sample of this study includes 339 non-financial firms from NIFTY-500 for 11 years from 2011 to 2020, which contains 128 family and 211 non-family companies. Both static (fixed-effect model) and dynamic (two-step system generalized method of moments) models are employed to test the hypotheses.FindingsFindings suggest that foreign institutional investors outshine domestic institutions regarding value creation. Meanwhile, higher (>50%) family holdings are detrimental to foreign institutional investors, while moderate holdings (26–49%) improve domestic institutional investments. The favorable effect of foreign players gets diluted with the higher (>50%) family holdings, while the adverse impact of domestic players improves with the moderate (26–49%) family holdings. Overall, partial family control is beneficial, while low and absolute family control is detrimental to market value. These findings indicate that institutional investors are family control-dependent, where the family control effect is not static.Originality/valueThis paper offers a novel perspective by addressing the effect of costs and benefits realized at three distinctive levels of family holdings on foreign and domestic institutional investors and their value impacts to witness differences caused by varying family control, which is not done earlier as per the best of our knowledge.

Exploring the Association between Perceived Career Crisis, Discrepancy of Career Expectations, and Wellbeing among Hungarian Employees

The study aims to extend knowledge of how Discrepancy of Career Expectations, Perceived Career Crisis, and Wellbeing relate to each other, and thereby contribute to the theory and application of targeted wellbeing positive psychology interventions, with a special focus on Hungarian employees. Cross-sectional research was conducted to analyse whether there is a relationship between the level of Perceived Career Crisis and the Discrepancy between Current and Ideal Career Stages, the relationship between Perceived Career Crisis and Wellbeing, and which Wellbeing components seem to be overarching in the case of Perceived Career Crisis and Discrepancy. In the case of Discrepancy, there is a significant difference between distinct levels of Perceived Career Crisis. Our findings suggest that Autonomy, Optimism, and Empathy seem to be overarching Wellbeing components that might serve to reduce the potential negative outcomes of Discrepancy of Career Expectations and Perceived Career Crisis. Although further research is needed, these findings may help practitioners to further develop customised wellbeing and career management programmes, as well as to apply targeted interventions – as opposed to general solutions – to increase employees' career satisfaction.

Self-Bias and Self-Related Mentalizing are Unaltered in Adolescents with Autism.

The self is a multidimensional concept that can be represented at a pre-reflective (first-order) level, at a deeper, reflective level (second-order), or even at a meta-level (representing one's own thoughts, i.e. self-related mentalizing). Since self-related processing and mentalizing are crucial for social cognition, both constructs have been researched in individuals with autism, who experience persistent socio-communicative difficulties. Some studies suggested autism-related reductions of the self-bias, i.e. tendency to preferentially process self-related content; while others observed a decreased ability to mentalize on one's own thoughts in autism. However, prior research examined distinct levels of self-related processing in isolation, in the context of separate studies. In this investigation, we directly compared self-bias, self- and other-related mentalizing within the same sample of adolescents with and without autism, to identify which of these are altered in this condition. Thirty adolescents with autism and 26 age- and IQ-matched controls performed a visual search task (first-order self-bias), a trait adjectives task (second-order self-bias), a feeling-of-knowing task (self-related mentalizing) and the Frith-Happé animations task (other-related mentalizing). Parents also completed two questionnaires (i.e. SRS, SCQ) assessing the adolescent's degree of autism traits. Our findings replicated previous research showing reduced other-related mentalizing in autism. However, we did not find any difference between adolescents with and without autism in terms of first- or second-order self-bias, nor in the ability to mentalize on one's own thoughts. In line with recent investigations, our results do not support earlier claims of altered self-related information processing in autism.

Task-Oriented Adversarial Attacks for Aspect-Based Sentiment Analysis Models

Adversarial attacks deliberately modify deep learning inputs, mislead models, and cause incorrect results. Previous adversarial attacks on sentiment analysis models have demonstrated success in misleading these models. However, most existing attacks in sentiment analysis have applied a generalized approach to input modifications, without considering the characteristics and objectives of the different analysis levels. Specifically, for aspect-based sentiment analysis, there is a lack of attack methods that modify inputs in accordance with the evaluated aspects. Consequently, unnecessary modifications are made, compromising the input semantics, making the changes more detectable, and avoiding the identification of new vulnerabilities. In previous work, we proposed a model to generate adversarial examples in particular for aspect-based sentiment analysis. In this paper, we assess the effectiveness of our adversarial example model in negatively impacting aspect-based model results while maintaining high levels of semantic inputs. To conduct this evaluation, we propose diverse adversarial attacks across different dataset domains, target architectures, and consider distinct levels of victim model knowledge, thus obtaining a comprehensive evaluation. The obtained results demonstrate that our approach outperforms existing attack methods in terms of accuracy reduction and semantic similarity, achieving a 65.30% reduction in model accuracy with a low perturbation ratio of 7.79%. These findings highlight the importance of considering task-specific characteristics when designing adversarial examples, as even simple modifications to elements that support task classification can successfully mislead models.

Evidence of Financial Market Synergies. A Combined Statistical and Machine Learning Research Approach

The research investigates how geopolitical conflicts influence global commodities and financial markets, aiming to identify effects on oil companies' economic and financial indicators within the context of financial market synergies. An integrated approach is implemented in the study, combining traditional statistical methods with advanced machine learning techniques. The results of the investigation were analyzed at two distinct levels: (1) that of the evolution of global commodity prices during the conflicts in Ukraine and the Gaza Strip, and (2) that of the financial performance of 250 companies in the oil industry, summarized in seven key indicators. Eloquent findings indicate that both conflicts have triggered significant changes in commodity prices, with strong investor reactions to safe haven assets such as gold and silver. In addition, there are clear manifestations of behavior reflecting supply-side concerns, as energy prices have risen considerably. Correlation analysis has shown a significant link between upward oil price movements and changes in some financial indicators (such as EBITDA) as well as asset increases. By integrating traditional statistical methods and advanced machine learning techniques, the ability of GARCH-ML hybrid models to accurately predict stock index volatility in the context of geopolitical tensions is highlighted. The research results highlight the benefits of the holistic approach in analyzing the complex interconnections between commodity markets, financial markets and company performance.

Multioptimization analysis of machining characteristics on spark electrical discharge machining of Al/SiC and Al/SiC/B4C composites

Abstract This study examines the machining characteristics of sintered aluminium composites, including Al+6% SiC and Al+4% SiC+2% B4C, utilising electrical discharge machining (EDM) by changing the input machining factors like current (C) in amperes, Pulse-OFF and Pulse-ON time in µs, at three distinct levels. The L9 Orthogonal array was employed to examine the influence of process factors on output variables, including Surface Roughness (SR), Material Removal Rate (MRR), and Hole Circularity (HC). An Analysis of Variance (ANOVA) was conducted to determine the percentage contribution of the input factors to the output variables. Observation implies that the GRG ANOVA for Al+6%SiC composite has achieved the most significant contribution to the total GRG, with the current contributing 86.66%, followed by the Pulse-ON at 8.52% and the Pulse-OFF at 3.63%. In contrast, the Al+4%SiC+2%B4C composite shows the current contributing 74.07%, followed by the Pulse-ON at 15.25% and the Pulse-OFF at 9.82%. The ideal input levels for Al+6%SiC and Al+4%SiC+2%B4C composites were found to be A3B3C1 and A3B3C3, correspondingly.

Design optimisation of kirigami-based auxetic metamaterials with multistability and shape-morphing capability

ABSTRACT This study presents the development of kirigami-based auxetic metamaterials with multistability and shape-morphing capabilities through a design optimisation framework leveraging machine learning technology. The framework employs surrogate models and a differential evolution algorithm to optimise the design variables of a kirigami cell, ensuring specified bistability and scalability conditions. This cell-level optimisation is extended to the structure-level, where each cell is optimised for its assigned multistability and scalability. For multistability, the auxetic structure is divided into subregions with distinct bistability levels, with optimisation conducted accordingly. For shape morphing, the expansion ratio of each cell is predefined according to the target geometry, with optimisation performed to ensure required scalability. Various auxetic structures, combining different bistability levels and target geometries, are optimally designed and additively manufactured for experimental validation. The experimental results confirm that the proposed design optimisation effectively controls the auxetic behaviour, enabling tunable shapes morphing and programmed transformation sequences.

The Evolution of Digital Building Logbook: Exploring Building Information Gathering Systems to Boost Building Maintenance and Renovation

The architecture, engineering, construction, and operations industry is distinguished by having plenty of and a variety of data, which makes the acquisition, storage, retrieval, and use of information difficult. Due to a data exchange system primarily based on paper-based transmission, multiple classification systems, the use of inconsistent criteria and practices, and a significant number of stakeholders involved during the building life cycle (each with distinct requirements and levels of access to information), the entire construction process must deal with ineffective information exchange among actors. Instead of multiple sources of information and tools to collect, store, and share data, one single source of information could become a reference point for numerous stakeholders. In this regard, a digital building logbook is assumed to be a collector of building-related data starting from the design phase, which plays a fundamental role in information management. This paper proposes a systematic literature review aimed at identifying the main features of the tool, investigating its growth in the construction sector. The results show that the digital building logbook’s main application is in the operations and maintenance field with relevance to renovation. However, a common model is absent, varying greatly based on the country and category of building. This analysis contributes to increasing awareness by identifying the attributes, gaps, and potentialities of the subject matter.

Hierarchical graph-based integration network for propaganda detection in textual news articles on social media

During the Covid-19 pandemic, the widespread use of social media platforms has facilitated the dissemination of information, fake news, and propaganda, serving as a vital source of self-reported symptoms related to Covid-19. Existing graph-based models, such as Graph Neural Networks (GNNs), have achieved notable success in Natural Language Processing (NLP). However, utilizing GNN-based models for propaganda detection remains challenging because of the challenges related to mining distinct word interactions and storing nonconsecutive and broad contextual data. In this study, we propose a Hierarchical Graph-based Integration Network (H-GIN) designed for detecting propaganda in text within a defined domain using multilabel classification. H-GIN is extracted to build a bi-layer graph inter-intra-channel, such as Residual-driven Enhancement and Processing (RDEP) and Attention-driven Multichannel feature Fusing (ADMF) with suitable labels at two distinct classification levels. First, RDEP procedures facilitate information interactions between distant nodes. Second, by employing these guidelines, ADMF standardizes the Tri-Channels 3-S (sequence, semantic, and syntactic) layer, enabling effective propaganda detection through related and unrelated information propagation of news representations into a classifier from the existing ProText, Qprop, and PTC datasets, thereby ensuring its availability to the public. The H-GIN model demonstrated exceptional performance, achieving an impressive 82% accuracy and surpassing current leading models. Notably, the model’s capacity to identify previously unseen examples across diverse openness scenarios at 82% accuracy using the ProText dataset was particularly significant.