Large-scale Tests Research Articles

Solving the storage location assignment of large-scale automated warehouse based on dynamic vortex search algorithm

This paper establishes the mathematical model for Storage Location Assignment (SLA) problem in large-scale automated warehouses by combining three objectives: efficiency, shelf stability, and stacker load balancing. Along with a novel repair strategy to handle the complex constraints of large-scale problems. Additionally, a coding method and solution approach suitable for practical application scenarios are developed. In order to solve large-scale SLA problem, an improved vortex search algorithm is proposed based on attraction operation in flow field, dimension-by-dimension dynamic radius and leadership decision-making mechanism (FDVSA). In the experimental part, the algorithm effectiveness experiment of FDVSA was first conducted using the large-scale global optimization test sets IEEE congress on evolutionary computation 2010 and 2013 (CEC2010, CEC2013). The results show that: (1) Compared with other comparison algorithms, the comprehensive average optimization rate of FDVSA in CEC2010 and CEC2013 is 88 % and 78 %, respectively. (2) The experimental results of FDVSA showed that each improvement strategy has advantages in dealing with large-scale problems. (3) The post-hoc analysis showed that there are significant differences between FDVSA and other comparison algorithms, and FDVSA is significantly better. Finally, FDVSA and other comparison algorithms are solved on three different scale and complexity of SLA cases. The results show that: (1) FDVSA has significant advantages in solving large-scale SLA problem, and the comprehensive average optimization rate is 19 %. (2) The convergence curve and boxplot showed that FDVSA has good convergence speed and solving stability. (3) The effectiveness of the repair strategy was verified by experiments in the large-scale SLA problems.

Thiocarbohydrazide-cross-linked pectin: A promising support material for palladium nanoparticles in the water-mediated Suzuki-Miyaura cross-coupling reaction and the catalytic reduction of 4-nitrophenol

Suzuki-Miyaura reaction is a classic palladium-catalyzed reaction for forming Csp2-Csp2 bonds. However, the leaching of Pd species and using organic solvents are two important problems that adversely affect their performance, reusability, and greenness. Considering the abundant resources, cheapness, and availability of pectin (Pec), a Pec-based catalyst for the Suzuki reaction in pure water as a non-toxic reaction medium was developed for the first time. In this regard, Pec was chosen as a benign support and cross-linked by thiocarbohydrazide (TCH) to prepare Pec-TCH. The Pec-TCH acted as an efficient reducing and stabilizing agent for in-situ fabrication of Pd(0) nanoparticles. The heterogeneous and air-stable Pec-TCH-Pd(0) nanoparticles were thoroughly characterized. The results exhibited a significant catalytic activity (up to 99%) and reusability (at least 7 times) for in-water Suzuki-Miyaura cross-coupling reaction. The use of TBAB as a phase-transfer catalyst improved the yield of Suzuki reaction since it prevented the problems related to the immiscibility of water and aryl halides as well as the gelation of catalyst. The catalyst was also utilized for water-mediated 4-nitrophenol reduction to 4-aminophenol with NaBH4 which showed excellent activity (k = 9.2 × 10−3 s−1). A very good compatibility with iodobenzene, bromobenzene, and chlorobenzene was observed for the Suzuki reaction. Because of various binding sites such as amino, hydroxyl, carbonyl and thiocarbonyl groups, negligible leach of Pd nanoparticles was observed, as shown by ICP-MS after runing 7th of reusability studies. Moreover, the leaching, mercury poisoning, and large-scale tests showed its potential for industrial applications.

Large-scale model testing of high-pressure grouting reinforcement for bedding slope with rapid-setting polyurethane

Large-scale model testing of high-pressure grouting reinforcement for bedding slope with rapid-setting polyurethane

Foreword to the special issue “Adjacent Interacting Masonry Structures”

This special issue focuses on the seismic performance of adjacent interacting masonry structures, particularly in historical European city centers. The AIMS project provided unique data on interacting masonry structures through large-scale shake table tests on two adjacent half-scale stone masonry buildings. The experimental campaign was accomapgnied by a blind prediction study where participants modeled the aggregate’s seismic response. Findings highlight challenges in accurately predicting displacement demands and failure modes, providing critical insights for improving future modeling techniques for masonry buildings.

Transformation of Early Reading Ability Through Smart Wheel Media Based on the TGT Learning Model

This study introduces the Kintar/Smarwheel learning media, a one meter diameter Smart Wheel media that was developed using the Team Game Tournament (TGT) learning model to improve the initial reading skills of grade 1 elementary school students. The problem of urgency is highlighted by the low reading ability of the average grade 1 child, encouraging the development of effective learning media. This research aims to develop, validate and evaluate the effectiveness of Kintar in improving student learning outcomes in early reading. This study uses a development approach, integrating the product design phase, feasibility validation by media and materials experts, and effectiveness testing through small and large scale trials involving grade 1 students from SDN Purwoyoso 03, Semarang City. Feasibility assessments by experts confirmed a high degree of suitability for educational use, with significant improvements in student learning outcomes observed during the pilot. The small scale trial showed an average increase from pretest to posttest scores from 46.67 to 70.00, with a significant difference of 23.33. The large-scale trial showed a mean increase from 61.47 to 81.47, with a striking difference of 20.00. The N-Gain test results show a moderate impact, with values ​​of 0.3959 and 0.5298 for small and large scale tests, respectively. This study concludes that Kintar, integrated with the TGT learning model, is a powerful educational tool for improving early reading skills. Positive feedback from students and teachers underscores its effectiveness in generating engagement and improving learning outcomes in the classroom.

Vertical Track–Bridge Interaction in Railway Bridges with Ballast Superstructure: Experimental Analysis of Dynamic Stiffness and Damping Behavior

Realistic vibration predictions of railway bridges during high-speed train crossings require reliable dynamic input parameters for the applied calculation model. In particular, the dynamic stiffness and damping properties of the ballast superstructure for the mathematical consideration of the vertical track–bridge interaction (TBI) significantly influence the generated calculation results. However, due to a striking scattering of model-related characteristic values available in the literature, adequate and realistic consideration of the dynamic properties of the vertical TBI in vibration predictions is associated with considerable uncertainties. This uncertainness illustrates the need to determine experimental-based and reliable characteristic values. For targeted and isolated research of dynamic characteristics of vertical TBI, a unique large-scale test facility was developed at the Institute of Structural Engineering at TU Wien, which replicates a section of ballast superstructure on a railway bridge on a scale of 1:1 and excited to vertical movements. This paper presents the essential results and findings from the experiments, focusing on determining the ballast superstructure’s dynamic stiffness and damping characteristics, which can subsequently be implemented in practical applications for vibration predictions. As a result of the experiments, model-related dynamic stiffness and damping parameters are provided to describe the vertical TBI. In addition, the experiments are used to identify destabilization processes occurring in the ballast superstructure as a result of vertical vibrations. The investigations include the vertical TBI’s displacement and acceleration behavior and the track’s settlement behavior due to the entire structure’s vertical movements. These investigations allow for assessing the currently valid, internationally, and nationally normatively prescribed permissible accelerations for railway bridges due to train crossings. The conclusion is that short-term excessive vertical accelerations of the ballast superstructure caused by train passage do not destabilize the ballast bed or considerably change the track position.

Liver cancer in ovo models for preclinical testing.

Immunotherapies have significantly improved the prognosis of patients with advanced hepatocellular carcinoma (HCC), although more than 70% of patients still do not respond to this first-line treatment. Many new combination strategies are currently being explored, which drastically increases the need for preclinical models that would allow large-scale testing of new immunotherapies and their combinations. We developed several in ovo (in the egg) human liver cancer models, based on human tumor xenografts of different liver cancer cell lines on the chicken embryo's chorioallantoic membrane. We characterized the angiogenesis, as well as the collagen accumulation and tumor immune microenvironment, and tested atezolizumab (anti-PD-L1) plus bevacizumab (anti-VEGF) treatment. Our results show the involvement of chicken immune cells in tumor growth, reproducing a classical non-inflamed "cold" as well as inflamed "hot" tumor status, depending on the in ovo liver cancer model. The treatment by atezolizumab and bevacizumab was highly efficient in the "hot" tumor model PLC/PRF/5 in ovo with the reduction of tumor size by 76% (p ≤ .0001) compared with the control, whereas the efficacy was limited in the "cold" Hep3B in ovo tumor. The contribution of the anti-PD-L1 blockade to the anti-tumoral effect in the PLC/PRF/5 in ovo model was demonstrated by the efficacy of atezolizumab monotherapy (p = .0080, compared with the control). To conclude, our study provides a detailed characterization and rational arguments that could help to partially replace conventional laboratory animals with a more ethical model, suited to the current needs of preclinical research of new immunotherapies for liver cancer.

Development of wall volley digital test in badminton

Abstract: Introduction. Badminton is a sport that is popular with all levels of society, because it is easy to play and does not require a large field, and can even be played indoors or outdoors. Therefore, the game of badminton in Indonesia can develop rapidly. One of the tests in badminton definitely uses the wall volleyball test to determine the skill level of training children or students. As is known, the wall volleyball test is a very good test to see how far a person’s hitting ability in badminton is. The form of solving existing problems is by conducting research and developing wall volleyball tests so that teachers and coaches, especially athletes, no longer have doubts about their badminton skills. Objective. To develop one of the skills test tools in badminton, namely wall volleyball. Method. This research uses the research and development method which is used to produce certain product designs, test the effectiveness and validity of the designs that have been created so that the product is tested and can be used by the public. Results. Questionnaire results from small-scale tests on the development of digital-based wall volleyball tests in PB. Persada Kulon Progo Yogyakarta, showed an assessment percentage of 95.38% and was categorized as “Decent”. With such results, it is very feasible to continue this development to large-scale testing to see how feasible the results are with more people trying it and from different places. Large-scale trials were carried out at the PB club. PWS Godean Sleman and PB. Arjuna Bantul with a total of 35 athletes who met the criteria. Questionnaire results from a large-scale test of the development of a digital-based wall volleyball test in PB. PWS Godean Sleman and PB. Arjuna Bantul showed an assessment percentage of 99.56% and was categorized as “Decent”. With such results, this development is very feasible to continue to the next stage, namely effectiveness. Testing the validity of this digital-based wall volleyball test instrument includes testing material with a score of 11 out of 13 maximum scores and getting a percentage of 84.61% (Decent). Practitioner testing with a score of 11 out of 13 maximum scores and obtained a percentage of 84.61% (Layal). Meanwhile, testing the validity of test equipment experts with a score of 30 out of 32 maximum scores received a percentage of 93.75% (Decent). Based on the results of the data percentage above, what has been developed by the researchers received the title “feasible” and was declared valid. The first trial test between digital and manual tests was not significant because the results obtained were the same. Meanwhile, for the 2 digital and manual experimental tests, t = 4.779 and sig = 0.000 Sig < α (0.000 < 0.05), thus meaning that the 2 digital experimental tests are significantly different from the 2 manual tests. Conclusion. The development of a digital-based wall volleyball test was stated to be significant in achieving better results than the manual one.

Multi-guided population co-evolutionary algorithm based on multiple similarity decomposition for large-scale flexible job shop scheduling problem

As manufacturing shifts towards large-scale production, the size of the workshop increases, and its search space exponentially expands. It is difficult for existing algorithms to obtain an ideal scheduling solution in an acceptable time. For the large-scale flexible job shop scheduling problem (LSFJSP), a multi-guided population co-evolutionary algorithm based on multiple similarity decomposition (MPCSD) is designed. Faced with the problem of high dimensionality and complex solution space, a multiple similarity decomposition strategy is proposed. It proceeds to group based on similarity information at the dimension and population level. To obtain convergence-preferred and diversity-preferred dimension groupings, a training-set solution selection method is proposed. Inspired by the idea of divide-and-conquer, a multi-guided co-evolutionary strategy is proposed. It improves the exploration efficiency of the algorithm in the search space. To test effectiveness on more complex LSFJSP, a set of large-scale test problems including LS1-12 are designed. On LS1-12, MPCSD is compared with seven other algorithms to demonstrate its superiority. MPCSD performed best on 11, 7, and 10 of the 12 test problems on Inverted Generational Distance (IGD), Hypervolume (HV) and Schott’s Spacing Metric (SP), respectively. Meanwhile, the Relative Deviation (RD) results showed that MPCSD obtained the best fitness performance.

Tracking the pre-clinical progression of transthyretin amyloid cardiomyopathy using artificial intelligence-enabled electrocardiography and echocardiography.

Diagnosing transthyretin amyloid cardiomyopathy (ATTR-CM) requires advanced imaging, precluding large-scale testing for pre-clinical disease. We examined the application of artificial intelligence (AI) to echocardiography (TTE) and electrocardiography (ECG) as a scalable strategy to quantify pre-clinical trends in ATTR-CM. Across age/sex-matched case-control datasets in the Yale-New Haven Health System (YNHHS) we trained deep learning models to identify ATTR-CM-specific signatures on TTE videos and ECG images (area under the curve of 0.93 and 0.91, respectively). We deployed these across all studies of individuals referred for cardiac nuclear amyloid imaging in an independent population at YNHHS and an external population from the Houston Methodist Hospitals (HMH) to define longitudinal trends in AI-defined probabilities for ATTR-CM using age/sex-adjusted linear mixed models, and describe discrimination metrics during the early pre-clinical stage. Among 984 participants referred for cardiac nuclear amyloid imaging at YNHHS (median age 74 years, 44.3% female) and 806 at HMH (69 years, 34.5% female), 112 (11.4%) and 174 (21.6%) tested positive for ATTR-CM, respectively. Across both cohorts and modalities, AI-defined ATTR-CM probabilities derived from 7,423 TTEs and 32,205 ECGs showed significantly faster progression rates in the years before clinical diagnosis in cases versus controls (p time × group interaction ≤0.004). In the one-to-three-year window before cardiac nuclear amyloid imaging sensitivity/specificity metrics were estimated at 86.2%/44.2% [YNHHS] vs 65.7%/65.5% [HMH] for AI-Echo, and 89.8%/40.6% [YNHHS] vs 88.5%/35.1% [HMH] for AI-ECG. We demonstrate that AI tools for echocardiographic videos and ECG images can enable scalable identification of pre-clinical ATTR-CM, flagging individuals who may benefit from risk-modifying therapies.

Effective design of sustainable energy productivity based on the experimental investigation of the humidification-dehumidification-desalination system using hybrid optimization

Reliable and cost-effective industrial-based sustainable desalination technologies are crucial for efficient and effective desalination of saline water. Seawater desalination emerges as a vital treatment, with humidification-dehumidification identified as a promising technology due to its simple, low-maintenance design and compatibility with renewable energy sources. The present work was developed using a large-scale experimental test rig based on the performance of a humidification-dehumidification system operated by the heat pump. As such, the study presents an advanced approach for optimizing humidification-dehumidification desalination systems using hybrid machine learning optimization techniques. The effectiveness of a neuro-fuzzy model, a decision algorithm based on a boosted tree, and a simple averaging ensemble in enhancing the performance of humidification-dehumidification systems were investigated. Experimental data includes cold water flowrate (L/min), hot water flowrate (L/min), inlet air temperature (°C), inlet cold water temperature (°C), inlet hot water temperature (°C), and inlet air relative humidity (%) as input variables combination and freshwater productivity (L/h), recovery ratio (%) as target variables. Several statistical indicators were used to evaluate the models’ prediction skills; similarly, a 2-dimensional Taylor diagram and error-radial plot were also utilized. In the calibration phase, boosted tree models slightly outperform neuro-fuzzy models, particularly for recovery ratio, demonstrating high accuracy and low bias. However, all models exhibit robust predictive accuracy in the verification phase, especially for recovery ratio, emphasizing their potential in generalizing to unseen data. The result indicated that the boosted tree outperformed others with Nash–Sutcliffe Efficiency = 94 % and 88 % for recovery ratio and freshwater productivity, respectively. The simple averaging ensemble shows marginal improvement with 95 % accuracy for the recovery ratio. Integrating hybrid artificial intelligence optimization with humidification-dehumidification desalination systems marks a transformative step in addressing freshwater shortages. By utilizing cutting-edge machine learning techniques, we achieve a more efficient, accurate, and reliable prediction of desalination performance.

Investigating the contribution of student engagement and foreign language enjoyment to English majors’ TEM4-related positive emotions: A positive reinforcement perspective

With the rise of Positive Psychology, there have been abundant studies concerning positive emotions in English teaching and learning. However, the attention paid to positive emotions related to large-scale English tests as well as the influential factors of such emotions has been scarce. To this end, the present study, from the perspective of Positive Reinforcement, investigated to what extent Student Engagement (SE) and Foreign Language Enjoyment (FLE), as reinforcers, contribute to positive emotions related to Test for English Majors-Band 4 (TEM4). Altogether 580 English majors from universities across China were invited to complete three questionnaires as research instruments. Confirmative Factor Analysis was conducted with AMOS 24.0 to guarantee the construct, discriminant and convergent validity of each instrument, after which structural equation modeling and regression analysis were conducted to explore the contribution of SE and FLE to English majors’ TEM4-related positive emotions. The results of structural equation modeling indicated that both SE and FLE contribute significantly to TEM4-related positive emotions, respectively explaining 44.8 % and 51.8 % of the variance. The results of the present study were discussed, and some implications were offered at the end, concerning efforts that need to be made to promote SE and FLE, which ultimately foster English majors’ TEM4-related positive emotions.

Sliding Shear Failure of Basement-Clamped Reinforced Concrete Shear Walls.

After an earthquake struck Chile in 1985, sliding shear failure was observed in the clamping zone of stabilising walls. Even houses with only four storeys can fail due to sliding shear below basement ceilings. This type of failure has received little attention in the past; however, it leads to premature failure of the clamping effect in the basement box. Bending deformations cannot fully develop, which significantly reduces the seismic safety; it is thus important to learn more about the sliding shear failure of basement-clamped shear walls. The overall behaviour is analysed based on three large-scale shear wall tests. The deformation states around the sliding shear zone are evaluated and a simple estimate is given of when sliding shear failure can occur.

Load–displacement behavior of helical pile using Frustum Confining vessel (FCV) and full-scale testing in Babolsar sand

Nowadays, the use of deep foundations in various civil engineering projects, especially in marine and coastal structures, has been increasing. One type of deep foundations are helical pile foundations, which have gained significant attention in recent decades. This research investigates the load–displacement behavior of different helical pile configurations regarding geometry, i.e., helix diameter and spacing ratio (S/D) in two different soil densities under pullout and compressive loading conditions via the FCV-AUT device. Babolsar sand from the southern coast of the Caspian Sea was utilized for advancing this study. Additionally, four full-scale pile tests were conducted on the Caspian Sea coast under both grouted and non-grouted conditions for pullout and compressive loading. The results indicate that increasing the helix diameter, soil density, and S/D ratio enhances the compressive load-bearing capacity. However, in tensile loading, the ultimate load decreases with an increase in the spacing ratio. Furthermore, the grouting process in the three-helix pile exhibits better performance compared to the two-helix pile, potentially increasing the pile capacity up to 30%. In conclusion, the results obtained from the FCV device, utilizing scale theory, have been generalized to the large-scale pile test results in the field, are representative of in agreement and compatible.

Multi-scale experimental investigation on the structural behaviour of novel nanocomposite/natural textile-reinforced mortars

Natural fibre composites present a very appealing area of research for the structural strengthening of masonry structures. Natural fibres possess several drawbacks, such as low initial stiffness and poor interfacial bond behaviour with inorganic matrices that hinder their functions. Such challenges were overcome by applying an innovative nanocomposite-based impregnation system that was pre-engineered and presented by the authors previously. In this paper, a full experimental study involving pullout, tensile tests and large-scale testing via diagonal compression test was carried out to evaluate the structural efficiency of the developed system. Remarkable improvement at yarn-to-matrix level upon nanocomposite coating was observed. At a medium bond length, yarn rupture was observed, highlighting the great anchorage provided by the nanocomposite coating within the matrix. Coupons cast with the developed system demonstrated outstanding composite behaviour and multicracking under tensile loading. Finally, wallets strengthened with the developed system displayed multiple cracks and maintained high shear capacity (40–60 % of τmax) at remarkably large displacements developed at later stages of the test. Such ductility is crucial for retrofitting masonry structures, especially in seismic areas.

Development of Digital Biology Book on Antioxidant Activity of Red Dragon Fruit Extract (Hylocereus polyrhizus) for Class XI High School Students

With technological advances, digital-based learning resources are needed that can be studied by students independently. The results of the research can be used as a learning resource, one of which is in the form of a digital book. This study aims to determine the quality and feasibility of digital books compiled as learning resources. The type of research is research and development (R&D) which refers to the 4D development model (Define, Design, Development and Disseminate). Data collection techniques with instruments in the form of expert assessment questionnaires and response questionnaires. The research subjects consisted of material experts, media experts, biology teachers and 37 students of SMA N 1 Koba class XI. Product data were analyzed descriptively qualitatively to determine the quality and feasibility of the compiled digital book. The results of research and development (R&D) obtained a Digital Book product for high school students class XI bioprocess material. Product assessment by material experts 88.64% (very good), media experts 86.11% (very good), biology teacher experts 100% (very good). The results of the small-scale test of students were 94.29% (very good) and the large-scale test was 98% (very good). Results Based on this research, digital books are appropriate to be used as a learning resource for class XI cell bioprocess materials.

(Invited) Multi-Modal Solid-State Gas Sensors-Based Breath Analysis System with Convolutional Neural Network (CNN) for Early Detection of Lung Cancer

Currently, digital healthcare technologies based on Internet of Things (IOT) are emerging, and the market for digital healthcare was estimated to be 151.5 billion dollars by 2020. Among the digital healthcare technologies, disease diagnosis is attracting maximum attention. Traditional diagnostic methods, such as radiology and human biological material analysis, have limitations of long study time, high cost, radiation exposure, and pain. Therefore, non-invasive diagnostics methods, for early diagnosis of specific diseases and personal healthcare management, have attracted attention.Breath sensors are one of the promising candidates for the realization of non-invasive diagnosis owing to their ease of use, safety, and cost effectiveness. The exhaled breath analysis technique holds great promise as a non-invasive method for diagnosing lung cancer. In the breath of lung cancer patients, various volatile organic compounds (VOCs) markers are generated during metabolic processes. Analyzing these VOCs markers presents a compelling avenue for early and non-invasive lung cancer diagnosis. Solid state gas sensors capable of measuring specific VOCs are currently being researched and utilized as commercial gas sensors for this purpose. However, the concentration of lung cancer VOCs markers in exhaled breath is commonly very low, and individual commercial gas sensors often lack the necessary sensitivity and selectivity to accurately diagnose lung cancer. In this study, we propose a multimodal solid state electronic biosensor system designed to differentiate lung cancer VOCs characteristics among multiple VOCs, enabling exhalation-based lung cancer diagnosis. Initially, we developed multimodal biosensors using more than 20 gas sensors, consisting of metal oxide semiconductor sensors, electrochemical sensors, photoionized detectors, metalorganic frame sensors and so on. And each exhibiting varying sensitivity profiles for complex gas mixtures. Additionally, we designed a classification algorithm based on convolutional neural networks (CNN) and multi-layered perceptron (MLP) to distinguish between lung cancer patients and healthy individuals using the output from these diverse multimodal gas sensors. To evaluate the system's performance, we collected exhaled air samples from 74 lung cancer patients and 107 normal subjects, conducting clinical trials with our developed system. We did model evaluation with confusion matrix and ROC curve. The results of these tests confirmed that our system achieved a high predictive accuracy of over 95% in classifying lung cancer patients. The simplicity of the sensor configuration in our developed system makes it suitable for large-scale screening tests to identify potential candidates for precise lung cancer diagnosis. This technology has the potential to contribute to reduced national welfare costs and improved patient survival rates through early diagnosis.

Lentiviral vector packaging and producer cell lines yield titers equivalent to the industry-standard four-plasmid process

Lentiviral vector (LVV)-mediated cell and gene therapies have the potential to cure diseases that currently require lifelong intervention. However, the requirement for plasmid transfection hinders large-scale LVV manufacture. Moreover, large-scale plasmid production, testing and transfection all contribute to operational risk and the high cost associated with this therapeutic modality. Thus, we developed LVV packaging and producer cell lines, which reduce or eliminate the need for plasmid transfection during LVV manufacture. To develop a packaging cell line, lentiviral packaging genes were stably integrated by random integration of linearised plasmid DNA. Then, to develop EGFP- and anti-CD19 chimeric antigen receptor-encoding producer cell lines, transfer plasmids were integrated by transposase-mediated integration. Single cell isolation and testing were performed to isolate the top-performing clonal packaging and producer cell lines. Production of LVV that encode various cargo genes revealed consistency in the production performance of the packaging and producer cell lines compared to the industry-standard four-plasmid transfection method. By reducing or eliminating the requirement for plasmid transfection, while achieving production performance consistent with the current industry standard, the packaging and producer cell lines developed here can reduce costs and operational risks of LVV manufacture, thus increasing patient access to LVV-mediated cell and gene therapies.

Microdissected tumor cuboids: a microscale cancer model for large-scale testing that retains a complex tumor microenvironment.

Current cancer disease models fail to faithfully recapitulate key features of the human tumor microenvironment (TME), such as immune and vascular cells, while simultaneously enabling high-throughput drug tests. We have recently developed a precision slicing method that optimizes the yield of large numbers of cuboidal microtissues ("cuboids", ∼(400 µm) 3 ) from a single tumor biopsy. Here we demonstrate that cuboids from syngeneic mouse tumor models and human tumors retain a complex TME, making them amenable for drug and immunotherapy evaluation. We characterize relevant TME parameters, such as cellular architecture, cytokine secretion, proteomics profiles, and response to drug panels in multi-well arrays. Despite the cutting procedure and the time spent in culture (up to 7 days), the cuboids display strong cytokine and drug responses, including to immunotherapy. Overall, our results suggest that cuboids could provide invaluable therapeutic information for personalized oncology applications, and could help the development of TME-dependent therapeutics and cancer disease models, including for clinical trials.

Improving clay-geogrid interaction: Enhancing pullout resistance with recycled concrete aggregate encapsulation

In this study, Recycled Concrete Aggregate (RCA) was employed as a sandwich technique around the geogrid to enhance the pullout resistance of the geogrid in clayey backfills. Large-scale pullout tests were conducted on three configurations: geogrid-reinforced clay, geogrid-reinforced RCA, and geogrid sandwiched between layers of RCA, aimed at investigating pullout resistance and deformation. The experiments encompassed two different geogrid types (designated as G1 and G2), varying normal pressures ranging from 10 to 50 kPa, and RCA layers with thicknesses of 40, 80, 160, and 320 mm. Results from the experiments revealed that the inclusion of RCA layers around the geogrid substantially enhanced pullout resistance, with improvements ranging from 1.5 to 3 times compared to clay specimens. Optimal RCA thicknesses were determined in order to enhance soil-geogrid bonding and pullout resistance. For G1 geogrid, a thickness of 160 mm (equivalent to replacing 25% of clay volume with RCA) was identified as optimal, while for G2 geogrid, an 80 mm thickness (equivalent to replacing 15% of clay volume with RCA) was found to be sufficient. These thicknesses were established to achieve over 80% of the pullout force compared to full RCA specimens.