Rigorous Model Research Articles

Abstract 2263: Why does the loss of chromosomes 10 and the gain of 7 co-occur in gliomas

Abstract Introduction: Gliomas, including aggressive forms such as glioblastoma (GBM) and lower-grade gliomas (LGG), present formidable challenges for treatment. Since the late 1980’s it has been known that loss of chromosome 10 (10 loss) and gain of chromosome 7 (7 gain) often co-occur in GBM. The reasons for this frequently co-occurring event are not very clear, with some prior studies speculating on a few driver genes as culprits. In this study, we aim to study and understand the phenomenon of 10 loss and 7 gain in gliomas in a systematic, chromosome-wide level. Methods: Our analysis consists of three main analyses, that bring independent sources of evidence to address our research question: a) Analyzing data from Progenetix, we developed rigorous probabilistic models that elucidate the probability dynamics of 10 loss and 7 gain in gliomas. b) Next, by analyzing hundreds of genomic and transcriptomic samples from TCGA brain cancer patients, and cell lines, we identified a category of clinically relevant genetic interactions termed 'synthetic rescues'. Synthetic rescue (DU-SR) interactions refer to a form of functional interplay in which the detrimental impact on cell fitness caused by the inactivation of a specific gene (termed ‘vulnerable gene’) is compensated by upregulation of another gene (termed ‘rescuer gene’). The enrichment of rescuer genes on 7 for the vulnerable genes on 10 was then tested. c) Utilizing large-scale in vitro essentiality screens via CRISPR technology in central nervous system (CNS) cancer cell lines, we sought to investigate the fitness effects of different possible sequences of chromosomal 10 and 7 loss and gains. Results: The main results emerging from this three-pronged analysis are as follows: 1) We find that 7 gain after 10 loss is a more likely event than the opposite order of loss of 10 after 7 gain whose probability is explained by a random chance model. 2) We show that loss of 10 is enabled by the cells’ ability to readily mitigate its loss, specifically through rescue interactions with genes lying on the amplified 7 chromosome. 3) These findings in the analysis of patients’ tumors are further reinforced by an independent investigation in IDH wild-type CNS cancer cell line essentiality screens. Conclusions: The co-occurrence of 10 loss and 7 gain in gliomas is the most frequent loss-gain co-aneuploidy pair occurring in human cancer. Our analysis presents a new multi-pronged approach to analyze co-occurring aneuploidy events in cancer, showing the long-contemplated chromosomal co-occurrence events in gliomas, a mystery that has persisted for decades, is likely to arise from genetic rescue interactions between many genes that lie on those chromosomes. From a future translational standpoint, it additionally points to key rescuer genes that may be potentially targeted. Citation Format: Nishanth Ulhas Nair, Alejandro A. Schäffer, Michael E. Gertz, Kuoyuan Cheng, Avinash Das Sahu, Gil Leor, Eldad D. Shulman, Kenneth D. Aldape, Uri Ben-David, Eytan Ruppin. Why does the loss of chromosomes 10 and the gain of 7 co-occur in gliomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2263.

Modelling of reaction kinetics in production of hydroxy carboxylic acids by alkaline degradation of cellulosic waste

The reaction kinetics in alkaline degradation of cellulosic materials to monomeric compounds at elevated temperatures was studied. The target compounds are hydroxy carboxylic acids, preferably glucoisosaccharinic acid (GISA), that are valuable platform chemicals. This methodology, which focuses on depolymerizing cellulosic materials and transforming them into hydroxy carboxylic acids, has not been explored in existing literature. To this end, a rigorous mathematical model was developed that considers phenomena at macroscopic level (transformation of crystalline cellulose into amorphous) and at microscopic level (cleavage of glycosidic bonds). Experimental cellulose degradation data in 10 wt-% NaOH and 20–200 °C was correlated with the model. The agreement between the model results and the experimental data confirmed that the process obeys the proposed reaction pathway. Around 80 % of degradation occurs during the reactor warming up period. Analysis of rate constants indicates that GISA is not degraded into smaller hydroxy acids (SHA) at the temperatures studied. Instead, monosaccharides are converted into SHA as soon as they are produced. Conversion of crystalline cellulose into amorphous form was identified as the rate determining step.

Iterative real-time optimization of a membrane pilot plant

In this paper, we describe the implementation and experimental validation of iterative real-time optimization (RTO) at a continuously operated membrane separation pilot plant. The objective is to achieve an economically optimal operation using the transmembrane pressure and the feed flow rate as adjustable operational parameters (set-points for the lower-level plant controllers). In spite of the popularity of RTO based on rigorous nonlinear plant models in the research literature, its practical use so far remains limited to large scale plants in the petrochemical industries. The main issue behind the limited application of RTO schemes is the necessary effort to derive and maintain sufficiently accurate models of the plants. In case of a significant plant-model mismatch, the results are not reliable, the optimal performance is not reached and even systematic violations of constraints may occur. The applicability of RTO could be extended significantly if simple process models sufficed for its successful application. Then an adaptation of the model or of the optimization problem to the real behavior of the plant is necessary to achieve an optimal operation. For this example, we tackle this issue by applying an iterative scheme called modifier-adaptation with quadratic approximation in combination with model adaptation to ensure the convergence of the plant to the true process optimum despite the fact that the model has deficiencies. The experimental results show that, in spite of a considerable plant-model mismatch, the presented RTO scheme is capable of boosting the process economics.

Sensitivity of bias correction step on generating hydrological scenarios

ABSTRACT Significant shifts in hydro-climatic regimes are expected in many parts of the world during the 21st century, affecting the water cycle. Vulnerability, impact, and adaptation studies often use tailored modeling chains to assess the expected effects of climate change, but the robustness of these chains is rarely investigated. This highlights the need for more rigorous evaluation of modeling chains to ensure that they are reliable for informed decision-making processes. To address this gap, we propose a framework for evaluating the sensitivity of hydrological scenario production to the bias correction step. We apply the framework to the Senegal River Basin, using three bias correction methods (linear scale, empirical quantile mapping, and nested bias correction) and three procedures (climate-correction, hydrological-correction, and climate-hydrological-correction). Our results show that the choice of modeling chain has a significant impact on future hydro-climatic trajectories. In particular, the combination of climate-and-hydrological-correction procedures may be optimal when both climate biases and hydrological model errors are significant. Moreover, using multiple bias correction methods can strengthen the ensemble of future hydro-climatic conditions. These findings have implications for vulnerability–impact–adaptation studies and underscore the importance of rigorous modeling chain design and sensitivity analysis.

A Knowledge and Evaluation Model to Support the Conservation of Abandoned Historical Centres in Inner Areas

The planning of interventions aimed at preserving the built heritage of inner areas is a complex process due to the fragility of these contexts. It should stem from adequate knowledge to support the recognition of qualities, resources, and potentials, and the reinterpretation of residual values. From the perspective of an axiological approach to the built heritage, it is possible to foster the resemantization of such values based on a rigorous cognitive model. This research proposed a cognitive model of the built heritage of the historic neighbourhood of Granfonte in Leonforte (Enna). The knowledge model, developed in Excel, has a hierarchical type of structure characterized by domain, classes, properties, and the attribution of values to properties. The approach makes it possible to execute queries that arise from specific relationships between classes. In this study, we developed both simple queries to measure the percentages of certain characteristics of the building units and complex queries for the estimation of aggregate indices to define the degree of transformation and loss of integrity ITI and degradation ID of the building units or to identify those most exposed to the risk of ruination and contagion. The proposed model can be framed within the framework of ontologies supporting structured heritage knowledge.

Achieving carbon neutrality in Africa is possible: the impact of education, employment, and renewable energy consumption on carbon emissions

This paper analysed the causal link between education, employment, renewable energy consumption and carbon emissions in Africa, where there is scant evidence. Relying on panel data obtained from the World Development Indicators for thirty-two African countries covering a period of 19 years, and five panel rigorous regression models, we found that renewable energy, investment in education, school enrolment, net national income per capita, and employment had negative and significant effects on carbon emission, thus increasing these predictors would result in significant reduction in carbon emission in Africa. We identified a bidirectional causality between carbon emissions and net national income per capita, education expenditure and renewable energy consumption, and carbon emissions and employment. Our findings suggest that investment in education, renewable energy, and employment are relevant in mitigating carbon emissions in Africa. We recommend African governments to invest heavily in education, improve school enrolment, environmental education, renewable energy and employment provision to mitigate carbon emissions.Graphical

Forecasting weekly dengue incidence in Sri Lanka: Modified Autoregressive Integrated Moving Average modeling approach.

Dengue poses a significant and multifaceted public health challenge in Sri Lanka, encompassing both preventive and curative aspects. Accurate dengue incidence forecasting is pivotal for effective surveillance and disease control. To address this, we developed an Autoregressive Integrated Moving Average (ARIMA) model tailored for predicting weekly dengue cases in the Colombo district. The modeling process drew on comprehensive weekly dengue fever data from the Weekly Epidemiological Reports (WER), spanning January 2015 to August 2020. Following rigorous model selection, the ARIMA (2,1,0) model, augmented with an autoregressive component (AR) of order 16, emerged as the best-fitted model. It underwent initial calibration and fine-tuning using data from January 2015 to August 2020, and was validated against independent 2000 data. Selection criteria included parameter significance, the Akaike Information Criterion (AIC), and Schwarz Bayesian Information Criterion (SBIC). Importantly, the residuals of the ARIMA model conformed to the assumptions of randomness, constant variance, and normality affirming its suitability. The forecasts closely matched observed dengue incidence, offering a valuable tool for public health decision-makers. However, an increased percentage error was noted in late 2020, likely attributed to factors including potential underreporting due to COVID-19-related disruptions amid rising dengue cases. This research contributes to the critical task of managing dengue outbreaks and underscores the dynamic challenges posed by external influences on disease surveillance.

Optimization of Clustering and Trajectory for Minimizing Age of Information in Unmanned Aerial Vehicle-Assisted Mobile Edge Computing Network.

With the development of the Internet of Things (IoT) technology, massive amounts of sensor data in applications such as fire monitoring need to be transmitted to edge servers for timely processing. However, there is an energy-hole phenomenon in transmitting data only through terrestrial multi-hop networks. In this study, we focus on the data collection task in an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) network, where a UAV is deployed as the mobile data collector for the ground sensor nodes (SNs) to ensure high information freshness. Meanwhile, the UAV is equipped with an edge server for data caching. We first establish a rigorous mathematical model in which the age of information (AoI) is used as a measure of information freshness, related to both the data collection time and the UAV's flight time. Then a mixed-integer non-convex optimization problem is formulated to minimize the peak AoI of the collected data. To solve the problem efficiently, we propose an iterative two-step algorithm named the AoI-minimized association and trajectory planning (AoI-MATP) algorithm. In each iteration, the optimal SN-collection point (CP) associations and CP locations for the parameter ε are first obtained by the affinity propagation clustering algorithm. The optimal UAV trajectory is found using an improved elite genetic algorithm. Simulation results show that based on the optimized ε, the AoI-MATP algorithm can achieve a balance between data collection time and flight time, reducing the peak AoI of the collected data.

Quaia, the Gaia-unWISE Quasar Catalog: An All-sky Spectroscopic Quasar Sample

We present a new, all-sky quasar catalog, Quaia, that samples the largest comoving volume of any existing spectroscopic quasar sample. The catalog draws on the 6,649,162 quasar candidates identified by the Gaia mission that have redshift estimates from the space observatory’s low-resolution blue photometer/red photometer spectra. This initial sample is highly homogeneous and complete, but has low purity, and 18% of even the bright (G < 20.0) confirmed quasars have discrepant redshift estimates (∣Δz/(1 + z)∣ > 0.2) compared to those from the Sloan Digital Sky Survey (SDSS). In this work, we combine the Gaia candidates with unWISE infrared data (based on the Wide-field Infrared Survey Explorer survey) to construct a catalog useful for cosmological and astrophysical quasar studies. We apply cuts based on proper motions and colors, reducing the number of contaminants by approximately four times. We improve the redshifts by training a k-Nearest Neighbor model on SDSS redshifts, and achieve estimates on the G < 20.0 sample with only 6% (10%) catastrophic errors with ∣Δz/(1 + z)∣ > 0.2 (0.1), a reduction of approximately three times (approximately two times) compared to the Gaia redshifts. The final catalog has 1,295,502 quasars with G < 20.5, and 755,850 candidates in an even cleaner G < 20.0 sample, with accompanying rigorous selection function models. We compare Quaia to existing quasar catalogs, showing that its large effective volume makes it a highly competitive sample for cosmological large-scale structure analyses. The catalog is publicly available at 10.5281/zenodo.10403370.

Building a Better Wall: Assessing Children’s Design Technology Learning in Nature-Based Early Childhood Education

The teaching and learning of design technology that occurs in nature-based early childhood education and care (ECEC) contexts such as nature kindergartens remains under-theorised. There is a growing body of scholarship that describes how teaching and learning occurs in these contexts as well as highlighting the benefits for young children learning in the natural environment. Recently, in the perspective of the Australian ECEC sector, how students experience design technology in nature-based contexts (bush kinders, an adaption of the European forest school approach to ECEC) was reported on. Despite design technology being accounted for in bush kinders as part of play-based learning of STEM, assessment of how this learning is supporting student’s comprehension of design technology remains an area for further attention. Often, educators rely solely on observations and anecdotal note taking for assessment which points to a need to support teachers with more rigorous assessment models. This paper adapts an assessment model for science learning and reconsiders it in terms of design technology teaching and learning. The paper’s aim is to support educators to develop children’s deeper understandings of design technology and make learning meaningful in nature-based education settings. Using vignettes, the children’s learning of design technology available in natural surroundings is analysed. This paper proposes that bush kinders are a valuable context for teaching and learning as they allow educators to develop skills to assess children’s design technology knowledge. The analysis of the data and its consideration against one play-based learning assessment model is also valuable in generating a broader narrative that deepens insights into the teaching and learning experience of design technology education in early childhood nature-based contexts.

Anticipating the Side Effects of Educational Reform Using System Dynamics Modeling

There have been countless efforts to improve academic achievement in public schools across the United States, especially in urban school districts. Few efforts, however, use rigorous analytic modeling tools to anticipate and prevent the potential side effects of change. This chapter proposes the application of system dynamics on educational reform efforts by offering a conceptual model, literature review, and research agenda to identify and mitigate the potential side effects of reform. The model is based on interviews and workshops with public school leaders, teachers, and parents in a midsized midwestern urban city. We identify a “capability trap” that may lead to policy resistance with educational reforms. Limitations to this approach are noted, and future areas of research are identified.

Consistent scholarship standards among DNP-prepared faculty needed: Actionable insights

DNP-prepared faculty report challenges and barriers to achieving success in academic roles when criteria for promotion includes scholarship. The purpose of this evidence-based initiative was to explore thoughtful scholarship standards for DNP-prepared faculty which can be adapted and transferred across academic institutions with the goal of elevating faculty scholarship. Given a paucity of available research evidence, a review and synthesis of non-research evidence was conducted. DNP scholarship standards from high-ranking intuitions were critically appraised, and this evidence, along with the diverse and collective expertise of the authors, was translated into recommendations for an inclusive model of rigor for DNP-prepared faculty scholarship. A template for appraising the scholarship of DNP-prepared faculty based on strategic evaluation of impact is included. Academic institutions may use this work to expand the fundamental level of evolving scholarship, determine parameters, and provide clarity and support to DNP-prepared faculty as they seek to progress in rank.

Simultaneous integration of acoustic and environmental data in a graph-based framework for underwater waveguide analysis

Accurate assessment of underwater acoustic propagation relies heavily on understanding variability in the waveguide. Typically, prevailing methods in the field use environmental data exclusively to evaluate water column fluctuations. This study breaks new ground by integrating a graph-based approach, merging insight from acoustics and environmental data to comprehensively analyze ocean fluctuations. The proposed framework leverages data obtained during the Shallow Water ‘06 Experiment (SW06) from a densely measured region in the experimental area. The data used in this work include acoustic broadband signals below 1 kHz from a 48-element L- shape array and environmental measurements from 59 thermistors across 16 mooring arrays, affected by the passing of internal waves. The spatial relationship in the data, facilitated by sensor locations, is harnessed through a graph built upon underlying features from the datasets, providing a nuanced understanding of environmental variability in the water column. The proposed framework is not confined to theoretical constructs; it is substantiated through rigorous model evaluation on the measured data. This validation process robustly demonstrates the generalization power of the proposed architecture, affirming its effectiveness in a highly fluctuating water column and showcasing its potential for advancing underwater acoustic research. [Work supported by ONR Ocean Acoustics program.]

Prediction of Effectiveness and Toxicities of Immune Checkpoint Inhibitors Using Real-World Patient Data.

Although immune checkpoint inhibitors (ICIs) have improved outcomes in certain patients with cancer, they can also cause life-threatening immunotoxicities. Predicting immunotoxicity risks alongside response could provide a personalized risk-benefit profile, inform therapeutic decision making, and improve clinical trial cohort selection. We aimed to build a machine learning (ML) framework using routine electronic health record (EHR) data to predict hepatitis, colitis, pneumonitis, and 1-year overall survival. Real-world EHR data of more than 2,200 patients treated with ICI through December 31, 2018, were used to develop predictive models. Using a prediction time point of ICI initiation, a 1-year prediction time window was applied to create binary labels for the four outcomes for each patient. Feature engineering involved aggregating laboratory measurements over appropriate time windows (60-365 days). Patients were randomly partitioned into training (80%) and test (20%) sets. Random forest classifiers were developed using a rigorous model development framework. The patient cohort had a median age of 63 years and was 61.8% male. Patients predominantly had melanoma (37.8%), lung cancer (27.3%), or genitourinary cancer (16.4%). They were treated with PD-1 (60.4%), PD-L1 (9.0%), and CTLA-4 (19.7%) ICIs. Our models demonstrate reasonably strong performance, with AUCs of 0.739, 0.729, 0.755, and 0.752 for the pneumonitis, hepatitis, colitis, and 1-year overall survival models, respectively. Each model relies on an outcome-specific feature set, though some features are shared among models. To our knowledge, this is the first ML solution that assesses individual ICI risk-benefit profiles based predominantly on routine structured EHR data. As such, use of our ML solution will not require additional data collection or documentation in the clinic.

Heterogeneity in Systematic Reviews of Medical Imaging Diagnostic Test Accuracy Studies

Systematic reviews of medical imaging diagnostic test accuracy (DTA) studies are affected by between-study heterogeneity due to a range of factors. Failure to appropriately assess the extent and causes of heterogeneity compromises the interpretability of systematic review findings. To assess how heterogeneity has been examined in medical imaging DTA studies. The PubMed database was searched for systematic reviews of medical imaging DTA studies that performed a meta-analysis. The search was limited to the 40 journals with highest impact factor in the radiology, nuclear medicine, and medical imaging category in the InCites Journal Citation Reports of 2021 to reach a sample size of 200 to 300 included studies. Descriptive analysis was performed to characterize the imaging modality, target condition, type of meta-analysis model used, strategies for evaluating heterogeneity, and sources of heterogeneity identified. Multivariable logistic regression was performed to assess whether any factors were associated with at least 1 source of heterogeneity being identified in the included meta-analyses. Methodological quality evaluation was not performed. Data analysis occurred from October to December 2022. A total of 242 meta-analyses involving a median (range) of 987 (119-441 510) patients across a diverse range of disease categories and imaging modalities were included. The extent of heterogeneity was adequately described (ie, whether it was absent, low, moderate, or high) in 220 studies (91%) and was most commonly assessed using the I2 statistic (185 studies [76%]) and forest plots (181 studies [75%]). Heterogeneity was rated as moderate to high in 191 studies (79%). Of all included meta-analyses, 122 (50%) performed subgroup analysis and 87 (36%) performed meta-regression. Of the 242 studies assessed, 189 (78%) included 10 or more primary studies. Of these 189 studies, 60 (32%) did not perform meta-regression or subgroup analysis. Reasons for being unable to investigate sources of heterogeneity included inadequate reporting of primary study characteristics and a low number of included primary studies. Use of meta-regression was associated with identification of at least 1 source of variability (odds ratio, 1.90; 95% CI, 1.11-3.23; P = .02). In this systematic review of assessment of heterogeneity in medical imaging DTA meta-analyses, most meta-analyses were impacted by a moderate to high level of heterogeneity, presenting interpretive challenges. These findings suggest that, despite the development and availability of more rigorous statistical models, heterogeneity appeared to be incomplete, inconsistently evaluated, or methodologically questionable in many cases, which lessened the interpretability of the analyses performed; comprehensive heterogeneity assessment should be addressed at the author level by improving personal familiarity with appropriate statistical methodology for assessing heterogeneity and involving biostatisticians and epidemiologists in study design, as well as at the editorial level, by mandating adherence to methodologic standards in primary DTA studies and DTA meta-analyses.

Complex far fields and optical singularities due to propagation beyond tight focusing: combined effects of wavefront curvature and aperture diffraction

All optical systems, which involve the collimation of a reflected, transmitted or scattered wave subsequent to tight focusing, are subject to two kinds of deviations. One is the wavefront curvature due to inaccurate focal placement of the interface or scatterer particle under consideration, and the other is the diffraction caused by the finite lens aperture. In the present paper we explore these phenomena in detail by considering a rigorous simulated model and an appropriate experimental setup. We hence demonstrate the complicated intensity profiles and optical singularity characteristics of the observed far field. Then we describe ways to minimize these deviations in a general experiment. But more importantly, our analysis proves that these deviations by themselves are significant optical phenomena of fundamental interest. The observed complex field profiles have similarities to standard diffraction-limited tight focal fields, though our field detection is different from the standard schemes. This indicates the relevance of these complex fields to a larger class of systems involving wavefront curvature and aperture diffraction. The detailed analysis and results of the present paper already serve as core explorations of these optical phenomena; and we also suggest future research directions where these system aspects can be purposefully created and explored further.

FRP bending-active gridshells: Numerical simulation and model test

Bending-active gridshells are a distinct category of grid structures achieved through elastic bending of initially straight profiles arranged in perpendicular layers and interconnected by hinge joints. These joints permit free in-plane rotation, thereby enabling the progressive transformation from the initial planar configuration to the desired curved shape. Rigorous modelling of hinge joint behavior is crucial for an accurate simulation of bending-active gridshells. Nevertheless, prior numerical studies have relied on oversimplified approximations in this regard. To overcome these limitations, this study developed an explicit dynamic numerical procedure, where the hinge joints are modelled as rigid links characterized by paired nodes that represent the actual hinge length. The proposed procedure incorporates all six nodal degrees of freedom (DoFs). This not only facilitates modelling the spatial behavior of the constituent profiles but, more importantly, allows for independent treatment of the in-plane rotational DoFs of a hinge joint’s paired nodes. Simultaneously, it addresses the joint’s coupling behavior in relation to the translational and out-of-plane rotational DoFs. The proposed procedure was verified through benchmark numerical tests. Furthermore, validation was conducted through forming and loading tests on a 4.60-m-span bending-active gridshell model constructed from carbon fiber-reinforced polymer (CFRP) strips.

The prediction and analysis of heart disease using XGBoost algorithm

Heart diseases remain a global health concern, with their intricate aetiology and multifactorial risk factors making early diagnosis challenging. Recognizing the pressing need for accurate prediction tools, this research ventured into harnessing the power of machine learning, notably the Xtreme Gradient Boosting (XGBoost) algorithm, to fill this gap. The main object is to devise a robust predictive framework capable of early and accurate identification of heart disease. Specifically, our methodology unfolded systematically, beginning with data preprocessing, then delving into incisive feature selection, rigorous model training, and finally, thorough evaluation. This study is meticulously conducted on the heart.csv dataset, a comprehensive repository of cardiovascular data points. The experimental outcomes were nothing short of revelatory. Not only did the XGBoost model manifest superior performance metrics, but its precision also outpaced several contemporary models referenced in existing literature. Ultimately, our findings underscore the profound potential of the XGBoost algorithm in heart disease predictions. Beyond academic intrigue, this research holds tangible implications for healthcare practitioners, potentially offering a novel tool for early interventions and patient management.

A transhumeral prosthesis with an artificial neuromuscular system: Sim2real-guided design, modeling, and control

In this work we introduce a new type of human-inspired upper-limb prostheses. The Artificial Neuromuscular Prosthesis (ANP) imitates the human neuromuscular system in the sense of its compliance, backdrivability, natural motion, proprioceptive sensing, and kinesthetics. To realize this challenging goal, we introduce a novel human-inspired and simulation-based development paradigm to design the prosthesis mechatronics in correspondence to the human body. The ANP provides body awareness, contact awareness, and human-like contact response, realized via floating base rigid-body models, disturbance observers, and joint impedance control—concepts known from established state-of-the-art robotics. The ANP mechatronics is characterized by a four degrees of freedom (dof) torque-controlled human-like kinematics, a tendon-driven 2-dof wrist, and spatial orientation sensing at a weight of 1.7 kg (without hand and battery). The paper deals with the rigorous mathematical modeling, control, design and evaluation of this device type along initially defined requirements within a single prototype only. The proposed systemic and grasping capabilities are verified under laboratory conditions by an unimpaired user. Future work will increase the technology readiness level of the next generation device, where human studies with impaired users will be done.

Generation of THz radiation through molecular modulation in hydrogen-filled hybrid anti-resonant fibers.

We study the generation of narrowband terahertz (THz) pulses by stimulated Raman scattering and molecular modulation in hydrogen-filled hybrid hollow-core fibers. Using a judicious combination of materials and transverse structures, this waveguide design enables simultaneous confinement of optical and THz signals with reasonably low attenuation, as well as high nonlinear overlap. The THz pulses are then generated as the second Stokes band of a ns-long near-infrared pump pulse, aided by Raman coherence waves excited in the gaseous core by the beat-note created by the pump and its first Stokes band. Optimization of the fiber characteristics facilitates phase matching between the corresponding transitions and coherence waves while avoiding coherent gain suppression, resulting in potential optical-to-THz conversion efficiencies up to 60%, as confirmed by rigorous numerical modelling under ideal zero-loss conditions. When the current optical material constraints are considered, however, the attainable efficiencies relax to 0.2%, a still competitive value compared to other systems. The approach is in principle power and energy scalable, as well as tunable in the 1-10 THz range without any spectral gaps, thereby opening new pathways to the development of fiber-based THz sources complementary to other mature technologies such as quantum cascade lasers.