Multiple Arms Research Articles

Online Learning for Orchestration of Inference in Multi-user End-edge-cloud Networks

Deep-learning-based intelligent services have become prevalent in cyber-physical applications, including smart cities and health-care. Deploying deep-learning-based intelligence near the end-user enhances privacy protection, responsiveness, and reliability. Resource-constrained end-devices must be carefully managed to meet the latency and energy requirements of computationally intensive deep learning services. Collaborative end-edge-cloud computing for deep learning provides a range of performance and efficiency that can address application requirements through computation offloading. The decision to offload computation is a communication-computation co-optimization problem that varies with both system parameters (e.g., network condition) and workload characteristics (e.g., inputs). However, deep learning model optimization provides another source of tradeoff between latency and model accuracy. An end-to-end decision-making solution that considers such computation-communication problem is required to synergistically find the optimal offloading policy and model for deep learning services. To this end, we propose a reinforcement-learning-based computation offloading solution that learns optimal offloading policy considering deep learning model selection techniques to minimize response time while providing sufficient accuracy. We demonstrate the effectiveness of our solution for edge devices in an end-edge-cloud system and evaluate with a real-setup implementation using multiple AWS and ARM core configurations. Our solution provides 35% speedup in the average response time compared to the state-of-the-art with less than 0.9% accuracy reduction, demonstrating the promise of our online learning framework for orchestrating DL inference in end-edge-cloud systems.

A Phase 1b Open-Label Study of Loncastuximab Tesirine in Combination with Other Anticancer Agents in Patients with Relapsed or Refractory (R/R) B-Cell Non-Hodgkin Lymphoma (LOTIS-7)

Background: Patients (pts) with relapsed/refractory (R/R) B-cell non-Hodgkin lymphoma (B-NHL) may not respond well to single-agent therapy. Combining agents with different mechanisms of action may enhance the efficacy of single agents. Loncastuximab tesirine (loncastuximab tesirine-lpyl; Lonca) is an FDA-approved CD19-directed antibody-drug conjugate (ADC) indicated for R/R large B-cell lymphoma and showed significant antitumor activity and manageable toxicity in a phase 2 trial in pts with R/R diffuse large B-cell lymphoma (Lancet Oncol 2021;22(6):790-800). In preclinical xenograft models, Lonca in combination with gemcitabine, polatuzumab vedotin (Pola), or phosphatidylinositol 3-kinase inhibitors showed improved antitumor activity. The safety and activity of Lonca in combination with other established anticancer agents in pts with R/R B-NHL will be evaluated in LOTIS-7. Methods: LOTIS-7 is a phase 1b, open-label, multicenter, multiarm study (NCT04970901) with 2 parts: dose escalation (part 1) and dose expansion (part 2). Approximately 200 pts with B-NHL will be enrolled (part 1, 70 pts; part 2, 130 pts). Part 2 may include DLBCL, high-grade B-cell lymphoma, follicular lymphoma, mantle cell lymphoma, marginal zone lymphoma, and Burkitt lymphoma cohorts. The design of part 2, such as the sample size, population, etc., may be informed by data collected in part 1. For safety, tolerability, and identifying the maximum tolerated dose and/or recommended dose for part 2, primary endpoints are frequency and severity of adverse events (AE), serious adverse events, dose-limiting toxicities during cycle 1, and frequency of dose modifications due to AE. For efficacy, pharmacokinetics, and immunogenicity, secondary endpoints are overall response rate, duration of response, complete response rate, progression-free survival, relapse-free survival, overall survival, Lonca concentrations, and antidrug antibodies to Lonca titers. Multiple arms are planned; currently, enrollment is open to 1 arm, wherein patients will be treated with Lonca + Pola as an outpatient infusion every 3 weeks (Q3W). In part 1, patients will receive Lonca at escalating doses (from 90 µg/kg to 150 µg/kg) and Pola at a dose of 1.8 mg/kg on day 1 of each 3-week cycle. In part 2, patients will receive Lonca in combination with Pola at the maximum tolerated dose and/or recommended dose for expansion if favorable results in part 1 are achieved. The study has a screening period (≤28 days from the day of the first dose), a treatment (Tx) period (cycles Q3W, ≤1 year), and a follow-up period (~Q12W visits, ≤2 years after Tx discontinuation). Treatment may continue for up to 1 year or until disease progression, unacceptable toxicity, or other discontinuation criteria-whichever occurs first. Key inclusion criteria are patients age ≥18 years with a pathologic diagnosis of R/R B-NHL (2016 WHO classification) and the following: those who failed/are intolerant to any approved Tx, received ≥2 systemic Tx regimens, need systemic Tx, have an ECOG performance status of 0-2, and have measurable disease (2014 Lugano Classification). Patients who previously received any study drugs; received a stem-cell transplant within 60 days prior to the start of study drugs; have lymphoma with active CNS involvement, serious skin-related disease, ascites, edema, or effusion; and have significant medical comorbidities are excluded. Results: The study opened in December 2021, and the first patient was dosed in the Lonca + Pola arm in June 2022. Funding: ADC Therapeutics SA; medical writing: CiTRUS Health Group.

Abstract A028: The Early Detection Initiative trial version 2: A platform trial to test novel approaches to pancreatic cancer screening in patients with new onset hyperglycemia and diabetes

Abstract The Early Detection Initiative (EDI) is an innovative study designed to build a platform that will evaluate novel approaches to early detection of resectable pancreatic ductal adenocarcinoma (PDAC) in patients over age 50 with new onset hyperglycemia and diabetes (NOD). This randomized controlled trial (RCT) of algorithm-based screening for PDAC uses a modified Zelen’s design with post-randomization consent. Eligible subjects are identified through electronic medical record (EMR) surveillance and randomized 1:1 to the Observation or Intervention Arm. The Enriching New-onset Diabetes for Pancreatic Cancer (ENDPAC) score, an algorithm that further risk stratifies NOD based on age and changes in weight and glycemic parameters, is calculated in the Intervention Arm. Consenting subjects with ENDPAC >0 have Computerized Tomography (CT) scan imaging for PDAC detection; potential harm, including over-diagnosis, is also estimated. At the time of abstract submission >2000 individuals have been randomized and >50 subjects consented for CT imaging. EDI version 2 adds several innovations in response to challenges encountered and experience gained so far. Most significantly, EDI v2 has a platform design with a common control arm and allows for multiple intervention arms. This opens the door for evaluating future new biomarkers for PDAC early detection in a high–risk NOD population, including new blood-based biomarkers for the early detection of cancer that are now commercially available. EDI v2 focuses primarily on co-efficacy endpoints (PDAC stage shift in ENDPAC >0 group, and in all patients, excluding non-consented patients); effectiveness evaluation becomes a secondary objective. This addresses the challenges of low consent rate (~25%) due to lack of awareness of connection between NOD and PDAC and low acceptance for a novel research screening modality. The analyses of primary endpoints were adjusted to accommodate potential over-diagnosis due to screening and imbalance between ENDPAC >0 and ENDPAC ≤0 groups due to low consent rate and likely imperfect enrichment by ENDPAC, when evaluating all patients. Electronic eligibility checks and consent shorten the recruitment time, a critical factor for stage shift due to rapid progression of PDAC. A pre-randomization screening pilot study is planned to enhance the post-randomization consent rate. Intervention to Control randomization ratio is changed to 2:1, allowing more rapid recruitment and, when factoring the consent rate, doubling the sample size of the observation arm. Through these innovations, EDI v2 maintains the full advantage of Zelen’s design that made the trial feasible in sample size and cost. Simulation studies demonstrate that our approach correctly controls the type-1 error and the trial has adequate statistical power. The EDI trial represents an innovative and contemporary approach to developing and refining an early detection strategy for pancreatic cancer. Citation Format: Ying-Qi Zhao, Suresh T. Chari, Anirban Maitra, Lynn M. Matrisian, Eva E. Shrader, Bechien U. Wu, Avinash Kambadakone, Barbara Kenner, Jo Ann S. Rinaudo, Sudhir Srivastava, Ying Huang, Ziding Feng. The Early Detection Initiative trial version 2: A platform trial to test novel approaches to pancreatic cancer screening in patients with new onset hyperglycemia and diabetes [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A028.

Quantized Fuzzy Cooperative Output Regulation for Heterogeneous Nonlinear Multiagent Systems With Directed Fixed/Switching Topologies.

This article investigates the cooperative output regulation problem for heterogeneous nonlinear multiagent systems subject to disturbances and quantization. The agent dynamics are modeled by the well-known Takagi-Sugeno fuzzy systems. Distributed reference generators are first devised to estimate the state of the exosystem under directed fixed and switching communication graphs, respectively. Then, distributed fuzzy cooperative controllers are designed for individual agents. Via the Lyapunov technique, sufficient conditions are obtained to guarantee the output synchronization of the resulting closed-loop multiagent system. Finally, the viability of proposed design approaches is demonstrated by an example of multiple single-link robot arms.

Continuum robots for endoscopic sinus surgery: Recent advances, challenges, and prospects.

Endoscopic sinus surgery (ESS) has been recognized as an effective treatment modality for paranasal sinus diseases. Over the past decade, continuum robots (CRs) for ESS have been studied, but there are still some challenges. This paper presents a review on the scientific studies of CRs for ESS. Based on the analysis of the anatomical structure of the paranasal sinus, the requirements of CRs for ESS are discussed. Recent studies on rigid robots, handheld flexible robots, and CRs for ESS are presented. Surgical path planning, navigation, and control are also included. Concentric tube CRs and cable-driven CRs have great potential for applications in ESS. The CRs incorporated with multiple replaceable arms with different functions are preferable in ESS. Further study on navigation and control is required to improve the performance of CRs for ESS.

3D-printed cavity backed crossed dipole antenna for high gain, wideband circular polarization in sub-6 GHz

Triple-arm crossed dipole antenna (CPDA) backed by 3D printed cavity exhibits high gain over wideband axial ratio for sub-6 GHz applications is presented. To improve peak gain and axial ratio bandwidth performance, multiple radiating arms are introduced. The arms are cascaded using vacant quarter rings to obtain circular polarization. Unlike the conventional CPDAs that yield single circular polarized (CP) wave, two CP waves produced by double-arm or triple-arm CPDAs enhances overall gain performance across wideband axial ratio. The length and orientation of the multiple arms in the CPDA determine the maximum current generated on the crossed dipole antenna, resulting in an improved gain since two CP waves are combined in the free space. To further increase boresight gain, 3D printed cavity is placed beneath the triple-arm CPDA. Unbalance impedance line presented in this work extends impedance bandwidth of the triple-arm CPDA to cover 3-dB axial ratio bandwidth. For validation purpose, a prototype was fabricated and measured. The measurement results demonstrate that the triple-arm CPDA has a boresight gain of 8.91–11.7 dBic within 3-dB axial ratio bandwidth of 51.4% (3.25–5.5 GHz). The proposed antenna produces a stable gain within sub-6 GHz bands.

Multi-Arm Global Cooperative Coal Gangue Sorting Method Based on Improved Hungarian Algorithm.

The existing multi-manipulator sorting method for gangue that utilizes a multi-task allocation strategy is not satisfactory. The single manipulator working space is fixed, lowering the cooperation degree between the manipulators and leading to a low sorting rate. Therefore, this paper proposes a multi-manipulator cooperative sorting method that can work globally. First, a benefit function based on the sorting time and quality of the gangue is constructed by combining the gangue flow information and the manipulator state. The time parameter is obtained via the manipulator’s dynamic target tracking trajectory planning algorithm based on PID control. Secondly, the benefits matrix is standardized and updated many times to improve the Hungarian algorithm to achieve task allocation, and the initial solution with priority is obtained. Finally, the solutions are analyzed and processed cooperatively in order of priority. The conflicts between multiple robotic arms are eliminated through task cooperation and trajectory cooperation until the sorting task that the robot arm can execute is obtained from the allocation results. Experiments involving different sorting methods were completed on a multi-arm coal and gangue sorting experimental robot platform. The experimental results show that the sorting efficiency of the proposed method is about 10% and 20% higher than that of the fixed space dynamic and designated space fixed points methods, respectively, under different belt speeds. This method can guarantee system benefits, effectively implements cooperative control of multi-manipulator operations in the whole area, and improves the efficiency of coal gangue sorting.

Design and Analysis of a New Deployable Docking Mechanism for Microsatellites

In-orbit docking technology of microsatellites to realize combined reconfiguration has a wide application prospect, such as large antennas and space telescopes. In order to reduce collision impact and improve docking accuracy, a new deployable docking mechanism is proposed based on the slider-crank principle, which has the advantages of smaller volume and larger posture tolerance. To achieve large capture tolerance and increase the success rate of docking, the posture error is analyzed by considering the specific boundary of the position and pose. And a step-by-step cooperative capture strategy is proposed to complete the velocity selection and action matching among multiple capture arms. The reliable docking of posture correction in the docking process is realized by designing the action path of the docking mechanism. The effects of tolerance capture under different initial posture conditions are analyzed by dynamic simulation. The effectiveness and superiority of the step-by-step cooperative capture strategy are valid by comparison with the synchronized capture strategy. The comparison results show that the impact force is reduced by 8% than the synchronized strategy. The capture experiments are carried out to verify the docking performance. The results show the proposed configuration with a step-by-step cooperative capture strategy achieves successfully reliable capture, weak impact, and large posture tolerance under eight extreme initial pose conditions.

Robust cooperative multiple flexible-joint arms control using the q-Bernstein-Schurer operators as the uncertainty approximator: A singular perturbation approach

The position-force tracking issue of an object being moved by collaborative Multiple Flexible-Joint arms, facing dynamic uncertainties, model nonlinearities, and unknown perturbations is studied in this paper. Toward this end, an adaptive control scheme applying the function approximation technique is suggested, enabling the object to track the reference trajectory. This capability arises from the universal approximation property of the FAT-based approaches. Herein, the q-Bernstein-Schurer operators are exploited to disturbances/uncertain dynamic approximations. Since the parameters of the system are not exactly recognized, adaptive rules are suggested for tuning the coefficients of the operator. The Lyapunov stability analysis guarantees uniformly ultimately bounded stability of all the error signals. Two Flexible-Joint manipulators transporting a rigid object are employed to validate the theoretical achievements. The state-of-the-art Chebyshev Neural Network approximator is also used to compare the suggested methodology. The outcomes exhibit the usefulness of the presented approach, handling the system even in the incidence of uncertainties and disturbances needless to the system’s state feedback for function approximation with a low computational burden.

Skin Ulceration and Exposure of Herograft.

A 57 year-old catheter dependent dialysed woman with systemic erithematous lupus and central vein stenosis underwent HeRO graft placement in right internal jugular vein. In the next months she had multiple arm infections and venous outflow component was removed after impossibility to perform second thrombectomy at 14 months. Over time the graft migrated distally (Figure 1), and patient developed skin ulceration and graft exposition (Figure 2). No important weight loss was noted. Arterial graft was removed surgically and she had kidney transplantation. Arterial graft removal when HeRO graft is abandoned should be considered.

A Shared Autonomy Reconfigurable Control Framework for Telemanipulation of Multi-Arm Systems

Teleoperation is a widely adopted strategy to control robotic manipulators executing complex tasks that require highly dexterous movements and critical high-level intelligence. Classical teleoperation schemes are based on either joystick control, or on more intuitive interfaces which map directly the user arm motions into one robot arm's motions. These approaches have limits when the execution of a given task requires reconfigurable multiple robotic arm systems. Indeed, the simultaneous teleoperation of two or more robot arms could extend the workspace of the manipulation cell, or increase its total payload, or afford other advantages. In different phases of a reconfigurable multi-arm system, each robot could act as an independent arm, or as one of a pair of cooperating arms, or as one of the fingers of a virtual, large robot hand. This manuscript proposes a novel telemanipulation framework that enables both the individual and combined control of any number of robotic arms. Thanks to the designed control architecture, the human operator can intuitively choose the proposed control modalities and the manipulators that make the task convenient to execute through the user interface. Moreover, through the tele-impedance paradigm, the system can address complex tasks that require physical interaction by letting the robot mimic the arm impedance and position references of the human operator. The proposed framework is validated with 8 subjects controlling 4 Franka Emika Panda robots with 7-DoFs to execute a telemanipulation task. Qualitative results of the experiments show us the promising applicability of our framework.

Scheduling of Multi-Robot Job Shop Systems in Dynamic Environments: Mixed-Integer Linear Programming and Constraint Programming Approaches

This paper investigates a dynamic scheduling problem within a job shop robotic cell, wherein multiple robotic arms are responsible for material handling in a U-shaped arrangement. Each robotic arm has access to specific workstations based on their distance in the cell layout. Therefore, a part may need to be exchanged between several robots according to its process plan. For this purpose, intermediate buffers are positioned between each pair of consecutive robots. Due to the dynamic nature of the problem, new jobs arrive at unpredictable times, which in turn necessitates rescheduling taking the system’s current state into account. To tackle this problem, firstly, a Mixed-Integer Linear Programming (MILP) model is devised. Secondly, three distinct Speed-up Constraints (SCs) derived from the problem’s inherent characteristics are designed and implemented to accelerate the MILP model’s solving procedure. Afterward, the problem is formulated using Constraint Programming (CP) approach. The performance of the CP model and the MILP model in presence of all possible combinations of the SCs are evaluated and compared through solving various random instances. Next, an analysis is performed on the buffers’ pick-up criterion and how it is affected by the problem’s size. Besides, the impact of changes in the robots’ speed on the productivity of the cell is assessed. Finally, the extent to which the rescheduling priority affects the output of the model is studied.

Optimizing a Bayesian hierarchical adaptive platform trial design for stroke patients

BackgroundPlatform trials are well-known for their ability to investigate multiple arms on heterogeneous patient populations and their flexibility to add/drop treatment arms due to efficacy/lack of efficacy. Because of their complexity, it is important to develop highly optimized, transparent, and rigorous designs that are cost-efficient, offer high statistical power, maximize patient benefit, and are robust to changes over time.MethodsTo address these needs, we present a Bayesian platform trial design based on a beta-binomial model for binary outcomes that uses three key strategies: (1) hierarchical modeling of subgroups within treatment arms that allows for borrowing of information across subgroups, (2) utilization of response-adaptive randomization (RAR) schemes that seek a tradeoff between statistical power and patient benefit, and (3) adjustment for potential drift over time. Motivated by a proposed clinical trial that aims to find the appropriate treatment for different subgroup populations of ischemic stroke patients, extensive simulation studies were performed to validate the approach, compare different allocation rules, and study the model operating characteristics.Results and conclusionsOur proposed approach achieved high statistical power and good patient benefit and was also robust against population drift over time. Our design provided a good balance between the strengths of both the traditional RAR scheme and fixed 1:1 allocation and may be a promising choice for dichotomous outcomes trials investigating multiple subgroups.

1559P Towards more efficient multi-arm exercise trials in oncology: Application of a Bayesian adaptive decision-theoretic approach

Conducting a traditional, adequately powered multi-arm exercise trial is often challenging in terms of time and available resources. Novel, efficient trial designs are needed for rigorous comparison of multiple treatment arms. This study illustrates a Bayesian adaptive decision-theoretic design using a multi-arm exercise oncology trial.

K-Band Circularly Polarized Beam Steerable Reflectarray Enabling Internet of Space: Conceptualization and Validation

Low-cost and lightweight CubeSats have opened new possibilities to realize Internet of Space (IoS), a vision that aims to provide global network access, particularly for remote areas. A key challenge for IoS realization is the development of an antenna that can meet the gain, bandwidth, and polarization requirements for IoS while complying with the small form factor of CubeSats. The challenge is further magnified by the need to dynamically reconfigure the antenna’s beam location to maintain connectivity as the satellite orbits. This work addresses these challenges by proposing a beam steerable reflectarray antenna that can potentially be integrated with CubeSats to enable IoS. A novel reflectarray unit cell containing multiple Archimedean spiral arms is designed to support broadband circularly polarized (CP) operation, and its operational principle is discussed in depth. The unit cell provides four states of phase shift that are electronically switchable, allowing the reflectarray to achieve the required aperture phase for the desired beam scan. Representative reflectarray prototypes are designed, fabricated, and measured. CP beam scan up to 60° in the plane of offset across the targeted frequency band of 17.8–20.2 GHz is achieved.

Hybrid-control arm construction using historical trial data for an early-phase, randomized controlled trial in metastatic colorectal cancer

BackgroundTreatment for metastatic colorectal cancer patients beyond the second line remains challenging, highlighting the need for early phase trials of combination therapies for patients who had disease progression during or following two prior lines of therapy. Leveraging hybrid control design in these trials may preserve the benefits of randomization while strengthening evidence by integrating historical trial data. Few examples have been established to assess the applicability of such design in supporting early phase metastatic colorectal cancer trials.MethodsMORPHEUS-CRC is an umbrella, multicenter, open-label, phase Ib/II, randomized, controlled trial (NCT03555149), with active experimental arms ongoing. Patients enrolled were assigned to a control arm (regorafenib, 15 patients randomized and 13 analysed) or multiple experimental arms for immunotherapy-based treatment combinations. One experimental arm (atezolizumab + isatuximab, 15 patients randomized and analysed) was completed and included in the hybrid-control study, where the hybrid-control arm was constructed by integrating data from the IMblaze370 phase 3 trial (NCT02788279). To estimate treatment efficacy, Cox and logistic regression models were used in a frequentist framework with standardized mortality ratio weighting or in a Bayesian framework with commensurate priors. The primary endpoint is objective response rate, while disease control rate, progression-free survival, and overall survival were the outcomes assessed in the hybrid-control study.ResultsThe experimental arm showed no efficacy signal, yet a well-tolerated safety profile in the MORPHEUS-CRC trial. Treatment effects estimated in hybrid control design were comparable to those in the MORPHEUS-CRC trial using either frequentist or Bayesian models.ConclusionsHybrid control provides comparable treatment-effect estimates with generally improved precision, and thus can be of value to inform early-phase clinical development in metastatic colorectal cancer.

Chromosome-level genome assembly of Plazaster borealis sheds light on the morphogenesis of multiarmed starfish and its regenerative capacity.

Background Plazaster borealis has a unique morphology, displaying multiple arms with a clear distinction between disk and arms, rather than displaying pentaradial symmetry, a remarkable characteristic of echinoderms. Herein we report the first chromosome-level reference genome of P. borealis and an essential tool to further investigate the basis of the divergent morphology.FindingsIn total, 57.76 Gb of a long read and 70.83 Gb of short-read data were generated to assemble a de novo 561-Mb reference genome of P. borealis, and Hi-C sequencing data (57.47 Gb) were used for scaffolding into 22 chromosomal scaffolds comprising 92.38% of the genome. The genome completeness estimated by BUSCO was 98.0% using the metazoan set, indicating a high-quality assembly. Through the comparative genome analysis, we identified evolutionary accelerated genes known to be involved in morphogenesis and regeneration, suggesting their potential role in shaping body pattern and capacity of regeneration.ConclusionThis first chromosome-level genome assembly of P. borealis provides fundamental insights into echinoderm biology, as well as the genomic mechanism underlying its unique morphology and regeneration.

Research on Multiobjective Optimization Algorithm for Cooperative Harvesting Trajectory Optimization of an Intelligent Multiarm Straw-Rotting Fungus Harvesting Robot

In view of the difficulties of fruit cluster identification, the specific harvesting sequence constraints of aggregated fruits, and the balanced harvesting task assignment for the multiple arms with a series-increasing symmetric shared (SISS) region, this paper proposes a multi-objective optimization algorithm, which combines genetic algorithm (GA) and ant colony optimization (ACO) stepwise, to optimize the multiarm cooperative harvesting trajectory of straw-rotting fungus to effectively improve the harvesting efficiency and the success rate of non-destructive harvesting. In this approach, firstly, the multiarm trajectory optimization problem is abstracted as a multiple travelling salesman problem (MTSP). Secondly, an improved local density clustering algorithm is designed to identify the cluster fruits to prepare data for harvesting aggregated fruits in a specific order later. Thirdly, the MTSP has been decomposed into M independent TSP (traveling salesman problem) problems by using GA, in which a new DNA (deoxyribonucleic acid) assignment rule is designed to resolve the problem of the average distribution of multiarm harvesting tasks with the SISS region. Then, the improved ant colony algorithm, combined with the auction mechanism, is adopted to achieve the shortest trajectory of each arm, which settles the difficulty that the clustered mature fruits should be harvested in a specified order. The experiments show that it can search for a relatively stable optimal solution in a relatively short time. The average harvesting efficiency is up to 1183 pcs/h and the average harvesting success rate is about 97%. Therefore, the proposed algorithm can better plan the harvesting trajectory for multiarm intelligent harvesting, especially for areas with many aggregated fruits.

Effectiveness of multistimulation approach on feeding habits of low-birth-weight babies-A randomized control trial.

IntroductionLow- birth- weight neonates face oral feeding difficulties due to hemodynamic instability, immaturity of central nervous systems, and incomplete development of oral functions. Use of several interventions might help in improvement of the feeding ability of neonates. The objective of the study was to evaluate the effect of the multistimulation approach in low-birth-weight babies on the oral feeding performance, oral intake volume, weight gain and transition time from tube to total oral intake.MethodsA Randomized, parallel-group, multiple arm trial study was conducted, and a total of 44 low birth weight babies were randomized into three parallel groups with a 2:1:1 ratio. Babies who are Hemodynamically stable were included in the trial. In two Intervention groups, one received an oral stimulation program, another intervention group received tactile stimulation, and the control group received routine newborn procedures for the same duration of time. Oral feeding performance was determined by Oral Feeding Skills (OFS) on a daily basis for five days after providing ten days of intervention. Neonates were monitored until hospital discharge.ResultsInfants in the stimulation groups had significantly better oral feeding performance than infants in the control group in terms of mean proficiency, transfer rate and overall transfer of feeding volume. There was a substantial increase in mean feeding score, daily weight, oral intake volume, and early transition time in both intervention groups compared to control. There was no significant difference in feeding behaviours between the oromotor and multistimulation groups, but the multistimulation group gained more weight compared to the oromotor group.ConclusionsInfants exposed to the stimulation programme had better feeding skills and a shorter transition period from tube feeding to oral feeding; however, the babies who received multistimulation gained greater weight than babies who received only oromotor stimulation. The study recommends multi stimulation in the form of oromotor, and tactile stimulation can be used as an effective NICU procedure for maintaining an infant's ability to take feeds orally before being discharged from the hospital.

Robust Multi-Robot Trajectory Optimization Using Alternating Direction Method of Multiplier

Wepropose a variant of alternating direction method of multiplier (ADMM) to solve constrained trajectory optimization problems. Our ADMM framework breaks a joint optimization into small sub-problems, leading to a low iteration cost and decentralized parameter updates. Starting from a collision-free initial trajectory, our method inherits the theoretical properties of primal interior point method (P-IPM), i.e., guaranteed collision avoidance and homotopy preservation throughout optimization, while being orders of magnitude faster. We have analyzed the convergence and evaluated our method for time-optimal multi-UAV trajectory optimizations and simultaneous goal-reaching of multiple robot arms, where we take into consider kinematics-, dynamics-limits, and homotopy-preserving collision constraints. Our method highlights an order of magnitude’s speedup, while generating trajectories of comparable qualities as state-of-the-art P-IPM solver.