Single Module Research Articles

A Method for Teaching Biostatisticians to Write and Critique Specific Aims

This article describes the design of a single instructional module on writing and critiquing the Specific Aims (SA) section of a grant proposal for a biomedical research study. It begins by discussing the goals and learning objectives for the module. Then, it discusses the typical structure of a SA section to orient readers to the core content. This method is rooted in using an established framework for scientific writing, which is presented in brief, along with examples of how it applies to writing the SA section. Then, the structure of the instructional module is described, including the exercises for participants. Finally, the utility of this approach for teaching an otherwise difficult-to-teach competency is discussed, as well as how this approach might be extended to similar pedagogic tasks.

Multi-feature enhancement based on sparse networks for single-stage 3D object detection

In the field of autonomous driving, the accuracy and real-time requirements for 3D object detection technology continue to improve, which is directly related to the commercialization process and market popularity of autonomous vehicles. Despite the efficiency of pillar-based coding for onboard systems, it falls short in terms of accuracy and the reduction of incorrect positives. In this paper, we will examine how to solve the problem of high incorrect rate and low accuracy of existing methods. Firstly, a MAP coding module is introduced to optimize previous point cloud feature coding modules, allowing for the efficient extraction of fine-grained features from point cloud data. Then, we introduce an innovative sparse dual attention (SDA) to efficiently filter out irrelevant details in feature extraction, thereby improving the pertinence and efficiency of information extraction. Finally, to address the potential loss of information from single local feature extraction, a local and global fusion module (CTGC) is introduced. Our method has proactively demonstrated its efficiency and accuracy through rigorous experimentation across diverse datasets. Analysis of the results leads to the conclusion that our solutions provide accurate and robust detection results. Code will be available at https://github.com/lcy199905/MyOpenPCDet.git.

Effect of module configurations on the performance of parallel-connected lithium-ion battery modules

To meet the power and energy of battery storage systems, lithium-ion batteries have to be connected in parallel to form various battery modules. However, different single module collector configurations (SCCs) and unavoidable interconnect resistances lead to inhomogeneous currents and state-of-charge (SoC) within the module, thereby significantly reducing the module lifetime and safety. Therefore, the correlation of SCCs to module inhomogeneous currents and SoC are worth investigating. Based on mathematical permutations and combinations calculations, all SCCs are firstly defined and obtained in this paper. Then, two parallel modules are developed in Matlab/Simscape based on fresh cell and degradation cell separately, which are used to quantify the influence of all SCCs. Furthermore, multiple module collector configurations (MCCs) and Urban Dynamometer Driving Schedule (UDDS) working condition are considered in the analysis. Results indicate that both module collectors connected to same edge cell of module cause the most inhomogeneity. The maximum SoC difference under the UDDS is only 46 % of the 0.5C. Moreover, MCCs can reduce inhomogeneous currents and SoC within the parallel module, while not all MCCs perform better homogeneous performance than SCCs. Finally, the effect of changes in SCCs on degradation module inhomogeneous currents and SoC is verified as same as for fresh cells parallel module. Based on the above results, the optimal single module collector configuration of the N cells parallel module is obtained through mathematical analysis, which can greatly improve the currents and SoC homogeneity of the module.

Numerical modeling of an offshore shellfish farm exposed to extreme wave conditions

Shellfish cultivation is a sustainable method of providing human food and can help remove large amounts of CO2 from the atmosphere. Over the last two decades, longline-based structures have dominated farming systems. So far, the innovative technologies for open-ocean shellfish farming remain stagnant and need to be developed. As such, this paper preliminarily studies the operation and survivability abilities of an innovative shellfish farm under extreme wave conditions. To that end, an efficient numerical scheme with a robust implicit finite element method is established. First, the numerical modeling of a single module of the shellfish farm is conducted and the numerical results are verified against physical model tests. Then, the numerical modeling is implemented in a full-scale shellfish farm containing nine floating rafts with suspended lantern nets in a 3×3 configuration exposed to extreme wave conditions. Different angles of wave attack and shellfish rafts with and without lantern nets are fully considered, allowing an assessment of the operation and survivability abilities of the shellfish farm under extreme wave conditions in various situations. The results highlight that the angle of wave attack significantly affected the energy absorption of the mooring system. Moreover, non-linear instability such as subharmonics, which existed in the motion dynamics, can be manipulated to avoid resonant motions. This study provides insights into the evaluation of the safety design of a shellfish farm at both operational and survivability levels. The numerical method can also model other advanced offshore marine structures with multi-modules, such as floating bridges, airports, and even floating energy islands.

Manipulation of high-fidelity sidebands under Large detuning by Floquet technology: application to Multi-Mode Cooling

Abstract The Floquet technology, a powerful way to manipulate quantum states, is employed to drive sidebands transition under large detuning. Our results demonstrate that high fidelities over 99% can be achieved through optimizing suitable modulation frequencies under large detuning. We observe high-fidelity transitions within a high bandwidth by utilizing a single modulation frequency and reveal this capability is due to the emergence of a flat-band structure in the bandwidth range. The key finding of high-fidelity sideband manipulation under large detuning is experimentally confirmed in nuclear magnetic resonance platform. Finally, we propose a new parallel sideband cooling scheme that enables simultaneous cooling of multiple motional modes. This approach improves the cooling rate compared to conventional schemes with fixed laser frequency and power, and eliminates the need for mode-specific addressing. Our Floquet parallel scheme is applicable to any harmonic oscillator system and is not limited by bandwidth in theory.

ВИКОРИСТАННЯ ЕЛЕКТРОННИХ СИСТЕМ ДОКУМЕНТООБІГУ ТА ЗВІТНОСТІ ЛОГІСТИЧНИМИ КОМПАНІЯМИ УКРАЇНИ

The article explores the possibilities and ways of using information technologies for document management and electronic reporting at logistics enterprises. Theoretical approaches to understanding the goals and tasks of logistics information systems in transport technologies have been considered. The main information technology systems that change the logistics industry in modern conditions have been characterized, namely: RPA; chat bots; visualization; Artificial Intelligence; predictive analytics. The electronic technologies used for information provision in Ukrainian practice, the IT-Enterprise ERP system of JSC ‘Ukrposhta’ and the CRM system of the company ‘Epicentr K’ have been analyzed. Their characteristics and advantages of use have been defined. It have been found that the basis of the functional component of the modern information system is a single module of operations between the links of the logistics system, which determines the relationship between the functional divisions of the company (in terms of the implementation of logistics functions), intermediate links and consumers of the company’s products. The implementation of a logistics information system in the work of a logistics enterprise makes it possible: - to increase the efficiency of its work, achieve a high level of service for customers and ensure effective control; - to ensure a modern approach to the management of implementation processes and the organization of various types of events; - to monitor the efficiency of commercial operations; - to conduct an analysis of the profitability of the activity; - to increase the effectiveness of price policy, etc. The problems of the introduction of electronic document flow and reporting systems in Ukraine have been noted: - the problem of interoperability in electronic document flow; - lack of clear standards and requirements for electronic reporting, companies may hesitate to switch to this type of reporting; – constant changes in legislation and requirements can create difficulties for enterprises in implementing stable electronic reporting systems; - inconsistency of electronic reporting formats with global ones; high implementation costs; - concerns about data privacy and security can deter companies from switching to electronic reporting, especially when the security of information on the network is at stake.

Evaluation of DSRD-based pulsers for a Dielectric Wall Accelerator

Silicon (Si) drift step recovery diodes (DSRD) are opening switches that form the basis for nanosecond-scale high voltage (HV) pulsers. These pulsers are crucial to the operation of a dielectric wall accelerator (DWA), a compact particle accelerator design for proton beam radiation therapy. For the DWA to operate properly, the pulser must generate HV pulses with fast rise times and widths on the order of 1 ns at an operating frequency above 1 kHz. Several pulser designs are available that can optimally drive DSRDs. The purpose of this study is to investigate the capabilities of two DSRD-based pulsers in the context of the DWA and the required pulse parameters. The first pulser is based on a magnetic saturation transformer (MST) and a DRSD. The second pulser considered is a multi-module MOSFET-DSRD-based pulser. Additionally, Sentaurus TCAD simulations are used to study the influence of the Si DSRD doping profile on the output pulse shape. The MST-DSRD-based pulser is able to generate higher amplitude pulses than the MOSFET-DSRD-based pulser with a single module (9645 V and 1807 V, respectively). However, the multi-module MOSFET-DSRD-based pulser achieves the maximum pulse amplitude (10.9 kV) compared to MST-DSRD-based pulser (9645 V). The MOSFET-DSRD-based pulser also generates pulses with shorter pulse widths compared to the MST-DSRD-based pulser (minimum of 3 ns vs. minimum of 8.36 ns, respectively). With no compression stage, the MOSFET-DSRD-based pulser (with more than one module) generates pulses with consistently faster rise times (≤3.06 ns) compared to the MST-DSRD-based pulser (≤4.31 ns). With a compression stage, the rise times were comparable between the two pulsers (MOSFET-DSRD: 1.66 ns and MST-DSRD: 1.48 ns). Both pulsers are able to operate at 1 kHz and the MOSFET-DSRD-based pulser up to 10 kHz. Parasitic capacitance and coupling between modules of the MOSFET-DSRD-based pulser affect the pulse through their influence on the forward and reverse pumping duration. Simulations show that the p region of the DSRD should be larger than the n doped region. Simulations including a parasitic capacitance, modelling the electrical connections to the DSRD, reduce the pulse amplitude. Simulations of the pulsers incorporating SiC DSRDs show a reduction in the pulse amplitude and a widening of the pulse width in comparison to the Si DSRDs.

Emerging ZnO Semiconductors for Photocatalytic CO2 Reduction to Methanol.

Carbon recycling is poised to emerge as a prominent trend for mitigating severe climate change and meeting the rising demand for energy. Converting carbon dioxide (CO2) into green energy and valuable feedstocks through photocatalytic CO2 reduction (PCCR) offers a promising solution to global warming and energy needs. Among all semiconductors, zinc oxide (ZnO) has garnered considerable interest due to its ecofriendly nature, biocompatibility, abundance, exceptional semiconducting and optical properties, cost-effectiveness, easy synthesis, and durability. This review thoroughly discusses recent advances in mechanistic insights, fundamental principles, experimental parameters, and modulation of ZnO catalysts for direct PCCR to C1 products (methanol). Various ZnO modification techniques are explored, including atomic size regulation, synthesis strategies, morphology manipulation, doping with cocatalysts, defect engineering, incorporation of plasmonic metals, and single atom modulation to boost its photocatalytic performance. Additionally, the review highlights the importance of photoreactor design, reactor types, geometries, operating modes, and phases. Future research endeavors should prioritize the development of cost-effective catalyst immobilization methods for solid-liquid separation and catalyst recycling, while emphasizing the use of abundant and non-toxic materials to ensure environmental sustainability and economic viability. Finally, the review outlines key challenges and proposes novel directions for further enhancing ZnO-based photocatalytic CO2 conversion processes.

Demonstration of 4-quadrant analog in-memory matrix multiplication in a single modulation

Analog in-memory computing (AIMC) leverages the inherent physical characteristics of resistive memory devices to execute computational operations, notably matrix-vector multiplications (MVMs). However, executing MVMs using a single-phase reading scheme to reduce latency necessitates the simultaneous application of both positive and negative voltages across resistive memory devices. This degrades the accuracy of the computation due to the dependence of the device conductance on the voltage polarity. Here, we demonstrate the realization of a 4-quadrant MVM in a single modulation by developing analog and digital calibration procedures to mitigate the conductance polarity dependence, fully implemented on a multi-core AIMC chip based on phase-change memory. With this approach, we experimentally demonstrate accurate neural network inference and similarity search tasks using one or multiple cores of the chip, at 4 times higher MVM throughput and energy efficiency than the conventional four-phase reading scheme.

Digitally generated high-resolution mid-infrared dual-comb spectroscopy system based on electro-optic modulation.

In this Letter, we propose a high-resolution dual-comb spectroscopy (DCS) in the mid-infrared (MIR) region. A broadband electro-optic frequency comb (EOFC) with a line spacing of 13 GHz is generated in the near-infrared region. The injection locking technique is employed to lock the distributed feedback (DFB) laser to each comb line of the 34 comb lines as the seed laser for the subsequent electro-optic modulation. A dual radio frequency (RF) comb source with a 50 MHz line spacing and a 13 GHz bandwidth drives a single IQ Mach-Zehnder modulator (IQ-MZM), functioning as a single-sideband (SSB) generator and producing a DCS with high spectrum flatness and resolution flexibility. The generated DCS is converted to the MIR region via a nonlinear difference frequency generation (DFG) system. A DCS with a bandwidth of 442 GHz and a resolution of 50 MHz is achieved in the 3.3 µm region, and the figure of merit reaches 2.94×106 H z 12 in a 183.6 ms measurement time.

Compact, Energy Efficient Simultaneous Transmission of Analog and Different Digital Signals With a Single Ring Modulator

Compact, Energy Efficient Simultaneous Transmission of Analog and Different Digital Signals With a Single Ring Modulator

Large Language Model as Parking Planning Agent in the Context of Mixed Period of Autonomous Vehicles and Human-Driven Vehicles

Autonomous vehicles (AVs) are anticipated to revolutionize future transportation, necessitating updates to traffic infrastructure, particularly parking facilities, due to the unique characteristics of AVs compared to Human-Driven Vehicles (HDVs). During the transition period in which AVs and HDVs coexist, adaptable infrastructure is essential to accommodate both vehicle types. Traditional research, typically reliant on complex mathematical models and simulations, faces challenges in adapting to diverse urban settings, requiring substantial time and resources. To address these challenges, a government-level framework was developed, enabling urban planners to quickly and accurately evaluate and optimize existing parking facilities for future AV and HDV coexistence scenarios. The framework integrates a Large Language Model (LLM) to enhance flexibility and efficiency in parking planning throughout the transitional period. Structured guidance is incorporated to enhance decision-making precision and reduce LLM hallucination risks. The flexibility, robustness, and accuracy of the framework were validated through step-by-step and end-to-end testing using real-world datasets. Specifically, the framework achieved 91.1% comprehensiveness and 70.2% consistency in Indicator Selection Module testing, a 68.9% success rate in the Single Indicator Calculation Module, and a 66.7% success rate in end-to-end testing, demonstrating its practical value in supporting cities during AV integration. Finally, the success rates of different LLM agent modules were further explored, along with a comparison of multiple LLMs and an analysis of key issues related to LLM trustworthiness in urban planning applications. The research highlights the potential of LLMs in advancing urban planning processes and optimizing existing infrastructure, contributing to smarter and more adaptable urban environments.

Bias control for optical single-sideband transmitter based on single IQ modulator

The optical in-phase and quadrature modulator (IQM) is typically utilized as an optical single-sideband (SSB) transmitter for its simple structure and low cost. It requires the modulator biases to be slightly offset for generating the optical carrier. However, this poses significant challenges in the bias control circuits, as most off-the-shelf automatic bias control (ABC) modules are designed to stabilize the IQM at its carrier suppression point for coherent optical communication. In this paper, a novel arbitrary ABC scheme is proposed to stabilize the carrier-to-signal power ratio (CSPR) of an optical SSB signal over long-term operation. This scheme uses SSB rather than double-sideband (DSB) sinusoidal signals as the dither signals. It does not violate the minimum phase condition, thus avoiding performance penalties compared to the conventional ABC scheme in which the DSB dither signal is used. We also develop a closed-form expression to reveal the relationship between the control parameter in the ABC algorithm and the CSPR of the optical SSB signal. By this means, the optical SSB transmitter can automatically adjust its bias voltages and stabilize them at a given CSPR value. The experimental results with ∼80 Gb/s optical SSB signal show that our proposed ABC scheme is effective in stabilizing the bias of the IQM at any point and maintains the CSPR value of the optical SSB signal over hours. It is also shown that the proposed method leads to 1 dB improvement in the receiver sensitivity compared with the traditional ABC technique.

Redesign of a virtual reality basic life support module for medical training – a feasibility study

BackgroundHealthcare providers, including medical students, should maintain their basic life support (BLS) skills and be able to perform BLS in case of cardiac arrest. Research shows that the use of virtual reality (VR) has advantages such as improved accessibility, practice with lifelike situations, and real-time feedback during individual training sessions. A VR BLS module incorporating these advantages, called Virtual Life Support, has been developed especially for the medical domain. Virtual Life Support was collaboratively developed by software developers and stakeholders within the field of medical education. For this study, we explored whether the first version of this module capitalised on the advantages of VR and aimed to develop an understanding of barriers to feasibility of use.MethodsThis study was conducted to assess the feasibility of employing Virtual Life Support for medical training and pinpoint potential obstacles. Four groups of stakeholders were included through purposive sampling: physicians, BLS instructors, educational experts, and medical students. Participants performed BLS on a BLS mannequin while using Virtual Life Support and were interviewed directly afterwards using semi-structured questions. The data was coded and analysed using thematic analysis.ResultsThematic saturation was reached after seventeen interviews were conducted. The codes were categorised into four themes: introduction, content, applicability, and acceptability/tolerability. Sixteen barriers for the use of Virtual Life Support were found and subsequently categorised into must-have (restraining function, i.e. necessary to address) and nice to have features (non-essential elements to consider addressing).ConclusionThe study offers valuable insights into redesigning Virtual Life Support for Basic Life Support training, specifically tailored for medical students and healthcare providers, using a primarily qualitative approach. The findings suggest that the benefits of virtual reality, such as enhanced realism and immersive learning, can be effectively integrated into a single training module. Further development and validation of VR BLS modules, such as the one evaluated in this study, have the potential to revolutionise BLS training. This could significantly improve both the quality of skills and the accessibility of training, ultimately enhancing preparedness for real-life emergency scenarios.

Effect of CFTR Modulators on Oxidative Stress and Autophagy in Non-CFTR-Expressing Cells.

The triple combination therapy for cystic fibrosis (CF), including elexacaftor, tezacaftor and ivacaftor (ETI or Trikafta), has been shown to improve lung function and reduce pulmonary exacerbations, thereby enhancing the quality of life for most CF patients. Recent findings suggest that both the individual components and ETI may have potential off-target effects, highlighting the need to understand how these modulators impact cellular physiology, particularly in cells that do not express CF transmembrane conductance regulator (CFTR). We used HEK293 cells, as a cell model not expressing the CFTR protein, to evaluate the effect of ETI and each of its components on autophagic machinery and on the Rab5/7 components of the Rab pathway. We firstly demonstrate that the single modulators Teza and Iva, and the combinations ET and ETI, increased ROS production in the absence of their target while decreasing it in cells expressing the CFTR ∆F508del. This increase in cellular stress was followed by an increase in the total level of polyubiquitinated proteins as well as the p62 level and LC3II/LC3I ratio. Furthermore, we found that ETI had the opposite effect on Rabs by increasing Rab5 levels while decreasing Rab7. Interestingly, these changes were abolished by the expression of mutated CFTR. Overall, our data suggest that in the absence of their target, both the individual modulators and ETI increased ROS production and halted both autophagic flux and plasma membrane protein recycling.

Functional network modules overlap and are linked to interindividual connectome differences during human brain development.

The modular structure of functional connectomes in the human brain undergoes substantial reorganization during development. However, previous studies have implicitly assumed that each region participates in one single module, ignoring the potential spatial overlap between modules. How the overlapping functional modules develop and whether this development is related to gray and white matter features remain unknown. Using longitudinal multimodal structural, functional, and diffusion MRI data from 305 children (aged 6 to 14 years), we investigated the maturation of overlapping modules of functional networks and further revealed their structural associations. An edge-centric network model was used to identify the overlapping modules, and the nodal overlap in module affiliations was quantified using the entropy measure. We showed a regionally heterogeneous spatial topography of the overlapping extent of brain nodes in module affiliations in children, with higher entropy (i.e., more module involvement) in the ventral attention, somatomotor, and subcortical regions and lower entropy (i.e., less module involvement) in the visual and default-mode regions. The overlapping modules developed in a linear, spatially dissociable manner, with decreased entropy (i.e., decreased module involvement) in the dorsomedial prefrontal cortex, ventral prefrontal cortex, and putamen and increased entropy (i.e., increased module involvement) in the parietal lobules and lateral prefrontal cortex. The overlapping modular patterns captured individual brain maturity as characterized by chronological age and were predicted by integrating gray matter morphology and white matter microstructural properties. Our findings highlight the maturation of overlapping functional modules and their structural substrates, thereby advancing our understanding of the principles of connectome development.

Neural network architecture of a mammalian brain

Connectomics research is making rapid advances, although models revealing general principles of connectional architecture are far from complete. Our analysis of 106 published connection reports indicates that the adult rat brain interregional connectome has about 76,940 of a possible 623,310 axonal connections between its 790 gray matter regions mapped in a reference atlas, equating to a network density of 12.3%. We examined the sexually dimorphic network using multiresolution consensus clustering that generated a nested hierarchy of interconnected modules/subsystems with three first-order modules and 157 terminal modules in females. Top-down hierarchy analysis suggests a mirror-image primary module pair in the central nervous system's rostral sector (forebrain-midbrain) associated with behavior control, and a single primary module in the intermediate sector (rhombicbrain) associated with behavior execution; the implications of these results are considered in relation to brain development and evolution. Bottom-up hierarchy analysis reveals known and unfamiliar modules suggesting strong experimentally testable hypotheses. Global network analyses indicate that all hubs are in the rostral module pair, a rich club extends through all three primary modules, and the network exhibits small-world attributes. Simulated lesions of all regions individually enabled ranking their impact on global network organization, and the visual path from the retina was used as a specific example, including the effects of cyclic connection weight changes from the endogenous circadian rhythm generator, suprachiasmatic nucleus. This study elucidates principles of interregional neuronal network architecture for a mammalian brain and suggests a strategy for modeling dynamic structural connectivity.

Computationally effective machine learning approach for modular thermal energy storage design

This research presents an innovative approach that integrates computational fluid dynamics (CFD) and machine learning (ML) for the design and optimization of thermal energy storage (TES) systems. Heat discharging parametric analyses conducted using CFD serve as the basis for training ML models, including linear regression, K-nearest neighbor (KNN) regression, gradient boost regression (GBR), XGBoost, LightGBM, and neural network (NN). NN emerges as the most suitable for predicting time-dependent variations of concrete and heat transfer fluid (HTF) temperatures. The trained ML models offer an efficient alternative to traditional CFD simulations, enabling the prediction of temperatures in concrete thermal energy storage (CTES) modules under varying inlet conditions, velocities, and time. Leveraging these ML models, the research demonstrates the design of modular CTES cascaded systems with multiple modules in series and parallel configurations, significantly reducing computational cost and time by over 99% compared to full-scale CFD simulations. For instance, in predicting 4-hour time-dependent thermal behavior, CFD takes 97 s per data point and 238,500 s for a single module, compared to ML models’ 16-20 ms per data point and around 290 s per module, indicating their efficiency and scalability in predicting thermal discharge, especially for modular CTES system design and optimization. ML models also demonstrate computational efficiency for designing CTES systems involving multiple modules, taking approximately 765 s - 1047 s for various CTES system configurations, indicating their effectiveness over CFD in predicting thermal discharge for modular CTES systems. The integration of CFD and ML provides a streamlined workflow for designing and optimizing CTES systems, reducing computational efforts, cost, and time. Moreover, this workflow can be updated with additional training data to implement it for unique modular designs with different conditions. Such a generalization of this ML-based approach makes it applicable to a wide range of thermal energy storage designs and geometries, offering a promising avenue for future research and development in the field of thermal energy storage.

Dual role of triglyceride structures facilitates anti-tumor drug delivery: Both as a self-assembling module and a responsive module

Small molecule prodrugs self-assembled nano-delivery systems with tumor responsive linkages are emerging as an effective platform. However, the heterogeneity of tumor microenvironment may limit the anti-tumor effect of prodrug nanomedicines with a single response module. Here, we chose disulfide bond as the response module and branched chain alcohol as the self-assembly modification module to construct a single-responsive prodrug. We also constructed a double-responsive paclitaxel prodrug combining triglyceride and disulfide bond, taking into account of the highly expressed lipase and glutathione levels in tumor cells. The results showed that the anti-tumor effect of single-responsive branched chain alcohol modified prodrug nanoparticles was inferior to triglyceride prodrug nanoparticles with dual response modules. The triglyceride structure can not only serve as a self-assembly modification module, but also serve as a response module for intelligent drug release in tumor. Such dual roles will facilitate the efficient delivery of small molecule self-assembled prodrugs to tumor sites.

An Improved Adaptive Sinusoidal Single Phase Shift Modulation for Bidirectional Single-Stage Onboard EV Chargers

An Improved Adaptive Sinusoidal Single Phase Shift Modulation for Bidirectional Single-Stage Onboard EV Chargers