bluuebunny/crossref_metadata_2025 huggingface dataset:
An Improved and Enhanced Depth Map Approach for Obstacle Detection in Unknown Environments for Reactive Autonomous Mobile Robots
Authors: Raju Vomsheendhur , Selekwa Majura F.
Published: January 2025

Interest in autonomous vehicles has attracted research into developing better navigation systems. Autonomous navigation systems guide unmanned vehicles to traverse any environment without human intervention; they require clear knowledge of the environment to make correct decisions. Information about the environment can be obtained from either ranging or vision sensors. Although ranging sensors such as LiDARs, radars, or sonars can detect the presence and location of obstacles, they don’t provide any information on whether the obstacles are traversable. On the other hand, vision sensors produce images similar to how human eyes perceive the environment, which can be processed to determine whether the sensed obstacle is traversable. Since vision sensors are light, less costly, and consume low power compared to LiDARs or Radars, there is an increasing interest in using them in autonomous navigation of unmanned ground vehicles. The main limitation of vision sensors for this purpose is their failure to directly identify the locations of the obstacles. Often, the location is determined by using a depth map, which is not always accurate and can be very computational. This paper proposes a new method of processing a depth map with improved accuracy. The depth of the closest objects to the stereo camera is extracted using the horizontal and vertical field of view of the sensor. It is further enhanced using morphological operations and the P-Tile segmentation technique to segment the obstacle(s) from its background and noise. This approach generates a denser map without any discontinuities, which leads to improved accuracy in locating close-by objects at less computational cost. Information from this depth map can be used as a low-cost alternative to ranging information in the navigation system for ground vehicles.
Machine-Learning Based Curing Cycle Optimization in Wind Blade Manufacturing
Authors: Kamath Sahil , Caselato Gandia Guilherme , Adab Niloufar , Mehrdad Ehsan , Qian Dong , Lu Hongbing
Published: January 2025

The vacuum-assisted resin infusion mold (VARIM) process is widely used in wind blade manufacturing for its cost-effectiveness and reliability. However, the current method faces challenges such as long curing times and defects due to nonuniform heating across the blade structure. To address this, a multi-zone heated bed setup tailored to blade thickness has been considered. However, determining an optimal temperature for each zone poses a computational challenge, which can be tackled with a novel machine-learning approach. Using a digital twin based on a high-fidelity multiphysics solver, a time-distributed LSTM model was trained to understand complex resin curing dynamics. This eliminates the need for costly lab experiments, as the model learns heating patterns and curing behavior efficiently. Once trained, the ML model acts as a digital twin by predicting the degree of cure for a given temperature setpoint with 96.73% accuracy. This model, when used as a surrogate for a Nelder-mead optimization workflow, improves the curing time by roughly 12.5% and presents a more uniform curing rate throughout the part.
Modeling of Turbulent Non-Premixed Combustion Using High Order Compact Finite Difference Method Combined With a Steady Flamelet Approach
Authors: Mahamud Rajib , Hasan Md Kamrul , Haque Md Azazul
Published: January 2025

Non-premixed combustion accounts for most of the real-world applications; however, numerical modeling of non-premixed flames presents significant challenges due to the substantial computational time required to incorporate detailed chemical kinetic mechanisms and to accurately capture the effects of turbulent fluctuations on reaction rates. The proposed research focuses on developing compact finite difference schemes with high-order accuracy that allow for the detailed and accurate capture of small-scale turbulent structures and the steep gradients typical of combustion fronts, without requiring prohibitively dense computational grids. The compact finite difference scheme, exhibiting lower numerical dissipation and dispersion, is well suited for predicting flame propagation and shock waves. The flexibility of such schemes is beneficial for complex boundary conditions and for accurately modeling the combustion chamber geometries and the interactions of flames with walls and obstacles. The numerical discretization of Navier-Stokes-equations, energy equation, and mixture fraction equation using the compact finite difference method was performed on a FORTRAN code for non-premixed diffusion flame. The turbulence model will apply the standard k-ϵ turbulence model. The turbulence chemistry interactions model works on the principle of decoupling the combustion chemistry from the fluid flow through the fundamental assumption of a steady-state flamelet, where the steady flamelet structure was obtained from the combined statistical analysis of mixture fraction and the instantaneous scalar dissipation rate. The steady flamelet model predicts temperature and the species mole fraction using the mixture fraction obtained from the compact finite difference code and coupled it with the probability density function (PDF) model. The PDF method was applied with a beta distribution function to predict the temperature and species mole fraction and integrate the distribution across the mixture fraction and scalar dissipation space using the flamelet library. A counterflow geometry of fuel and oxidizer was accounted for in the modeling of the steady flamelet approach. The flamelet library was generated using a zero-dimensional (0-D) chemical kinetics solver called Cantera for methane-air combustion. The validation of the model will be carried out using experimental data from the literature to establish the accuracy and reliability of the code.
A Robotic Simulation Package: Architecture, Algorithms, and Applications
Authors: Dabiri Arman , Qi Shian , Zhao Shihao
Published: January 2025

This paper presents the development of a robotic simulation package designed to address forward and inverse kinematic problems in multibody dynamic systems. The core solver is enhanced by incorporating the special Euclidean group 𝕊𝕆(3) and quaternions, which are essential for accurately handling orientation representations. This integration allows the simulation to capture rigid body motion and joint constraints precisely. Additionally, the package employs the Moore-Penrose inverse technique and Lagrange multipliers to solve the resulting constrained optimization problem effectively. The software architecture is thoroughly described, highlighting the framework within which the robotic simulation package operates. The package uses an object-oriented design, ensuring a flexible and user-friendly platform. Each module of the software undergoes rigorous unit testing and validation against a variety of multibody systems. Finally, the paper demonstrates the developed package’s motion planning and simulation capabilities through selected robotic systems.
A Reinforcement Learning Approach for Production Scheduling in Industry 4.0 Considering Limited Data
Authors: Lopez-Salazar Camilo , Ekwaro-Osire Stephen , Ekwaro-Osire Henry , Hopfmüller Stephan
Published: January 2025

As the global pursuit of net-zero emissions by 2050 intensifies, the role of renewable energy sources such as solar photovoltaics becomes increasingly relevant. However, their intermittent nature and energy price volatility present challenges for energy-intensive sectors like manufacturing. Integrating renewables into production schedules emerges as a strategy to mitigate costs and environmental impact. Industry 4.0 technologies, particularly machine learning, offer opportunities for optimizing energy consumption. This paper introduces a NeuroEvolution of Augmented Topologies (NEAT) algorithm for energy management operations tailored for industrial prosumers. Inspired by the evolution of biological nervous systems, NEAT evolves a neural network’s topology and parameters to make optimal decisions for a given environment. The study’s objective is to generate a day-ahead energy schedule that considers renewable energy and primary grid utilization using limited information on the system’s current state. A comparative analysis between the NEAT scheduler, a traditional supervised learning-based scheduler, and a deterministic mixed-integer linear programming (MILP) solver as a baseline demonstrates the effectiveness and robustness of the proposed method, which reduced the electricity bill by 18.69% over one week of real data simulation, using just six hours of previous measurements.
Study of the Stresses and Friction During Blink Between the Posterior Surface of the Eye Lid and Anterior Surface of Cornea, Using Finite Element Analysis
Authors: Pai Raghuvir Ballambat , S. Ve Ramesh , Sundaram Meenatchi , Shah Mohammed Abdul Khader , Hazarika Manali , Hariharan Girish , Rao Sohan , Hebbandi Abhilash
Published: January 2025

During the blinking process, the posterior surface of the eye lid interacts with the anterior surface of the cornea, which is a complex tribological system. In this paper, a simplified eye model is used to investigate the interaction between the above two surfaces during blink, using Finite Element Analysis (FEA).The analysis simulates the eye lid motion determined experimentally and available in the literature. The deformation and shear stresses on the ocular surface are recorded. Large deformations are observed during the movement of the eye lid at the point where the peripheral cornea forms the limbus. The large deformations due to the shear stresses is the result of high frictional forces generated due to poor quality of tear film between the interacting surfaces, resulting in dry eye condition. The FEA simulation enables a better understanding of the stresses and friction during the blinking process and therefore the dry eye condition.
Surface Acoustic Wave (SAW) Force Myography Sensor for Muscle Force Output Identification
Authors: Kohler Michael C. , Voiculescu Ioana , Li Fang
Published: January 2025

Precise measurement of muscle force output is fundamental for evaluating physical performance and overall health. It serves as a direct and reliable indicator of muscle strength and functionality, which are indispensable for everyday activities, athletic performance, and rehabilitation. Data on muscle force output also play a pivotal role in controlling prostheses and exoskeletons. This paper presents an innovative approach to capturing muscle force output using Surface Acoustic Wave (SAW) sensors. While not frequently highlighted in this field, SAW sensors are increasingly valued for their versatility and potential to enable self-powered, wireless devices. This research focuses on a 128° YX-cut LiNbO3-based SAW sensor, used here as a force myography sensor to measure muscle contractions. The findings reveal that SAW sensors offer excellent sensitivity and accuracy in tracking muscle activities, potentially surpassing traditional methods like strain gauge measurements and conventional electromyography (EMG).
Electric Propulsion Powerplant Modeling and Simulation for Investigation of Cyclic Rapid Large-Amplitude Transients
Authors: Trejos Miguel , Xiros Nikolaos I. , Aktosun Erdem , Georgiou Ioannis
Published: January 2025

With the expansion of the Arctic and Northwest Passage, the use of icebreaking and ice class vessels outfitted with electric propulsion becomes more and more common. The transportation of cargo following this route offers economic and logistical opportunities. The Northern Sea Route (NSR) is a much shorter marine passage between the Atlantic and Pacific Oceans. Historically being used by several countries for the purposes of resource extraction, the NSR reduces the travel distances by 35–60%. The flexibility of electric propulsion systems makes them ideal for navigation through ice infested waters where rapid, large amplitude transients may arise. In view of the fact that there is often a lack of extensive experimental data in the literature for how these systems operate in changing conditions such as in Artic and Polar regions, simulations are an important resource for analyzing the effects on propulsion operation. The major purpose of this work is to construct a simulation for the investigation of the dynamics of the diesel-electric propulsion system when receiving a large transient input. To this end, the transients are modeled as large, periodic rectangular pulses with noise superimposed over to simulate random fluctuations. The propulsion outfit considered includes at least two screws powered by synchronous three-phase motors which are driven by Variable-Frequency Drives. The integrated power system of the vessel consists of several generators operating in drooped or isochronous mode and driven by a diesel engine prime mover. Single-phase equivalent circuits are employed for the synchronous machines based on a quasi-steady-state approximation. This as well as other approaches are examined through mathematical modeling and dynamic simulation runs.
Simulation Model Development and Calibration of Surface Watercraft Dynamics Using Neural Networks and Machine Learning
Authors: Freels Jonathan Parker , Xiros Nikolaos , Trejos Miguel A.
Published: January 2025

The scale model of a surface watercraft with electric propulsion by a dc motor, propeller and rudder for steering was built. A simulation model that can reproduce motion patterns of the boat with sufficient fidelity in various sets of conditions was developed by compounding data sets acquired through various trials and controlled experiments with established governing principles of ship theory. The aim was to reduce the number and cost of instrumentation required to calibrate the model, without unacceptably compromising precision or fidelity. In effect, by using knowledge of boat dynamics, a custom grey-box model was built and calibrated. A pair of nonlinear state equations, capturing the dynamic response of the watercraft of interest were formulated derived. The dynamical model can form the foundation on which AI-inspired control approaches for navigation of a single or swarm of boats can be built. In the AI-inspired control synthesis approach, the control problem is tackled by concept of virtual physics applying on the boat and resulting in motion schemes that are beneficial in particular applications, e.g. homing or visiting waypoints. The design of such controllers involves cancellation of actual dynamics governing the boat’s motion complies and then implementation of laws of virtual (i.e. artificial) physics. In the present work, as an initial step in this direction, a series of open-loop tests enable us to identify and formulate the dynamics governing the watercraft’s motion.
A Symbolic-Numerical Approach to Suspension Kinematics and Compliance Analysis
Authors: Larcher Matteo , Stocco Davide , Tomasi Matteo , Biral Francesco
Published: January 2025

This paper presents an innovative method for the analysis and simulation of suspension systems through either symbolic or mixed symbolic-numeric computation, which leads to the development of a comprehensive analytical model for the Kinematics and Compliance (K&C) characteristics. State-of-the-art approaches involve either resource-intensive flexible multi-body dynamics or static K&C maps, however, they come with intrinsic limitations. Flexible multi-body systems require substantial computational resources and are unsuitable for real-time applications, whereas K&C maps need frequent regeneration when suspension set-up changes. The proposed approach, rooted in the finite element method and, more specifically in the direct stiffness method, aims at overcoming these challenges. It seeks to create generic analytical K&C models to be employed with suspensions presenting the same topology. Furthermore, this approach achieves a high level of generalization by also taking into account the compliance of bushings, along with the deformation of relative hard points, chassis, and suspension links.
Two-Scale Co-Simulation Approach for Drop-Test Simulation of Kovan PCB Board
Authors: Shejwal Amogh , Medikonda Sandeep , Lyu Dandan , Sengupta Arkaprabha , Mao Henan , Kumar M. Satish
Published: January 2025

Solder joints in PCBs are typically observed to be the weakest links in the drop impact of most consumer electronics products. A drop-shock analysis and particularly the failure of solder joints involves the modeling of meso-scale solder joints and macro-scale chip packages, which is a typical multi-scale problem. Conventional finite element analysis (FEA) approaches using beam elements for the representation of the solders and the one-way sub-modeling technique cannot offer a high-fidelity solution in a reasonable amount of time. The two-scale co-simulation approach discussed in this study provides an efficient analysis tool to enrich Integrated Circuit (IC) packaging models with detailed solder joints so that complex failure modes of solder joints and corresponding structural responses can be accurately captured.

In this context, a detailed solder joint model of a consumer electronics board has been studied using a multi-scale approach in a drop-test scenario. The local solder joint models are coupled to the large-scale global packaging model without the need for a conformal mesh at the coupling interface, this largely simplifies the workflow in both meshing and analysis and provides the flexibility to vary the characteristics of local solder models without the added cost of changing the global model. Under this proposed co-simulation framework, data exchange between the two explicit jobs (at different scales) occurs at the coupling interface synchronously using the Message Passing Interface (MPI). Additionally, this approach eliminates the constraint on time step variation imposed by a mesh-sensitive explicit analysis. The concurrent solder joint models with much smaller time-step sizes run separately from the global model, and communication happens at every global time step resulting in low overhead cost.

The multi-scale analysis has been carried out using the trace-mapped shell PCB. It has been compared against a trace-mapped solid PCB model and a lumped solid PCB model.

The multiscale results show a good correlation with the solid trace-mapped PCB model thus validating this approach. A detailed HPC scalability study and the dependency on the number of processors on the master and slave processes of a multi-scale analysis have also been discussed in detail.
Deep Learning Enabled Approach for Nonlinear Response Modeling of Piezoelectric Energy Harvesters
Authors: Li Dubai , Liao Yabin , Zhang Ruiyang , Lan Chunbo
Published: January 2025

Piezoelectric energy harvesting has been extensively investigated to provide a sustainable power supply for small-scale devices. Though significant progress has been made in the past two decades, accurate modeling of piezoelectric energy harvesters (PEHs) still remains difficult or usually involves parametric identification from experimental results. This paper explores and leverages deep learning techniques for modeling behaviors of nonlinear PEHs. To this end, we present a deep learning-based surrogate model for nonlinear response analysis of PEHs with high complexity and strong nonlinearity. The key concept is to establish a long short-term memory (LSTM) neural network to capture the dynamic properties of the piezoelectric energy harvesters from limited training data and infer the voltage and power given unseen excitations in a data-driven fashion without the need of conventional time-consuming numerical simulation or parametric identification. In addition, the input and output sequences are preprocessed into stacked structures to reduce the complexity and enhance the feature learning. The performance of the proposed approach was successfully demonstrated through numerical data of a monostable PEH of Duffing stiffness nonlinearity and piezoelectric material nonlinearity. The results indicate that the proposed framework is a promising, reliable and computationally efficient approach for nonlinear response modeling of complex piezoelectric energy harvesters.
Laser-Induced Graphene Heater for DNA Amplification and CRISPR-Cas9 Biosensing
Authors: Zhou Peng , McElroy Amber , Xu Yingming , Simon Terrence , Osborn Mark , Cui Tianhong
Published: January 2025

This paper presents an innovative approach by integrating laser-induced graphene (LIG) as a low-power heater and a CRISPR-Cas9 sensor for efficient DNA amplification and detection. LIG, engineered using a laser-induced carbonization technique on polyimide tape, serves as a rapid heater for isothermal DNA amplification, achieving results within 30 minutes with minimal power consumption. Simultaneously, a CRISPR-Cas9 sensor detects DNA concentrations as low as 16 pM, offering cost-effective and sensitive detection. The combination of LIG heaters and CRISPR-Cas9 sensors streamlines DNA amplification and detection processes, offering substantial advantages over traditional methods in terms of power efficiency, cost effectiveness, and application simplicity. We validated our approach using lateral flow assay technology. Further, a pyrolytic glassy carbon electrode-based electrochemical sensor demonstrates specificity in DNA detection, with a detection limit of 16 pM. This integrated approach holds promise for multiple applications, particularly in nucleic acid diagnostics, showcasing a resource-efficient strategy for DNA amplification and detection in molecular biology research.
An Experimental Study of the Thermal Transport Mechanisms in Porous Media for Thermal Protection Systems
Authors: Hassan Mahedi , Amin M. Ruhul , Li Yaofa
Published: January 2025

Thermal protection systems (TPS) are crucial to atmospheric reentry vehicles to shield the intense heat generated by propulsion systems as they enter the atmosphere of planets. Porous materials, among others, have been extensively used to build TPS due to their advantageous characteristics such as low thermal conductivity, lightweight design, high reliability, and cost-effectiveness. Heat transfer through a porous medium includes combined contributions from conduction along solid matrix, radiation across voids (pores), and conduction through gases filling the voids. Although this qualitative picture of the heat transfer mechanisms is well accepted, a quantitative understanding of the contribution of each individual mechanism is still lacking, primarily due to the complex morphology of the porous materials and the intrinsically coupling nature of different mechanisms across scales. To that end, this study aims to experimentally quantify thermal transport mechanisms in porous materials by creatively isolating individual mechanisms. Model porous media were fabricated from silicon and glass wafers employing microfabrication techniques, which were then tested with a customized thermal test apparatus. The results highlight the dominant contribution of gas conduction to the overall heat transfer in the entire test temperature range. However, as the temperature increases, the contribution from radiation has been observed to increase.
Dynamic Response of a Wind and Base Vibration Driven Linear Galloping Energy Harvester
Authors: Yu Bo
Published: January 2025

This paper presents the responses of a linear galloping energy harvester subjected to combined wind and base excitation. The nondimensionalized equations of the linear galloping energy harvester are presented. Compared with other existing studies on concurrent energy harvester, this study utilizes an implicit mapping method to investigate the system’s response characteristics across various excitation frequencies for a given set of system parameters. The stabilities of the periodic responses are also studied through eigenvalue analysis. Our study reveals the conditions under which stable periodic responses occur, and identifies the critical frequencies that lead to transitions between stable and unstable oscillations. Through time domain comparisons, our predictions are validated with numerical simulations. The displacement and voltage responses are illustrated. The findings provide novel insights into the optimal design of piezoelectric energy harvesters.
Developing a Course on Electric Vehicles Modeling for Mechanical Engineers: Challenges and Experiences
Authors: Das Shuvra
Published: January 2025

There is a world-wide push to transform automobile technologies from traditional fossil-fuel based to one that is based on electric drives. Many automotive companies have launched Electric (EV) and Hybrid Electric vehicles (HEV). These vehicles are more fuel-efficient and environmentally friendly compared to conventional vehicles. Optimizing the power intake in EVs allows the vehicle’s operation to be kept within the range designed for best fuel economy and lowest emission, while the motor/generator system provides additional power input as well as generates electricity using the excessive power from the engine. In the case of hybrid electric vehicles (HEVs) the motor/generator system also recovers the kinetic energy during braking or coasting. All these advantages have attracted worldwide development interests for such vehicles. It has also created a need for engineers working in the automotive industry to develop expertise in these systems as they contribute to the design and manufacturing of EVs and HEVs. The mechanical engineering curriculum needs to adjust to this new reality, and it is imperative that graduates of mechanical engineering programs graduate with a decent understanding and knowledge of EVs and HEVs if they are to be employed by the automotive industry. This course was designed with that need in mind and careful attention was paid to designing the content and focus of this course. It needed to be introductory in nature, yet students needed more than just awareness and gain some skill and confidence in the topic. That is why this course covers all aspects of the EV and HEV technology and every topic discussion is followed by modeling exercises. Simscape, a physical modeling toolbox in Matlab, is used for all the modeling work. The simulation exercises are designed with ample scaffolding so that the students can gain proficiency in using the tool while simultaneously accomplish the modeling task assigned. This paper discusses the course content, the simulation exercises, the challenges, and the lessons learned.
Privacy-Preserving In-Situ Monitoring in Additive Manufacturing Through Hyperdimensional Computing
Authors: Piran Fardin Jalil , Poduval Prathyush P. , Errahmouni Barkam Hamza , Imani Mohsen , Imani Farhad
Published: January 2025

Machine learning (ML) models are used for in-situ monitoring in additive manufacturing (AM) for defect detection. However, sensitive information stored in ML models, such as part designs, is at risk of data leakage due to unauthorized access. To address this, differential privacy (DP) introduces noise into ML, outperforming cryptography, which is slow, and data anonymization, which does not guarantee privacy. While DP enhances privacy, it reduces the precision of defect detection. This paper proposes combining DP with Hyperdimensional Computing (HDC), a brain-inspired model that memorizes training sample information in a large hyperspace, to optimize real-time monitoring in AM while protecting privacy. Adding DP noise to the HDC model protects sensitive information without compromising defect detection accuracy. Our studies demonstrate the effectiveness of this approach in monitoring anomalies, such as overhangs, using high-speed melt pool data analysis. With a privacy budget set at 1, our model achieved an F-score of 94.30%, surpassing traditional models like ResNet50, DenseNet201, EfficientNet B2, and AlexNet, which have performance up to 66%. Thus, the intersection of DP and HDC promises accurate defect detection and protection of sensitive information in AM. The proposed method can also be extended to other AM processes, such as fused filament fabrication.
ADArC: Automated Design Tool for Arduino Circuits
Authors: Wohlers Casey , Jin Yangyang , Negron Andres , Tu Le Cam , Buziba Gabriel , Radhakrishnan Pradeep , Brown David C.
Published: January 2025

Experimentation using microcontrollers such as Arduino is becoming common in several university courses and capstone projects due to their low cost. But students often face challenges with microcontrollers, sensors, and actuators. Novice learners in Mechanical, Electrical, and Robotics Engineering struggle with wiring components, coding, and interpreting circuit results. This hinders their progress and leads to time-consuming troubleshooting. Our tool, called ADArC, Automated Design tool for Arduino Circuits, seeks to alleviate this struggle by offering an intuitive software simulator that supports circuit design and Arduino code generation. Users can use the graphical interface to choose components, have them automatically connected on screen to the Arduino, generate and edit code, as well as run and modify circuit simulations in real time. In a class setting, students showed enthusiasm for this app’s design and accessibility, also praising its efficacy in enhancing their learning experiences. The paper will describe the details of the software.
Multi-Directional Mechanical Property Characterization of Additively Manufactured Metal Utilizing Wavefield Analysis
Authors: Cai Bowen , Sun Chuangchuang , Sullivan Rani , Tian Zhenhua
Published: January 2025

The advancement of additive manufacturing has significantly transformed the production process of metal components. However, the unique challenges associated with layer-by-layer manufacturing result in anisotropy in the microstructure and uneven mechanical properties of additive-manufactured metal products. Traditional testing methods often fall short of providing the precise mechanical performance evaluations required to meet industry standards. This paper introduces an innovative approach that combines a nondestructive Lamb wave sensing system with a wavenumber analysis method to characterize the mechanical properties of 3D-printed metal panels in multiple directions. Our method employs piezoelectric actuators (PZT) to generate Lamb waves and utilizes a laser Doppler vibrometer (LDV) for non-contact, two-dimensional grid acquisition of the wavefield. The anisotropic properties of the metal 3D-printed structure will be captured in the wavefield, offering an informative dataset for wavenumber analysis. The proposed analytical method includes multi-directional frequency wavenumber analysis and a least-squares-based dispersion curves regression. The integration of the above advanced analytical tools allows for the accurate characterization of the shear wave velocity and Poisson’s ratio of the plate structure. This precise characterization is crucial for ensuring the structural integrity and consistent mechanical properties of 3D-printed metal components. We validated our method using a 3D-printed stainless-steel plate, demonstrating its capability to effectively characterize the multi-directional mechanical properties of additively manufactured metal plates. We expect that our method can provide a nondestructive, time-efficient, and comprehensive quality control solution for additive manufacturing across various industries.
Cost-effective fabrication of RF AlGaN/GaN HEMTs on surface activated bonding-bonded SiC-SiC substrate
Authors: Jing Guanjun , Xing Xiangjie , Zhang Dongguo , Mu Fengwen , Huang Sen , Wei Ke , Jiang Qimeng , Wang Xinhua , Liu Xinyu
Published: January 2025

High cost of high-purity semi-insulating (HPSI) SiC is a significant barrier to its widespread industrial use as a substrate for RF GaN transistors. This study presents a cost-effective 6 in. SiC-on-SiC (SOS) composite substrate. This novel substrate is created by bonding an HPSI-SiC film onto a more affordable supporting SiC wafer using smart cut and surface activated bonding technologies. This approach not only allows multiple transfers of HPSI-SiC films but also enables the reuse of the supporting substrate, potentially reducing costs by over 60%. The epitaxial AlGaN/GaN heterostructure grown on the SOS substrate exhibited excellent high-resistance properties with minimal RF loss, measuring less than 0.04 dB/mm across frequencies from 30 MHz to 40 GHz. The RF loss characteristics were analyzed using a small signal model, revealing detailed capacitance and resistance behavior. Additionally, an AlGaN/GaN RF device, developed using a 0.12 μm process on the SOS substrate, achieved good electric performance with an output current of 1.3 A/mm and ft/fmax values of 63.3/173.4 GHz. The SOS substrate offers a cost-effective alternative to traditional GaN substrates while maintaining the high standards required for advanced RF electronic devices.
Beneath and beyond frustrated total reflection: A practical demonstration
Authors: Filgueira-Rama Carolina , Doval Alejandro , Arosa Yago , de la Fuente Raúl
Published: January 2025

Frustrated total internal reflection (FTIR) is analyzed from a novel perspective. Unlike similar works, the angle of incidence is used here as the experimental variable instead of the film thickness through which light tunnels. This method makes it possible to visualize not only the phenomenon of FTIR but also the resonance processes that occur for angles of incidence below the critical angle. An affordable straightforward experiment appropriate for undergraduates is presented. The experiment involves measuring the reflection and transmission of light through a pair of prisms separated by an air or water layer, and the results are in fair agreement with theory.
Biological, linguistic, and individual factors govern voice quality
Authors: Kreiman Jody , Lee Yoonjeong
Published: January 2025

Voice quality serves as a rich source of information about speakers, providing listeners with impressions of identity, emotional state, age, sex, reproductive fitness, and other biologically and socially salient characteristics. Understanding how this information is transmitted, accessed, and exploited requires knowledge of the psychoacoustic dimensions along which voices vary, an area that remains largely unexplored. Recent studies of English speakers have shown that two factors related to speaker size and arousal consistently emerge as the most important determinants of quality, regardless of who is speaking. The present findings extend this picture by demonstrating that in four languages that vary fundamental frequency (fo) and/or phonation type contrastively (Korean, Thai, Gujarati, and White Hmong), additional acoustic variability is systematically related to the phonology of the language spoken, and the amount of variability along each dimension is consistent across speaker groups. This study concludes that acoustic voice spaces are structured in a remarkably consistent way: first by biologically driven, evolutionarily grounded factors, second by learned linguistic factors, and finally by variations within a talker over utterances, possibly due to personal style, emotional state, social setting, or other dynamic factors. Implications for models of speaker recognition are also discussed.
Activation and modulation of the host response to DNA damage by an integrative and conjugative element
Authors: McKeithen-Mead Saria , Anderson Mary E. , García-Heredia Alam , Grossman Alan D.
Published: January 2025

Mobile genetic elements help drive horizontal gene transfer and bacterial evolution. Conjugative elements and temperate bacteriophages can be stably maintained in host cells. They can alter host physiology and regulatory responses and typically carry genes that are beneficial to their hosts. We found that ICE
Bs1
, an integrative and conjugative element (ICE) of
Bacillus subtilis
, inhibits the host response to DNA damage (the SOS response). Activation of ICE
Bs1
before DNA damage reduced host cell lysis that was caused by SOS-mediated activation of two resident prophages. Further, activation of ICE
Bs1
itself activated the SOS response in a subpopulation of cells, and this activation was attenuated by the functions of the ICE
Bs1
genes
ydcT
and
yddA
(now
ramT
and
ramA
;
ram
for RecA modulator). Double-mutant analyses indicated that RamA functions to inhibit and RamT functions to both inhibit and activate the SOS response. Both RamT and RamA caused a reduction in RecA filaments, one of the early steps in activation of the SOS response. We suspect that there are several different mechanisms by which mobile genetic elements that generate single-stranded DNA (ssDNA) during their life cycle inhibit the host SOS response and RecA function, as RamT and RamA differ from the known SOS inhibitors encoded by conjugative elements.

IMPORTANCE

Bacterial genomes typically contain mobile genetic elements, including bacteriophages (viruses) and integrative and conjugative elements, that affect host physiology. ICEs can excise from the chromosome and undergo rolling-circle replication, producing ssDNA, a signal that indicates DNA damage and activates the host SOS response. We found that following excision and replication, ICE
Bs1
of
B. subtilis
stimulates the host SOS response and that ICE
Bs1
encodes two proteins that limit the extent of this response. These proteins also reduce the amount of cell killing caused by resident prophages following their activation by DNA damage. These proteins are different from those previously characterized that inhibit the host SOS response and represent a new way in which ICEs can affect their host cells.
Supplemental Material: Regional exhumation of the Laramide Province
Authors: Jepson Gilby , al. et
Published: January 2025

Single-grain apatite fission-track data of samples collected in this study; thermal history models, assumptions, and model fits; published low-temperature thermochronological data from the Laramide Province; and a version of Figure 5 filtered for apatite fission-track data with long confined track lengths (>13 µm).
Measuring the duration of kangaroo mother care for neonates: a scoping review
Authors: Tumukunde Victor Spector , Loucaides Eva , Medvedev Melissa M , Nyirenda Moffat , Lawn Joy , Tann Cally J
Published: January 2025

Objectives

Kangaroo mother care (KMC) is high impact for survival of low birth weight neonates, but there are few rigorous evaluations of duration required for impact. We conducted a scoping review of KMC duration measurement methods and assessed their validation.

Design

Scoping review in accordance with Joanna Briggs Institute guidance for conducting scoping review.

Data sources

MEDLINE, Embase, Cochrane Library, PsycINFO, African Index Medicus, Latin American and Caribbean Health Sciences Literature, ClinicalTrials.gov, International Clinical Trials Registry Platform, International Standard Randomised Controlled Trial Number Registry, Medrxiv and OpenGrey were searched through November 2022.

Eligibility criteria for selecting studies

Publications with primary data on KMC duration were included. We excluded short procedural skin-to-skin care studies.

Data extraction and synthesis

Selection and data abstraction were conducted by two independent reviewers. A data charting form based on the variables of interest was used to abstract data.

Results

A total of 213 publications were included, of which 54 (25%) documented a method of measuring KMC duration. Only 20 publications (9%) provided a detailed description of the duration measurement method, and none reported validity. Most studies used caregiver reports (29, 54%) or healthcare worker observations (17, 31%). Other methods included independent observers and electronic monitoring devices.

Conclusion

Only 9% of KMC studies reporting duration documented the measurement method applied, and no studies were found with documented validation of duration measurement methods. Accurate and comparable data on the dose response of KMC will require duration measurement methods to be validated against a gold standard such as an independent observer.
Blinding assessment in randomised sham-controlled trials of acupuncture:protocol for a systematic survey
Authors: Yang Jiahui , Liu Jiali , Luo Xiaochao , Yao Minghong , Fu Yong , Li Ling , Sun Xin
Published: January 2025

Introduction

Although various sham acupuncture techniques have been employed to ensure blinding in randomised controlled trials (RCTs) of acupuncture, the effectiveness of blinding in these trials and its influence on trial effect size estimates remain unclear. The objectives of this study are the following: (1) to investigate the proportion and study characteristics of sham-controlled trials reporting on blinding assessment, (2) to assess the blinding effectiveness of different types of sham acupuncture, (3) to investigate the relationship between blinding effectiveness and effect sizes in acupuncture RCTs.

Methods and analysis

We will search PubMed and EMBASE from inception to 1 January 2025 to identify RCTs that compared acupuncture with sham acupuncture in humans with any disease or symptom, with no restrictions on language. Paired investigators will independently determine eligibility and use pilot-tested standardised forms for data extraction. We will calculate the proportion of sham-controlled trials that assessed and reported blinding success and conduct descriptive analyses of general study characteristics, acupuncture treatment details, sham acupuncture details and blinding assessments for included trials. We will assess the effectiveness of blinding success using the James blinding index (BI) and Bang BI, and pool data from included trials using random-effects models. We will use Hedges’ g, a standardised mean difference, with its 95% CI, to calculate treatment effects. We will use Pearson’s r correlation coefficient to assess the relationship between blinding effectiveness and trial effect sizes when variable distributions meet the assumptions of normality and linearity; otherwise, we will consider employing non-parametric tests. When sufficient data are available, we will also use random-effects meta-regression to explore the relationship.

Ethics and dissemination

Ethical approval is not required. The findings of this study will be disseminated through peer-reviewed publications, conference presentations and condensed summaries for clinicians, health policymakers and guideline developers regarding the design, conduct, analysis and interpretation of blinded assessment of sham acupuncture RCTs.

Study registration

Open Science Framework (https://doi.org/10.17605/OSF.IO/B3U7K).
Emotion regulation-based internet-delivered cognitive behavioural therapy for premenstrual dysphoric disorder: study protocol for a randomised controlled trial in Sweden
Authors: Hoppe Johanna M. , Weise Cornelia , Kleinstaeuber Maria , Skalkidou Alkistis , Vegelius Johan , Comasco Erika , Gröndal Maria , Kaltsouni Elisavet , Sundström Felicia , Sampaio Filipa , Andersson Gerhard , Buhrman Monica
Published: January 2025

Introduction

Premenstrual dysphoric disorder (PMDD) is a cyclic mood disorder affecting around 2%–5% of women of reproductive age. Pharmacological interventions exist, but many patients with PMDD experience residual symptoms, discontinue medications or refrain from them due to side effects. Thus, non-pharmacological treatments are needed as an alternative or additive treatment strategy. Evidence indicates that cognitive behavioural therapy (CBT) is a promising candidate. However, further research is required to establish its efficacy and identify ways to improve the treatment. Specifically, incorporating components targeting emotional and interpersonal dysregulation could theoretically enhance its effects. Furthermore, increasing the generally low accessibility of CBT for PMDD necessitates scalable and cost-effective ways to deliver treatment. The current study aims to evaluate the effects and cost-effectiveness of an internet-delivered CBT (ICBT) intervention for PMDD incorporating skills training in emotion regulation and interpersonal effectiveness.

Methods and analysis

The study is a parallel two-group randomised controlled trial with 1:1 allocation to 8 weeks of therapist-guided ICBT or a waitlist control condition. Following recruitment and inclusion, 164 individuals aged 18–45 years who fulfil the Diagnostic Manual of Mental Disorders-5 criteria for PMDD will be randomly assigned to the two groups. Primary outcomes are pretreatment to post-treatment group differences in premenstrual symptoms and their impact on everyday life, as well as psychological and functional impairment during the premenstrual phase. Secondary outcomes include treatment effects on quality of life and difficulties in emotion regulation. Long-term treatment effects will be assessed 6 and 12 months postintervention. Data will be analysed using latent Gaussian process modelling and linear mixed models. The economic evaluation will analyse individual-level societal costs and outcomes between trial arms. Recruitment is expected to begin in February 2025, with study completion anticipated by February 2028.

Ethics and dissemination

The study has been approved by the Swedish Ethical Review Authority (2023-00655-01). Results will be disseminated via presentations and publications in international journals and national outlets for clinicians and patients with PMDD.

Protocol version

PS2024_v1.

Trial registration number

NCT06496139.
Effect of photobiomodulation as preventive treatment of diabetic foot ulcer: randomised, controlled, double-blind, clinical trial protocol
Authors: Lourenço Roselene , Perez Silvana Torres , Motta Lara Jansiski , Duran Cinthya Cosme Gutierrez , Padilha Alexandre Rocha Santos , Bussadori Sandra Kalil , Malavazzi Tainá Caroline dos Santos , Horliana Anna Carolina Ratto Tempestini , Mesquita-Ferrari Raquel Agnelli , Fernandes Kristianne Porta Santos
Published: January 2025

Introduction

The prevention of diabetic foot ulcer (DFU) involves the classification of risk, systemic care, regular examinations, foot care, therapeutic education and adjunct treatments. Photobiomodulation (PBM) has been successfully administered for the healing of DFU and its preventive effects have drawn the interest of researchers.

Methods and analysis

The purpose of the study is to assess the effect of PBM for the prevention of DFU through a randomised, controlled, double-blind, clinical trial. Individuals from 18 to 75 years of age of both sexes with type 2 diabetes mellitus (DM) at moderate to high risk of developing DFU will be randomly allocated to two groups of 32 participants each. The PBM group will wear a boot with 1344 light-emitting diodes (LEDs)—504 with a wavelength of 660 nm located on the sides of the boot (28.5 mW, 10 J per LED), 504 with a wavelength of 850 nm also on the sides of the boot (23 mW, 8 J per LED), 168 with a wavelength of 660 nm on the base of the boot (28.5 mW, 10 J per LED) and 168 with a wavelength of 850 nm also on the base of the boot (23 mW, 8 J per LED). The boot will be worn once a day for 6 min over 60 days and the participants will also receive therapeutic education. The control group will wear a non-therapeutic LED boot (sham) under the same conditions and will also receive therapeutic education. Assessments will be performed at the beginning of the study, after 30 days (clinical examination) and after 60 days (clinical examination, assessment of peripheral neuropathy (PN) and peripheral artery disease (PAD), blood and urine examinations and quality of life).

Ethics and dissemination

This protocol received approval from the Human Research Ethics Committee of Nove de Julho University and the Mandaqui Hospital Complex (certificate number: 66098522.0.3001.5511; final approval date: 22 June 2023). The findings will be published in a peer-reviewed journal.

Trial registration number

NCT06353568, ClinicalTrials.gov.