Optimal Setup Research Articles

Electrode Setup for Electromyography-Based Silent Speech Interfaces: A Pilot Study

This paper describes a series of pilot experiments developed to define the electrode setup in order to record a novel parallel electromyography (EMG)–audio database. The main purpose of the database is to provide data useful for the development of an EMG-based silent speech interface for Spanish laryngectomized speakers. Motivated by the scarcity of information in related studies regarding this important decision-making process, we decided to carry out a set of experiments with multiple recording sessions and different setups. We included different electrode types (paired and concentric) and locations targeting different muscles in the face and neck involved in the speech production process. We then analyzed the results obtained in a phone classification task using frame-based phone accuracy. The final setup consists of eight channels with bipolar single-electrode pairs targeting eight specific muscles crucial for capturing speech-related information: the digastric, the depressor anguli oris, the risorius, the levator labii superioris, the masseter, the zygomaticus major, the depressor labii superioris, and the stylohyoid. This setup has been used for the final recordings in the database. By providing insight into the electrode setups that were used in related studies and the optimal setup that resulted from this pilot study, we hope that this research will help future researchers in the field in determining their experimental setup.

KiDS-Legacy: Angular galaxy clustering from deep surveys with complex selection effects

Photometric galaxy surveys, despite their limited resolution along the line of sight, encode rich information about the large-scale structure (LSS) of the Universe thanks to the high number density and extensive depth of the data. However, the complicated selection effects in wide and deep surveys can potentially cause significant bias in the angular two-point correlation function (2PCF) measured from those surveys. In this paper, we measure the 2PCF from the newly published KiDS-Legacy sample. Given an r-band $5σ$ magnitude limit of $24.8$ and survey footprint of $1347$ deg^2, it achieves an excellent combination of sky coverage and depth for such a measurement. We find that complex selection effects, primarily induced by varying seeing, introduce over-estimation of the 2PCF by approximately an order of magnitude. To correct for such effects, we apply a machine learning-based method to recover an organised random (OR) that presents the same selection pattern as the galaxy sample. The basic idea is to find the selection-induced clustering of galaxies using a combination of self-organising maps (SOMs) and hierarchical clustering (HC). This unsupervised machine learning method is able to recover complicated selection effects without specifying their functional forms. We validate this SOM+HC method on mock deep galaxy samples with realistic systematics and selections derived from the KiDS-Legacy catalogue. Using mock data, we demonstrate that the OR delivers unbiased 2PCF cosmological parameter constraints, removing the $27σ$ offset in the galaxy bias parameter that is recovered when adopting uniform randoms. Blinded measurements on the real KiDS-Legacy data show that the corrected 2PCF is robust to the SOM+HC configuration near the optimal set-up suggested by the mock tests.

Investigation on geometric, dimensional accuracy and surface roughness for 3D printed PLA via fused deposition modelling (FDM) process

Fused deposition modeling (FDM) is an advanced additive manufacturing technique known for its precision and repeatability, extensively applied in producing automotive and industrial components. This study employs the Taguchi L9 orthogonal array (OA) approach to optimize process parameters for minimizing dimensional inaccuracies, geometric deviations, and surface roughness (Ra) in polylactic acid (PLA) specimens fabricated as per ASTM D638 Type V standards. Four critical printing parameters: extrusion temperature (ET), infill pattern (Ip), print rate (Rp), and layer height (LH) are investigated, focusing on specimen thickness (Ts), width of the grip section (Wgs), overall length (OL), circularity (Circ), cylindricity (Cyl), surface roughness (Ra), and waviness (Wa). Signal-to-noise (S/N) ratio analysis identified optimal parameter combinations, revealing that Ip, ET, and Rp significantly influence circularity by 39.24%, 37.34%, and 15.19%, respectively. One of the important factors that affect the degree of inaccuracy in cylindricity is the Ip. Gyroid pattern generated the least amount of inaccuracy out of all the patterns that were tested. Layer height (LH) at 0.1 mm was the most critical factor affecting Ra and Wa, while an extrusion temperature of 215°C predominantly influenced Ts. The Ip has the most affected the grip section width. In contrast to expectation, the triangle Ip achieved the minimum width error that was 0.3384%. The Rp of 40 mm/s has the highest impact on the OL deviations. Out of all the other patterns, the Tri-Hexagon (Ip) has made the least contribution to the OL deviations. Rp exhibited a lower contribution to Ra (2.35%) and Wa (12.45%). The results demonstrate the effectiveness of the Taguchi approach in identifying optimal process setups for enhancing the precision, geometric accuracy, and surface quality of 3D-printed specimens.

Tibetan–Chinese speech-to-speech translation based on discrete units

Speech-to-speech translation (S2ST) has evolved from cascade systems which integrate Automatic Speech Recognition (ASR), Machine Translation (MT), and Text-to-Speech (TTS), to end-to-end models. This evolution has been driven by advancements in model performance and the expansion of cross-lingual speech datasets. Despite the paucity of research on Tibetan speech translation, this paper endeavors to tackle the challenge of Tibetan-to-Chinese direct speech-to-speech translation within the multi-task learning framework, employing self-supervised learning (SSL) and sequence-to-sequence model training. Leveraging HuBERT model to extract discrete units of target speech, we develop a speech-to-unit translation (S2UT) model using an encoder-decoder architecture which subsequently generates speech output through a unit-based vocoder. By employing SSL and utilizing discrete representations as training targets, our approach effectively captures linguistic differences, facilitating direct translation between the two languages. We evaluate the performance of HuBERT model under various configurations to select the optimal setup based on Phone-unit Normalized Mutual Information (PNMI) values. After fine-tuning the chosen HuBERT model on specific corpora, we introduce auxiliary tasks to enhance translation performance. This underscores the pivotal role of multi-task learning in improving overall model efficacy. Experimental results validate the feasibility of Tibetan-to-Chinese S2ST, demonstrating promising translation quality and semantic content preservation, despite limited data availability.

Stock Price Forecasting Using Long Short-Term Memory Networks: A Comprehensive Analysis of Financial Time-Series Data

In this study, we employ Long Short-Term Memory (LSTM) networks to predict stock prices using historical data from Tesla Inc. spanning 10 years. The research emphasizes the importance of LSTM's capability to model complex temporal dependencies in financial time-series data, outperforming traditional statistical methods. Various feature combinations and time steps are tested, identifying a 30-day window as the optimal setup. Key evaluation metrics, including Mean Squared Error (MSE), training loss, and validation loss, are utilized to assess model performance. The results show that LSTM networks are particularly effective for short-term stock price predictions, while longer-term forecasts experience decreasing accuracy. This work contributes to the growing body of knowledge on deep learning applications in financial markets, offering practical insights for investors and financial institutions

Assessing the number and placement of ground control points in low-cost UAV photogrammetry

This study examines the impact of the number and placement of Ground Control Points (GCPs) on the accuracy of photogrammetric results using low-cost UAVs. The relevance of this research stems from the growing popularity and applicability of UAV photogrammetry, particularly in areas with diverse terrains and requirements for precise spatial data. The experiment leverages modern photogrammetric techniques, including Structure from Motion (SfM) algorithms, to analyze how GCP configurations affect error distribution and model accuracy. The study's first phase focused on evaluating the influence of GCP placement at varying heights. A photogrammetric survey was conducted at the Kyiv Hippodrome, utilizing terrestrial laser scanning (TLS) to establish high-precision coordinates for control points. The data acquisition involved the DJI Phantom 4 Pro V2 UAV, with multiple flight missions capturing images at 30-degree camera deviations from the nadir. Points were systematically analyzed by alternating their roles as GCPs and Control Points (CPs). The results demonstrated that errors significantly increased when CPs were located further from the UAV camera, emphasizing the need for proximity in GCP placement. The second phase analyzed how GCP configurations and quantities influence photogrammetric model accuracy. By forming 12 groups of GCPs, each varying in distribution and number, the study identified optimal setups for minimizing errors. Groups with evenly distributed points across the survey area, comprising at least eight GCPs, exhibited the lowest root mean square errors (RMSE). Conversely, configurations with GCPs concentrated along a single side or solely on the survey area's edges resulted in substantial inaccuracies. Key findings reveal that an effective GCP placement strategy involves prioritizing proximity to the UAV camera and achieving even distribution across the surveyed area. Additionally, configurations with fewer than eight GCPs tend to suffer from sharp declines in accuracy. The research underscores the importance of balancing GCP quantity and placement for achieving reliable photogrammetric outputs in low-cost UAV applications

Effective Implementation of Shariah Compliance Culture in Islamic Banks

Shariah governance and Shariah compliance culture are fundamental to the operational integrity and ethical conduct of Islamic banks. While Shariah governance provides oversight mechanisms, Shariah compliance culture integrates Shariah principles and values into the organization’s ethos. This study examines past researches on Shariah governance and its effectiveness in fostering the Shariah compliance culture within Islamic banks. The qualitative methodology employed in this paper utilizes content analysis, with data being analysed descriptively. The literature review underscores the importance of Shariah governance, the significance of Shariah compliance culture and the factors influencing it. The findings suggest that Shariah governance has a major influence in nurturing the Shariah compliance culture in Islamic banks, with its effectiveness measurable through six (6) key aspects namely leadership practices, main Shariah organs, policies implementation, regular communication, proper reporting, and education and training program. Further research is needed to assess whether the current Shariah governance structure is adequate in promoting Shariah compliance culture as well as to identify optimal Shariah governance setups that could further enhance Shariah compliance culture and practices.

Features of Terrestrial Laser 3D Scanning Technology for Pavement Condition Assessment

Abstract. Problem. Traditional methods of road condition monitoring have limitations in terms of data accuracy and completeness, particularly in the detection of surface defects. There is a need for methods that provide accurate, rapid data collection to support effective maintenance and repair planning. Goal. The goal of this study is to develop and optimise a methodology for terrestrial 3D laser scanning and to provide practical recommendations for its application in road surface monitoring. Methodology. The proposed methodology examines the effectiveness of terrestrial laser scanning on road segments, addressing parameters such as optimal station count, placement and scanning mode to ensure accurate and comprehensive data collection. The Trimble TX6 scanner is used as an example, with specifications including a range of up to 120 metres, 2 mm accuracy and an adjustable scanning speed of up to 500,000 points per second. Results. The research identifies optimal setups for different road segments, covering short (~100–150 m) and long (up to 1000 m) sections, as well as complex road infrastructure such as multi-level interchanges. The results demonstrate the ability of the 3D scanning method to detect various linear and areal pavement defects. Originality. This study advances the field by developing specific recommendations for station positioning and scanning protocols, addressing blind spots, and ensuring data integrity for 3D modelling. Practical value. The application of this technology supports detailed analysis of pavement condition, significantly increasing the efficiency of data processing and improving repair planning and material estimation. The results are beneficial for transport infrastructure management, enabling more reliable monitoring and maintenance practices

History of contrast enhanced ultrasound (CEUS).

The 50th year of the European Federation of Societies in Ultrasound in Medicine and Biology (EFSUMB) has been celebrated 2022 publishing articles on the history of US. Contrast enhanced ultrasound (CEUS) allows to visualize blood flow and tissue perfusion. CEUS has proven to be safe without risk of nephrotoxicity. The availability of a contrast agent (tracer) for ultrasound imaging allows for the first time a dynamic assessment of tissue perfusion (blood flow and wash-in/wash-out pattern) which is an essential part for the detection and characterisation of pathological tissue and abnormal organ function. It was an outstanding achievement of academic centers in close cooperation with EFSUMB to investigate and validate the clinical potential of this new technology for the diagnosis and monitoring of various diseases and to develop clinical guidelines based on an in-depth assessment of the existing scientific publications. An important part of the implementation of CEUS in clinical practice was the development of contrast-specific imaging modes on the ultrasound scanners (in close cooperation with the machine manufacturers), the optimization of the machine setups for contrast imaging and the education provided to clinical users in form of workshops, webinars, textbooks and scientific congresses.

Robot-assisted radical prostatectomy with the Versius surgical platform: An objective criticism and a guide for an optimal surgical setup

ABSTRACT Objectives This was an observational study aiming at providing a guide for an optimal setup of the Versius robotic system and evaluating its performance in robot-assisted radical prostatectomy (RARP). Patients and methods Between July 2022 and December 2022, all patients with pathologically confirmed prostate cancer candidates for radical prostatectomy were included. Patients who had previous abdominal surgeries or were unfit for pneumoperitoneum were excluded. The preoperative, intraoperative, and postoperative data were prospectively collected. RARP was performed through 5 ports (12 mm); 3 for the robotic arms, 1 for the camera, and 1 for the assistant surgeon. Repeated adjustments of the patient’s position and port distribution along with the bedside unit’s configuration, arrangement, and orientation were made until an optimal setup was achieved. The technical malfunctions were identified in each case and fixed in the subsequent ones. All patients were invited to the follow-up clinic for routine visits on the first and second weeks following surgery and then every month for three months. Results Thirty patients underwent the procedure without conversion to laparoscopy or open surgery and abided by the follow-up regimen. The first nine cases required frequent setup adjustments due to recurring alarms and arms-related conflicts. Thenceforth, the mean docking time, console time, urethro-vesical anastomosis time, and total operative time were enhanced in the last 21 cases without system alarms. No major intraoperative complications related to the robotic system utilization were reported. The postoperative course of all participants passed uneventfully. The median length of the hospital stay and catheterization time were 2 (1–2) and 7 (7–10) days, respectively. Conclusion The Versius system offers a promising robotic platform with a flexible surgical setup. The proposed setup provides a guide for a smooth performance in RARP with minimal instruments’ collision to eschew system failure. Being the first generation of this surgical robotic system, future efforts are still needed to improve its performance and minimize its drawbacks.

Advanced beamforming and reflection control in intelligent reflecting surfaces with integrated channel estimation

AbstractIntelligent Reflecting Surfaces (IRS) enhance wireless communication by optimising signal reflection from the base station (BS) towards users. The passive nature of IRS components makes tuning phase shifters difficult and direct channel measurement problematic. This study presents a machine learning framework that directly maximises the beamformers at the BS and the reflective coefficients at the IRS, bypassing conventional methods that estimate channels before optimising system parameters. This is achieved by mapping incoming pilot signals and data, including user positions, with a deep neural network (DNN), guiding an optimal setup. User interactions are captured using a permutation‐invariant graph neural network (GNN) architecture. Simulation results show that implicit channel estimation method requires fewer pilots than standard approaches, effectively learns to optimise sum rate or minimum‐rate targets, and generalises well. Specifically, the sum rate for GDNNet (GNN + DNN) improves by over linear minimum mean square error (LMMSE) and by over perfect CSI concerning the number of users, and by over LMMSE and by over perfect CSI concerning pilot length. Offering a feasible solution with reduced computing complexity for real‐world applications, the proposed GNN + DNN method outperforms conventional model‐based techniques such as LMMSE and approaches the performance of perfect CSI, demonstrating its high effectiveness in various scenarios.

Multi-Objective Optimization of the Surface Grinding Process for Heat-Treated Steel

This paper effectively integrates Taguchi, Response Surface Methodology (RSM), and Genetic Algorithm (GA) approaches for both single and multi-objective optimization, delivering low-cost, high-effectiveness solutions for grinding. It examines the effects of three factors—depth of cut in coarse grinding, depth of cut in fine grinding, and the number of spark-outs—on three objectives: surface roughness, grinding time, and the deviation between the desired and actual grinding depth for SKD61 steel. Through in-depth analysis, the paper describes the impact of these factors and their interactions with the responses. It also proposes the optimal parameter setup for each objective. The optimal grinding time is achieved at 950 seconds with a coarse grinding depth of 0.007 mm, fine grinding depth of 0.004 mm, and zero spark-out. The minimal deviation and surface roughness were obtained at 0 mm and 0.144 µm, respectively, using the optimal setup of a coarse grinding depth of 0.004 mm, fine grinding depth of 0.001 mm, and 10 spark-outs. By applying GA, the paper provides a Pareto solution set, offering multiple combinations of optimal factors for minimizing grinding time, deviation, and surface roughness. These solutions serve as useful references for users seeking the best trade-offs in multi-objective scenarios. This paper contributes to improving customer satisfaction by enhancing the quality and efficiency of the machining process while reducing production costs for grinding machines. Its methodology can also be applied to other optimization fields.

Interplay of aprotic ionic liquids with hydrogen bonded networks in associating aliphatic alcohols

Non-covalent interactions formed by molecules of aliphatic alcohols in the bulk liquid impart tendencies for the molecules to associate through formation of hydrogen bonding. Aprotic ionic liquids (IL) possess a varying potential to interact with the hydrogen-bonded networks. Mixtures of such alcohols and IL then exhibit strongly non-ideal character, propagating to some peculiar thermodynamic properties. In this work, a hierarchy of the interference of 15 aprotic 1-butyl-3-methyl-imidazolium based IL with hydrogen bonds among heptan-3-ol molecules is established based on a series of atomistic molecular-dynamics (MD) simulations and quantum–mechanical (QM) modeling. Structural, spectroscopic, and thermodynamic observables are calculated to illustrate how individual IL anions affect hydrogen bonding among alcohol molecules. While the inorganic halogenated anions interact weakly with the alcohols, oxygenated anions such as acetate or alkyl-phosphates interact very strongly. A special attention is paid to fine-tuning the implementation of the Drude polarizable model and to using ab initio MD at the density-functional theory (DFT) level to provide reference data to develop an optimum setup of the polarizable model. Challenges associated with using the polarizable molecular simulations of hydrogen-bonded networks and with the limited reliability of DFT-MD are highlighted in this work.

Unlocking the potential of urban EV battery recycling: A dual optimization model

As electric vehicles increase, they reduce greenhouse gas emissions but pose recycling challenges for their batteries. Economic and environmental impacts are significant concerns. This study proposes a dual optimization approach to develop a city-wide recycling network benefiting vehicle owners and recycling facilities. Focusing on Xi'an City, China, we project future battery recycling volumes and analyze optimal network setups at various stages. In reverse logistics and network design research, decentralized battery processing centers and centralized energy storage centers can greatly improve consumer benefits and ensure formal recycling practices. In research on consumer engagement and behavior, it's important to design recycling subsidies wisely and enhance consumer motivation to participate in formal recycling. As the number of retired vehicle batteries grows, the government should consider shifting subsidies from consumers to centralized recycling centers to maintain a sustainable and efficient recycling system.

Ultrafast imaging of laser-induced modifications in planar targets via single-shot in-line holography

Abstract We present a holographic single-shot coherent diffractive imaging method based on in-line holography that allows the ultrafast characterization of 2D transmission maps of semi-transparent planar targets such as foils in amplitude and phase. Holographic information is obtained from the interference of the transmitted primary beam with the fields scattered from the modified or unknown target regions. A specialized iterative phase retrieval is used to incorporate the holographic nature of the approach and to accelerate and improve convergence. The achievable quality and reproducibility of the reconstructed transmission maps as well as optimal setup parameters are investigated using realistic pre-characterized reference targets. We used non-circular laser-induced hole structures in 30 nm thin gold foils that represent the final state of a laser modification and show that the far field error of the reconstructed diffraction images can be used to estimate and optimize the reconstruction quality in the object plane in order to obtain accurate and reproducible transmission maps. Our results mark the important first step towards the full spatio-temporal analysis of all stages of laser material modification or laser ablation in two-color pump probe experiments, including plasma formation, equilibration, and expansion.

Definition and Validation of a Zero-Dimensional IC Engine Model for Assessing the Performance of Different Methane-Hydrogen Mixtures

Abstract To decarbonize the maritime transport sector, a solution inspired by the automotive industry involves using a solid oxide fuel cell (SOFC) in conjunction with an internal combustion engine. For optimal emission efficiency, the internal combustion engine is fueled with a primary fuel, typically methane already on board for feeding the SOFC, plus anode off-gases from SOFC, which are a mixture of variable composition of hydrogen, carbon dioxide, and carbon monoxide. To assess the performance of the integrated system (SOFC+ICE) and subsequently search for an optimal set-up, it is useful to use a zero-dimensional model of each component of the system. Particularly, the internal combustion engine model must balance speed of execution with flexibility in terms of fuel composition, engine dimensions, and other relevant parameters. In this study, an engine model originally developed for gasoline-fueled ICE, is adapted for variable methane-hydrogen mixtures. The model is validated against experimental data measured on an engine operating with similar mixture compositions and available in the literature.

Optimization of three-dimensional Tesla valve-based passive micromixer for gradient functional materials mixing

With the rapid development of three-dimensional (3D) printing technology, the field of multi-material fabrication, particularly in the production of functionally graded materials, has achieved revolutionary advancements. However, the structures of micromixers, which are central to this technology, have not been sufficiently studied. This study introduces an optimized passive micromixer design based on a 3D Tesla valve, specifically engineered for the efficient mixing of Fe3O4 particles within an epoxy resin E51 matrix. The micromixer leverages the fluid dynamics of the Tesla valve, where sudden changes in flow direction generate secondary flows that enhance mixing performance by disrupting laminar flow and inducing chaotic advection, thereby increasing the interfacial area between different material components. This process significantly improves mixing efficiency in the fabrication of gradient materials. Through theoretical analysis and computational simulations, the study identifies optimal configurations for the Tesla valve system, emphasizing mixing dynamics that enhance material gradient formation. The findings reveal that optimal mixing efficiency is achieved with a configuration comprising three Tesla valves. This optimal setup includes a 0.5-mm opening width at the turning point, an immediate transition from the turning point to the straight pipe (0 mm length), a 0.25-mm structural height for the Tesla valve, and an outlet distance of 0.25 mm from the curved pipe. Hopefully, this study can provide a robust reference for the significant potential of future applications in the realm of multi-material 3D printing, thereby fostering its substantial growth prospects.

Safety in Perioperative Period in Laparoscopic Surgery

Laparoscopic surgery has revolutionized the field of surgery, offering significant improvements in patient outcomes such as reduced recovery times, pain, and hospital stays compared to traditional open surgery. However, safety concerns arose with its introduction, prompting the need for advancements in technique and equipment. This article explores critical safety improvements in laparoscopic surgery, simulating a patient’s perioperative journey. Key topics include the importance of team briefings, proper patient positioning, ergonomics, optimal laparoscopic setup, safe port insertion techniques, pneumoperitoneum management, instrument handling, and hemostasis. The review highlights the ongoing need for research and consensus in patient safety practices within laparoscopic surgery.

Integration of a dose management system in the public hospitals of Cyprus: Key areas and initial outcomes.

Despite the growing use of dose management systems (DMSs), there is limited guidance on their optimal setup. This study aims to fill this gap by outlining the process followed to integrate "DOSE" (Qaelum NV, Belgium) DMS within the Cypriot public hospitals. DOSE was connected to the available central PACS, followed by a thorough data and functioning validation. A preliminary study was also conducted to assess the DOSE's ability to establish typical dose values in diagnostic radiography. Overall, over 70 x-ray units were configured. For some connections, it was necessary to first adjust the unit's DICOM attributes or to utilise the DICOM coercion technique. The DICOM image header was sometimes preferred over the DICOM RDSR, as the chosen data source. This study underscored the importance of understanding the equipment's technical capabilities for a successful DMS integration, while personnel with expertise in diagnostic radiology and the IT aspects of medical physics proved to have a vital role in the process. The pilot study showed that DOSE is an effective tool for establishing typical dose values and assessing compliance with relevant dose levels.

Absorption line broadening in atomic beams produced in a molecular beam epitaxy environment.

Tunable diode laser absorption spectroscopy (TDLAS) is used to measure the 6s2 1S-5d6p 3D0 absorption line profile of a Ba atomic beam produced in a molecular beam epitaxy (MBE) reactor. Despite the noisy MBE environment, a signal-to-noise ratio up to 100 is obtained thanks to a thorough optimization of the measurement setup. A model that realistically describes this absorption profile is presented, taking into account the angular distribution of atomic concentration in the atomic beam as well as the reactor and setup geometry. Our results highlight the influence of the latter on the Doppler line broadening, and allows to estimate the angular distribution of concentration in the beam emitted by the effusion cell. The model can be adapted to estimate the absorption profile for any element in any reactor geometry, making it a useful tool to design MBE dedicated atomic absorption sensors such as optical flux monitoring sensors, or to characterize the beam emitted by any effusion cell.