Search Results (102)

Based on the magnetic configuration of the China Astro-Torus-1 (CAT-1) levitated dipole device, this study investigated the confinement performance of common discharge gas ions under E × B transverse transport conditions induced by electric fields. By adjusting L-coil parameters to shift the inject location, it was found that when the loss boundary is in the outer weak-field region, most particles with large Larmor radii are lost after colliding with the wall, for particles with large pitch angles, the strongly anisotropic magnetic field causes particles across a broad range of energies to be lost through the X-point into the divertor. The study demonstrates that for particle kinetic energies between 100 and 300 eV, the CAT-1 device exhibits a loss cone angle θ_loss of approximately 58°, indicating favorable confinement performance. Full article

This systematic review synthesizes 82 peer-reviewed studies published between 2014 and 2024 on the use of audio features in educational research. We define audio features as descriptors extracted from audio recordings of educational interactions, including low-level acoustic signals (e.g., pitch and MFCCs), speaker-based metrics (e.g., talk-time and participant ratios), and linguistic indicators derived from transcriptions. Our analysis contributes to the field in three key ways: (1) it offers targeted mapping of how audio features are extracted, processed, and functionally applied within educational contexts, covering a wide range of use cases from behavior analysis to instructional feedback; (2) it diagnoses recurrent limitations that restrict pedagogical impact, including the scarcity of actionable feedback, low model interpretability, fragmented datasets, and limited attention to privacy; (3) it proposes actionable directions for future research, including the release of standardized, anonymized feature-level datasets, the co-design of feedback systems involving pedagogical experts, and the integration of fine-tuned generative AI to translate complex analytics into accessible, contextualized recommendations for teachers and learners. While current research demonstrates significant technical progress, its educational potential is yet to be translated into real-world educational impact. We argue that unlocking this potential requires shifting from isolated technical achievements to ethically grounded pedagogical implementations. Full article

The legal and social perception of sexually motivated crimes has undergone profound transformations in the Czech lands from the Middle Ages to the present. Acts once considered grave moral transgressions, punishable by death, have been gradually decriminalised or even integrated into the realm of personal identity and cultural self-expression. This article examines the evolving legal frameworks and societal attitudes towards such offences, with a particular focus on their implications for family structures, inheritance rights, and genealogical continuity. By analysing historical judicial records—primarily early modern pitch books—alongside contemporary legislation, we highlight the shifting boundaries between crime, morality, and individual rights. Methodologically, this study combines a historical–legal analysis with comparative criminology to elucidate the changing regulatory mechanisms governing sexual behaviour. The findings illustrate that, while legal norms have progressively moved away from religious morality toward individual freedoms, some taboos persist, reflecting enduring social anxieties. The Czech case serves as a model for broader European trends, offering valuable insights into the interplay between law, social norms, and genealogical structures across different historical periods. Full article

As semiconductor packaging technologies face limitations, through-silicon via (TSV) technology has emerged as a key solution to extending Moore’s law by achieving high-density, high-performance microelectronics. TSV technology enables enhanced wiring density, signal speed, and power efficiency, and offers significant advantages over traditional wire-bonding techniques. However, achieving fine-pitch and high-density interconnects remains a challenge. Solder flip-chip microbumps have demonstrated their potential to improve interconnect reliability and performance. However, the environmental impact of lead-based solders necessitates a shift to lead-free alternatives. This review highlights the transition from Sn-Pb solders to lead-free options, such as Sn-Ag, Sn-Cu, Sn-Ag-Cu, Sn-Zn, and Bi- or In-based alloys, driven by regulatory and environmental considerations. Although lead-free solders address environmental concerns, their higher melting points pose challenges such as thermal stress and chip warping, which affect device reliability. To overcome these challenges, the development of low-melting-point solder alloys has gained momentum. This study examines advancements in low-temperature solder technologies and evaluates their potential for enhancing device reliability by mitigating thermal stress and ensuring long-term stability. Full article

Cardiovascular diseases (CVDs) are the leading cause of death worldwide, claiming over 17 million lives annually. Early detection of conditions like heart murmurs, often indicative of heart valve abnormalities, is critical for improving patient outcomes. Traditional diagnostic methods, including physical auscultation and advanced imaging techniques, are constrained by their reliance on specialized clinical expertise, inherent procedural invasiveness, substantial financial costs, and limited accessibility, particularly in resource-limited healthcare environments. This study presents a novel convolutional recurrent neural network (CRNN) model designed for the non-invasive classification of heart murmurs. The model processes heart sound recordings using advanced pre-processing techniques such as z-score normalization, band-pass filtering, and data augmentation (Gaussian noise, time shift, and pitch shift) to enhance robustness. By combining convolutional and recurrent layers, the CRNN captures spatial and temporal features in audio data, achieving an accuracy of 90.5%, precision of 89%, and recall of 87%. These results underscore the potential of machine-learning technologies to revolutionize cardiac diagnostics by offering scalable, accessible solutions for the early detection of cardiovascular conditions. This approach paves the way for broader applications of AI in healthcare, particularly in underserved regions where traditional resources are scarce. Full article

Electroplating has become a cornerstone technology in semiconductor manufacturing, enabling high-performance interconnects and advanced packaging. Since the introduction of the Damascene Cu process at the 180 nm node, it has evolved to meet the demands for precision, uniformity, and scalability in miniaturized nodes and complex packaging architectures. The shift to horizontal electroplating systems has enhanced uniformity and process stability, particularly for applications such as TSVs, Cu pillars, micro-bumps, and RDLs. Emerging innovations like pulse electroplating, segmented anode control, and AI-driven monitoring are addressing the challenges of fine-pitch interconnects and emerging interconnect materials, such as cobalt. These advancements are critical for high-density interconnects used in AI, HPC, and high-frequency applications. This review explores the advancements in electroplating technologies, focusing on their role in semiconductor manufacturing. It highlights the evolving equipment designs and their implications for achieving precision, scalability, and reliability at advanced nodes. The ongoing development of electroplating equipment and techniques will support the reliability and performance of future semiconductor devices, reinforcing electroplating as a cornerstone technology in advanced packaging and fabrication. Full article

The musical signal produced by plucked instruments often exhibits non-stationarity due to variations in the pitch and amplitude, making pitch estimation a challenge. In this paper, we assess different transcription processes and algorithms applied to signals captured by optical sensors mounted on a pipa—a traditional Chinese plucked instrument—played using a range of techniques. The captured signal demonstrates a distinctive arched feature during plucking. This facilitates onset detection to avoid the impact of the spurious energy peaks within vibration areas that arise from pitch-shift playing techniques. Subsequently, we developed a novel time–frequency feature, known as continuous time-period mapping (CTPM), which contains pitch curves. The proposed process can also be applied to playing techniques that mix pitch shifts and tremolo. When evaluated on four renowned pipa music pieces of varying difficulty levels, our fully time-domain-based onset detectors outperformed four short-time methods, particularly during tremolo. Our zero-crossing-based pitch estimator achieved a performance comparable to short-time methods with a far better computational efficiency, demonstrating its suitability for use in a lightweight algorithm in future work. Full article

Diacritical Arabic (DA) refers to Arabic text with diacritical marks that guide pronunciation and clarify meanings, making their recognition crucial for accurate linguistic interpretation. These diacritical marks (short vowels) significantly influence meaning and pronunciation, and their accurate recognition is vital for the effectiveness of automatic speech recognition (ASR) systems, particularly in applications requiring high semantic precision, such as voice-enabled translation services. Despite its importance, leveraging advanced machine learning techniques to enhance ASR for diacritical Arabic has remained underexplored. A key challenge in developing DA ASR is the limited availability of training data. This study introduces a transformer-based approach leveraging transfer learning and data augmentation to address these challenges. Using a cross-lingual speech representation (XLSR) model pretrained on 53 languages, we fine-tune it on DA and integrate connectionist temporal classification (CTC) with transformers for improved performance. Data augmentation techniques, including volume adjustment, pitch shift, speed alteration, and hybrid strategies, further mitigate data limitations, significantly reducing word error rates (WER). Our methods achieve a WER of 12.17%, outperforming traditional ASR systems and setting a new benchmark for DA ASR. These findings demonstrate the potential of advanced machine learning to address longstanding challenges in DA ASR and enhance its accuracy. Full article

This research utilized a hybrid trajectory on a wing incorporating a dual flap with the goal of enhancing performance. The hybrid profiles initiate with a non-sinusoidal pattern during the interval 0.0 ≤ t/T ≤ 0.25, evolving toward a sinusoidal pattern within the range 0.25 < t/T ≤ 0.5. Similarly, the hybrid motion follows a non-sinusoidal pattern in the range 0.5 < t/T ≤ 0.75, before shifting back to a sinusoidal pattern within the range 0.75 < t/T ≤ 1.0. The effectiveness of using a hybrid trajectory on a wing with leading and trailing flaps in enhancing the energy harvesting performance is examined through numerical simulations. The results demonstrate that hybrid trajectories applied to a two-flap wing configuration outperform a single flat plate and a wing with leading and trailing flaps both operating under a sinusoidal trajectory. The wing length spans from 45% to 55%, with the leading flap length ranging from 25% to 35%. The trailing flap lengths adjust accordingly to ensure the combined total matches the flat plate’s full length, which is 100%. The wing pitch angle was fixed at 85° while the leading flap’s pitch angle varied between 40° and 55° and the pitch angle of the trailing flap ranged from 0° to 20°. The findings reveal that utilizing hybrid motion on a wing fitted with leading and trailing flaps notably improves power output in comparison to configurations with either one plate or three plates. The power output is achieved at particular dimensions: a leading flap length of 30%, a wing length of 55%, and a trailing flap length of 15%. The corresponding pitch angles are 50° for the leading flap, 85° for the wing, and 10° for the trailing flap. The aforementioned configuration results in a 34.06% increase in output power in comparison to one plate. The maximum efficiency for this setup reaches 44.21%. This underscores the superior performance of hybrid trajectories over sinusoidal trajectories in enhancing energy extraction performance. Full article

Compared to analog beamforming, digital beamforming offers better self-calibration and lower sidelobe performance, which has a profound impact on improving low Earth orbit receiver performance. The Digital Beamforming (DBF) module in the low Earth orbit satellite broadcast signal reception terminal can use digital phase shifting to compensate for the phase differences caused by path and spatial distance variations due to inconsistent Radio Frequency (RF) channel delays. This compensation ensures in-phase summation, thereby achieving maximum energy reception in the desired direction. Although DBF has gained widespread attention in the radar field due to its unique functions and advantages, its application is limited by beamforming accuracy and gain. Therefore, with the development of DBF technology, how to improve its accuracy and gain has also attracted extensive attention both domestically and internationally. To address this issue, this paper proposes a beamforming method based on a cap-shaped array for low Earth orbit satellite broadcast signal reception and processing terminals. The method combines prior information and spatial domain search for beam control, and employs a lookup table for beam synthesis. It derives formulas for the Signal-to-Noise Ratio, noise figure, processing flow of the beamforming network, and the determination of beamforming weights for the spherical antenna array. The paper presents a beam control approach that combines prior information with spatial domain search, along with an implementation process for beam synthesis using a lookup table. It also details the corresponding Field-Programmable Gate Array (FPGA) implementation process. Finally, the beamforming algorithm is experimentally validated, and error analysis is conducted. The experimental results show that the measured beamforming sensitivity at all incident angles is below −133 dBm and the G/T values are all greater than −9 dB/K, the beam uniformity at three operating frequencies is less than 3°, and the measured errors in pitch and azimuth angles are both below 2°. The beam pointing error is also below 2°. Full article

Speech emotion recognition (SER) is an emerging technology that utilizes speech sounds to identify a speaker’s emotional state. Computational intelligence is receiving increasing attention from academics, health, and social media applications. This research was conducted to identify emotional states in verbal communication. We applied a publicly available dataset called RAVDEES. The data augmentation process involved adding noise, applying time stretching, shifting, and pitch, and extracting the features zero cross rate (ZCR), chroma shift, Mel-Frequency Cepstral Coefficients (MFCC), and a spectrogram. In addition, we used many pretrained deep learning models, such as VGG16, ResNet50, Xception, InceptionV3, and DenseNet121. Out of all of the deep learning models, Xception yielded superior outcomes. Furthermore, we improved performance by changing the Xception model to include hyperparameters and additional layers. We used a variety of performance evaluation parameters to test the proposed model. These included F1-score, accuracy, misclassification rate (MCR), precision, sensitivity, specificity, negative predictive value, false negative rate, false positive rate, false discovery rate, false omission rate, and false discovery rate. The model that we suggested demonstrated an overall accuracy of 98%, with an MCR of 2%. Additionally, it attained precision, sensitivity, and specificity values of 91.99%, 91.78%, and 98.68%, respectively. Additional models attained an F1-score of 91.83%. Our suggested model demonstrated superiority compared to other cutting-edge techniques Full article

In this paper, we propose a method for how to find the optimum pitch of volumetric computational reconstruction (VCR) in integral imaging. In conventional VCR, the pixel shifts between elemental images are quantized due to pixel-based processing. As a result, quantization errors may occur during three-dimensional (3D) reconstruction in integral imaging. This may cause the degradation of the visual quality and depth resolution of the reconstructed 3D image. To overcome this problem, we propose a method to find the optimum pitch for VCR in integral imaging. To minimize the quantization error in VCR, the shifting pixels are defined as a natural number. Using this characteristic, we can find the optimum pitch of VCR in integral imaging. To demonstrate the feasibility of our method, we conducted simulations and optical experiments with performance metrics such as the peak-signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Full article

The distortion of helium speech caused by helium−oxygen gas mixtures significantly impacts the safety and communication efficiency of saturation divers. Although existing correction methods have shown some effectiveness in improving the intelligibility of helium speech, challenges remain in enhancing clarity and high−pitch correction. To address the issue of degraded speech quality post−correction, a novel helium speech correction method based on generative adversarial networks (GANs) is proposed. Firstly, a new helium speech dataset is introduced, which includes isolated words and continuous speech in both Chinese and English. By training and testing on both isolated words and continuous passages, the correction capability of the model can be accurately evaluated. Secondly, a new evaluation system for helium speech correction is proposed, which partially fills the gap in current helium speech evaluation metrics. This system uses comprehensive similarity to evaluate the similarity of keywords at the sentence level, thus assessing the correction results of helium speech from both word and sentence dimensions. Lastly, a GAN−based helium speech correction method is designed. This method solves the problems of pitch period distortion and formant shift in helium speech by introducing an adaptive speech segmentation algorithm and a fusion loss function and significantly improves the clarity and intelligibility of corrected helium speech. The experimental results show that the corrected helium speech is improved in clarity and intelligibility, which shows its practical value and application potential. Full article

The Darrieus vertical axis wind turbine (VAWT) is categorized as a lift-based turbomachine. It faces challenges in the low tip speed ratio (TSR) range and requires initial torque for the starting operation. Ongoing efforts are being made to enhance the turbine’s self-starting capability. In this study, Computational Fluid Dynamics (CFD) simulations were utilized to tackle the identified challenge. The Unsteady Reynolds-Averaged Navier–Stokes (URANS) approach was employed, combined with the shear–stress transport (SST) $k-\omega$ turbulence model, to resolve fluid flow equations. The investigation focused on optimizing the placement of auxiliary blades by considering design parameters such as the pitch angle and horizontal and vertical distances. The goal was to increase the turbine efficiency and initial torque in the low-TSR range while minimizing efficiency loss at high-TSR ranges, which is the primary challenge of auxiliary blade installation. Implementing the auxiliary blade successfully extended the rotor’s operational range, shifting the rotor operation’s onset from TSR 1.4 to 0.7. The optimal configuration for installing the auxiliary blade involves a pitch angle of 0°, a horizontal ratio of 0.52, and a vertical ratio of 0.41. To address the ineffectiveness of auxiliary blades at high-TSRs, installing deflectors in various configurations was explored. Introducing a double deflector can significantly enhance the overall efficiency of the conventional Darrieus VAWT and the optimum rotor with the auxiliary blade by 47% and 73% at TSR = 2.5, respectively. Full article

This paper presents a new type of phase-shifted Fiber Bragg Grating (FBG): the sliced-FBG (SFBG). The fabrication process involves cutting a standard FBG inside its grating region. As a result, the last grating pitch is shorter than the others. The optical output signal consists of the overlap between the FBG reflection and the reflection at the fiber-cleaved tip. This new fiber optic device has been studied as a vibration sensor, allowing for the characterization of this sensor in the frequency range of 150 Hz to 70 kHz. How the phase shift in the FBG can be controlled by changing the length of the last pitch is also shown. This device can be used as a filter and a sensing element. As a sensing element, we will demonstrate its application as a vibration sensor that can be utilized in various applications, particularly in monitoring mechanical structures. Full article