Search Results (254)

Using a fixed-effects panel threshold regression with Driscoll–Kraay inference, this paper examines how institutional quality shapes the distributional effects of foreign direct investment (FDI) in the ASEAN-5 economies (Indonesia, Malaysia, the Philippines, Thailand, and Vietnam) over 2002–2023. The empirical framework allows the impact of FDI on income inequality (net Gini index) to differ across low- and high-institutional regimes and to vary within regimes through interaction terms. Across governance indicators from the Worldwide Governance Indicators and a composite institutional quality index (IQ) constructed via principal component analysis (PCA), the results reveal pronounced nonlinearities, most clearly for government effectiveness, where the association between FDI and inequality switches sign across institutional regimes. For other governance dimensions, the FDI–inequality relationship is similarly regime-dependent and operates partly through regime-specific interaction effects, underscoring the importance of institutional thresholds in mediating distributional outcomes. Robustness checks confirm the directional consistency of the baseline results. Our findings imply that governance reforms must surpass critical institutional thresholds, particularly in effectiveness and implementation capacity, before FDI can contribute to reducing income inequality, highlighting the central role of deep governance improvements in enabling inclusive growth in ASEAN economies. Full article

Background: Twenty-four-hour movement behaviours (physical activity, sedentary time, and sleep) may be associated with adolescent cognitive performance, but evidence from Central/Eastern Europe is limited. Methods: A total of 82 Slovak adolescents (15–19 years) completed tests of IQ, attention, and visual memory. Participants wore a wrist accelerometer 24/7 for seven consecutive days (processed in GGIR v3.0–3). Moderate-to-vigorous physical activity (MVPA), total sedentary time, and sleep duration were derived from accelerometry; physical activity was also self-reported using the Physical Activity Questionnaire for Adolescents (PAQ-A). Non-parametric tests and Spearman correlations were applied; sleep × MVPA interaction models (robust HC3 standard errors) were adjusted for age and sex. Results: MVPA was low (median 32.9 min/day; 11% met ≥60 min/day), while sedentary time was high (median 652.6 min/day). Associations between movement behaviours and cognition were generally small, and no sleep × MVPA interaction effects were observed. The PAQ-A overestimated device-based MVPA (mean bias +1.68 units; 95% limits of agreement +1.10 to +2.25), with greater overestimation in girls and older adolescents. Conclusions: In this convenience sample, 24 h movement patterns were suboptimal, and their associations with cognition were modest and exploratory. Larger longitudinal studies are needed to confirm these findings. Full article

This paper presents a nine-switch inverter for brushless operation of wound-field synchronous machines with excellent rotor flux regulation freedom. The manufacturing cost of permanent magnet machines is high due to the instability of rare-earth magnet prices in the global market. Moreover, conventional wound-field synchronous machines (WFSMs) have problems with their rotor brushes and slip-ring assembly, wherein the assembly starts to malfunction in the long run. Furthermore, recently, some brushless WFSM topologies have been investigated to eliminate the problems associated with rotor brushes and slip rings, but they have either a high cost due to a double-inverter, or low flux regulation freedom due to a single inverter (−i_d). The proposed nine-switch topology achieves a low cost by using a single inverter with nine switches and excellent flux control through three variables (−i_d, i_q, and i_f), making it highly suitable for wide-speed applications. In the proposed topology, the machine’s armature winding is divided into two sets of coils: ABC and XYZ. A 12-slot and 8-pole machine stator is wound with armature winding coils ABC and XYZ, creating six terminals for injecting currents and two neutrals from each ABC and XYZ coil set. The current to the ABC and XYZ coils is supplied by a nine-switch inverter. The inverter is specially designed to supply rated currents to the ABC winding coils and half of the rated current to the XYZ winding coils. The number of turns of the ABC and XYZ winding coils are kept the same so they produce the same winding function. However, the current in the XYZ winding coils is half compared to that of the ABC winding coils, which creates an asymmetrical airgap magnetomotive force (MMF). The asymmetrical airgap MMF contains two working harmonics, i.e., fundamental MMF for torque production and an additional sub-harmonic MMF component for rotor field brushless excitation. The rotor field is controlled by the difference in current of the two armature winding coils: ABC and XYZ. The proposed topology is validated through theoretical analysis and finite element simulations of electromagnetic and flux regulation. A 2D finite-element analysis is performed to verify the idea. The proposed topology is capable of establishing a 9.15 A dc current in the rotor field winding coil, which consequently generates a torque of 7.8 N·m with a 20.30% torque ripple. Rotor field flux regulation was analyzed from the stator ABC and XYZ coils current ratio ζ. The ratio ζ is analyzed as 2 to 1.3; subsequently, the inducted field currents were 9.15 A dc to 4.8 A dc, respectively. Full article

Autism spectrum disorder (ASD) and intellectual disability (ID) frequently coexist and share heterogeneous cognitive manifestations, yet their specific performance patterns on Wechsler scales remain poorly systematized. This meta-analysis synthesized data from 31 studies using the WISC-IV, WISC-V, WAIS-III, and WAIS-IV to compare cognitive index profiles in individuals with ASD, ID and ASD+ID. Standardized mean differences (Hedges’ g) were calculated using random-effects models, adopting a normative reference of mean 100 and SD 15. Results showed a distinct profile for ASD, with greater impairments in the Processing Speed Index (PSI) and Working Memory Index (WMI), while the Vocabulary Comprehension Index (VCI), Perceptual/Fluid Reasoning Index (PRI/FRI), and Visual Processing Index (VPI) remained close to normative scores. In contrast, ID and ASD+ID exhibited generalized deficits across all indices, with the lowest scores in Full-Scale IQ (FSIQ) and broad effects above g = −2.5. No significant differences emerged between Wechsler versions or age-based test types. Heterogeneity was high in ASD and ID across outcomes, but negligible in ASD+ID due to reduced k. These findings reinforce that ASD presents a specific cognitive pattern, whereas ID and ASD+ID display diffuse impairment, and that Wechsler scales are consistent across versions for identifying these profiles. Full article

Background: Comprehensive and accurate compound composition characterization in natural sources has high relevance in food and nutrition, health and medicine, environmental and agriculture research areas, though profiling of plant metabolites is a challenging task due to the structural complexity of natural products. This study delves into the identification and characterization of compounds within the Plantago genus, leveraging state-of-the-art analytical techniques. Methods: Utilizing an ultra-high-performance liquid chromatography (UHPLC) system in conjunction with Orbitrap™ IQ-X™ Tribrid™ mass spectrometer (MS), we employed a Phenyl-Hexyl HPLC column alongside optimized extraction protocols to analyze both husk and leaf samples. To maximize compound identification, we implemented data-dependent acquisition (DDA) methods including MS² (ddMS2), MS³ (ddMS3), AcquireX™ deep scan, and real-time library search (RTLS). Results: Our results demonstrate a significant increase in the number of putatively yet confidently assigned compounds, with 472 matches in P. lanceolata leaves and 233 in P. ovata husk identified through combined acquisition methods. The inclusion of an additional fragmentation level (MS³) noticeably enhanced the confidence in compound annotation, facilitating the differentiation of isomeric compounds. Furthermore, the application of low-energy fragmentation (10 normalized collision energy (NCE) for higher-energy collisional dissociation (HCD)) improved the detection and grouping of MS¹ fragments by 55% in positive mode and by 16% in negative mode, contributing to a more comprehensive analysis with minimal loss in compound identification. Conclusions: These advancements underscore the potential of our methodologies in expanding the chemical profile of plant materials, offering valuable insights into natural product analysis and dereplication of untargeted data. Full article

Background/Objectives: Soroseris hookeriana, a Tibetan medicinal plant endemic to the high-altitude Qinghai–Tibet Plateau, possesses significant pharmacological value but lacks fundamental genomic characterization. This study aims to generate and comparatively analyse its complete chloroplast genome. Methods: Total DNA was sequenced, assembled with GetOrganelle, annotated with CPGAVAS2, and compared with eight Asteraceae species; phylogenetic placement was inferred with IQ-TREE from 21 complete plastomes. Results: The circular chloroplast genome is 152,514 bp with a typical quadripartite structure (LSC 84,168 bp, SSC 18,528 bp, two IRs 24,909 bp each). It contains 132 unique genes (87 protein-coding, 37 tRNA, 8 rRNA; 18 duplicated in IRs yield 150 total copies). Twenty-three genes harbour introns; clpP and ycf3 have two. Overall GC content is 37.73%, elevated in IRs (43.12%). Codon usage shows strong A/U bias at the third position; 172 SSRs and 39 long repeats are detected. IR-SC boundaries exhibit the greatest inter-specific variation, notably in ycf1 and ndhF. Conclusions: The complete plastome robustly supports S. hookeriana and Stebbinsia umbrella as sister species (100% bootstrap) and provides essential genomic resources for species identification, population genetics, and studies of high-altitude adaptation. Full article

Generative artificial intelligence (AI) models including GPT, Gemini, and DeepSeek are reshaping embodied agents, temporal prediction, and autonomous driving, demanding a ten-fold annual growth in training FLOPS that Moore’s law can no longer sustain. Consequently, scale-out GPU clusters require >400 Gb/s lane-rate optical interconnects within AI data-centers (AIDCs). Single-photodiode direct detection offers density, latency, and energy advantages, but DAC bandwidth remains limited to around 70 GHz. We present an optical triple-band multiplexing scheme that replaces high-frequency radio frequency (RF) mixers and local oscillators (LOs) with photonic components. A Mach–Zehnder modulator (MZM) generates 80-GBd PS-PAM-20 signal while an in-phase/quadrature (IQ) modulator driven by a wavelength-offset laser creates two independent 35-GBd PS-64-QAM bands. The proposed optical multiplexing method breaks conjugate symmetry and enhances dispersion tolerance of the direct detection system. After 200 m SSMF transmission and single 70-GHz photodiode (PD) detection, digital signal-signal beating interference (SSBI)/cross-beating compensation enables the recovery of net 543.9 Gb/s signal (line rate of 686.6 Gb/s) using only 45-GHz DACs. The optical multiplexing architecture provides a path to beyond-400 Gb/s lanes and demonstrates a scalable, energy-efficient solution for next-generation AI clusters. Full article

Mn⁴⁺-activated fluoride phosphors possess outstanding luminescent properties, making them highly suitable for applications in lighting and display technologies. However, the synthesis of such phosphors generally requires the use of large amounts of highly toxic aqueous HF, leading to serious environmental pollution. To eliminate the use of hazardous HF solution, a low-temperature molten salt method employing NH₄HF₂ was developed to synthesize the narrow-band red emitter Cs₂GeF₆: Mn⁴⁺ phosphor. Following the reaction, the product was washed with a dilute H₂O₂ solution to remove residual NH₄HF₂ and other impurities. The phase purity and morphology were analyzed by X-ray diffraction (XRD) and scanning electron microscopy (SEM), respectively, and the luminescence properties were examined via photoluminescence (PL) spectroscopy. The obtained phosphors exhibit bright red emission characteristics of Mn⁴⁺ under blue-violet excitation. Among them, Cs₂GeF₆: 0.08 Mn⁴⁺ shows the highest emission intensity, with an internal quantum efficiency (IQE) of 78%. A white light-emitting diode (WLED) fabricated by combining this phosphor with a blue chip and commercial Y₃Al₅O₁₂: Ce³⁺ (YAG) phosphor achieved a high luminous efficacy (LE) of ~146 lm/W, a correlated color temperature (CCT) of ~4396 K, and a color rendering index (Ra) of ~83, alongside excellent operational color stability. Full article

The emerging development of Global Navigation Satellite System (GNSS) software receivers has opened new opportunities in diverse operations. However, non-line-of-sight (NLOS) concatenated signal reception is one prevalent deterioration factor causing positioning errors in urban scenarios. To enhance integrity and reliability through receiver autonomous integrity monitoring (RAIM) techniques in urban environments, distinguishing between line-of-sight (LOS) and NLOS signals facilitates the exclusion of NLOS channels: this is challenging due to uncertain signal reflections/refractions from diverse obstruction conditions in the built environment. Moreover, NLOS features show similarity to multipath effects like scattering and diffraction which causes difficulty in identifying the NLOS type. Recent work exploited NLOS detections with multi-correlator outputs using neural networks that outperform using signal strength techniques for NLOS detection. This paper proposes a neural network approach designed to recognise and learn spatial features among early, late, and prompt correlator outputs, differentiating between correlations, and also by memorising temporal features to acquire propagation information. Specifically, the spatial features of correlator IQ streams are derived from convolutional layers incorporated with concatenations, to formulate associate models like early-minus-late discrimination. A Recurrent Neural Network (RNN), i.e., long short-term memory (LSTM), is integrated to obtain comprehensive temporal features; hereby, a softmax classifier is appended in the last layer to distinguish between NLOS and LOS signals. By simulating synthetic datasets generated by a Spirent simulator and captured by a software-defined radio (SDR), the correlator outputs are acquired during the scalar tracking stage. The product of the proposed network demonstrates high performance in terms of accuracy, time consumption and sensitivity, affirming the efficiency of utilising early-stage correlations for NLOS detection. Moreover, an impact analysis of varying the sliding window length on NLOS discrimination underscores the need to fine-tune the parameter, as well as balancing accuracy, operation complexity and sensitivity. Full article

Narrowband Internet of Things (NB-IoT) was designed as a key Low-Power Wide-Area Network technology when 5G networks were established. The ideal quadrature demodulation in NB-IoT relies on the fundamental symmetry between the in-phase (I) and quadrature (Q) branches, characterized by a perfect 90-degree phase shift and matched amplitude. However, practical hardware imperfections in mixers, filters, and ADCs break this symmetry, leading to I/Q imbalances. Moreover, I/Q imbalance is coupled with carrier frequency offset (CFO), which arises from asymmetry in the frequency of the transceiver oscillator. In this paper, we propose a model-embedded lightweight network for joint CFO and I/Q imbalance estimation for NB-IoT systems. An I/Q imbalance compensation model is embedded as a layer to connect two subnetworks, I/Q estimation network (IQENET) and CFO estimation network (CFOENET). By embedding the physical model, the network gains the capability to learn the features of coupling effects during the training process, as the image signals caused by I/Q imbalance are removed before CFO estimation. A phased training strategy is also proposed. In the first phase, the two subnetworks are pre-trained independently. In the second phase, they are fine-tuned jointly to deal with the coupling effects. Simulation results show that the proposed network achieves high estimation accuracy while maintaining low complexity. Full article

Background: Polyols are widely used as tablet diluents due to their high solubility, favourable taste, and ability to form robust tablets. Thus, commercially available polyols, such as mannitol and isomalt, can be considered for the preparation of low-drug-dose formulations with a high polyol load. Methods/Results: This study investigated spray-dried mannitol (Mannogem^® XL Opal SD and Pearlitol^® 200 SD) and fluid-bed granulated isomalt (galenIQ™ 720 and galenIQ™ 721) at magnesium stearate levels of 0.5 and 3.0 wt.% and consolidation pressures of 100 and 300 MPa. During the tableting of 100 consecutive tablets, materials displayed different ejection force profiles: galenIQ™ 720 and galenIQ™ 721 demonstrated low and stable ejection pressures; Mannogem^® displayed a lubricant- and compaction pressure-dependent profile, whereas Pearlitol^® produced the highest ejection forces, particularly at 0.5 wt.% magnesium stearate. To elucidate these differences, the used materials were characterised in terms of SEM imaging, moisture content, surface area and porosity analysis, particle size distribution, pXRD, and densification kinetics. Using a compaction simulator, key parameters including pressure–displacement profiles, mean yield pressure, and strain rate sensitivity of the unlubricated materials were experimentally determined, while pressure transmission, residual die-wall pressure, and friction coefficient were computed. Conclusions: The study concluded that variations in tableting properties were primarily governed by moisture content and, for mannitol grades, by manufacturing method-dependent differences in particle microstructure. These insights provide guidance for the rational selection of polyol excipients and appropriate lubrication levels in direct compression tablet formulations. Full article

The evolution from fifth generation (5G) to sixth generation (6G) networks demands a paradigm shift from AI-assisted functionalities to AI-native orchestration, where intelligence is intrinsic to the radio access network (RAN). This work introduces two AI-based enablers for PHY-layer awareness: (i) a waveform classifier that distinguishes orthogonal frequency-division multiplexing (OFDM) and orthogonal time frequency space (OTFS) signals directly from in-phase/quadrature (IQ) samples, and (ii) a numerology detector that estimates subcarrier spacing, fast Fourier transform (FFT) size, slot duration, and cyclic prefix type without relying on higher-layer signaling. Experimental evaluations demonstrate high accuracy, with waveform classification achieving 99.5% accuracy and numerology detection exceeding 99% for most parameters, enabling robust joint inference of waveform and numerology features. The obtained results confirm the feasibility of AI-native spectrum awareness, paving the way toward self-optimizing, context-aware, and adaptive 6G wireless systems. Full article

Wideband spectrum sensing is a crucial technology for the efficient utilization of limited frequency resources in cognitive radio. While deep learning models have yielded promising results, they typically rely on either time-domain (I/Q) or frequency-domain (FFT) data alone, which can limit their performance. This study proposes IPFSCNN (IQ-Parallel FFT-Serial CNN), a novel asymmetric hybrid architecture that synergistically fuses both data representations. The key idea of its design is an asymmetric architecture that employs two specialized streams: a parallelized branch to efficiently capture temporal features from I/Q data, and a deep serial branch to extract spectral patterns from FFT data. These complementary features are fused to perform a multi-label classification task. Experiments on an LTE-M dataset demonstrate that the proposed IPFSCNN achieves a higher detection performance than state-of-the-art models, including DeepSense and ParallelCNN, particularly in low signal-to-noise ratio conditions. Furthermore, IPFSCNN achieves this superior accuracy while maintaining high computational efficiency, requiring 15% fewer parameters and only one-third of the multiply-accumulate (MAC) operations compared to the DeepSense model. Crucially, a comprehensive ablation study validates this asymmetric design, proving that the proposed ‘IQ-Parallel FFT-Serial’ combination is demonstrably superior to other hybrid configurations. Full article

The Laser Doppler Vibrometer (LDV) is widely used in precision vibration measurement due to its non-contact nature and high accuracy. However, when measuring non-cooperative targets, the internal stray light in the LDV interferes with the target’s return light, creating competition with the reference light, a phenomenon known as interference competition. This issue is particularly prominent in integrated transceiver LDV systems, where the backscattered light from the lens can be comparable in intensity to the target’s return light, significantly degrading phase extraction accuracy and limiting the LDV’s applicability. To address this challenge, this paper proposes a noise suppression algorithm based on the In-phase and Quadrature (IQ) demodulation. The algorithm uses the power spectrum within each frame’s relevant frequency band as an evaluation metric and employs the Three-point Probe Extremum Localization (3P-PEL) method to estimate the amplitude and phase of the stray light interference with the reference light in real time. This enables the accurate extraction of the interference signal between the measurement light and the reference light. Both simulations and experiments validate the effectiveness of the proposed method. The simulation results demonstrate that when the stray-to-measurement power ratio is below 0.25, the proposed algorithm can suppress spurious signals induced by multi-beam interference by more than 25 dB, while experimental results show it can reduce such signals below the LDV’s noise floor in various motion scenarios. The proposed algorithm holds potential applications in laser interferometry and effectively enhances LDV measurement accuracy. Full article

c-Jun N-terminal kinase (JNK) activation has been shown to play a crucial role in the development of various types of cancer. IQ-1S is a JNK inhibitor based on the 11H-indeno[1,2-b]quinoxalin-11-one scaffold. The aim of this study was to investigate the antiproliferative effect of IQ-1S on MCF7 breast cancer cells in both two-dimensional (2D) monolayer and 3D multicellular spheroid test-systems. Non-adherent, non-tethered 3D objects were generated from single MCF7 breast cancer cells in a hydrogel array. IQ-1S was added directly to the cells seeded in the hydrogel array. MCF7 spheroids were grown for 7 days. Spheroid size, growth rate, and morphology were assessed at single-object resolution. The study revealed significant differences in the size, morphology and some vital characteristics of breast cancer 3D objects when treated with the JNK inhibitor compared to vehicle (dimethyl sulfoxide)-treated controls. Spheroids treated with IQ-1S (20 μM) after 7 days are significantly smaller than the control objects. This difference was not attributable to variations in the initial number of cells seeding for the spheroid formation. Morphological examinations showed that 3D multicellular objects grown from IQ-1S-treated cells lose their regular, round morphology, in contrast to control spheroids. Furthermore, cell proliferation measured using a label-free impedance monitoring platform was reduced in monolayer (2D) culture of MCF7 cells in the presence of 10 and 20 μM IQ-1S. MCF7 cells in 2D culture treated with IQ-1S (20 μM) for 72 and 153 h showed a significant increase in apoptosis as assessed by flow cytometry with annexin V/propidium iodide staining. An in silico evaluation showed that compound IQ-1S has generally satisfactory ADME (absorption, distribution, metabolism, and excretion) properties and high bioavailability. We conclude that IQ-1S effectively inhibits the growth of 3D spheroids and MCF7 cells in 2D culture and has a high potential for use in preclinical tumor growth models. Full article