bluuebunny/crossref_metadata_2025 huggingface dataset:
Acnocure, a Synergistic Anti-Microbial and Anti-Inflammatory Combination of Thymol and Curcuma Turmerones, Formulation and Time-Kill Studies Against C. acnes
Authors: Pannakal Steve Thomas , Prasad Arpita , Phadke Snehal , Sanyal Aryasekhar , Butti Srinu , Khodr Ahmad , Morain Cynthia , Agnaou Reda , Shariff Rezwan , Benazzouz Adrien , Patil Ketan , Chawda Kirit , John Sherluck , Roy Dhimoy , Sharma Vishal
Published: February 2025

Today, most anti-acne treatments employ topical and systemic antibiotics such as erythromycin and clindamycin, which induce cutaneous dysbiosis with adverse side effects to the skin’s normal microbiota, consequently leading to the emergence of antimicrobial resistance. In our quest to discover natural anti-acne bioactives as alternatives, we undertook a research program with the aim to identify a new blend of active ingredients based on the monoterpene phenol moiety. Within this program, we evaluated the in vitro anti-acne efficacy of thymol, Curcuma turmerones and their patented combination “Acnocure” in a cosmetic formulation. The minimum inhibitory concentration (MIC) of Acnocure against C. acnes (ATCC 6919), S. aureus (ATCC 6538), S. epidermidis (ATCC 12228) and C. freneyi (CIP 52.16) was determined to be 0.32, 0.26, 0.47 and 0.11 mg/mL, respectively. In the time-kill curve study against C. acnes, Acnocure, containing thymol 0.25% and 0.1% Curcuma turmerone as well as thymol 0.1% and 0.1% Curcuma turmerone in a cosmetic simplex formulation, demonstrated rapid bactericidal activity with a 4.7 log reduction at pH 5.5, occurring within just two hours of the study and lasting for over 24 h. The killing efficacy was similar to our cosmetic reference benchmark, Effaclar DUO serum, used in the same study. Additionally, thymol, Curcuma turmerones and Acnocure were evaluated in an anti-inflammatory efficacy assay in lipopolysaccharide (LPS)-primed U937 macrophages model and demonstrated moderate inhibition of interleukin-1β (IL-1β) at 100 µg/mL and significant inhibition of prostaglandin E-2 (PGE-2) at 1 µg/mL, respectively. Further evidence gathered on thymol and Curcuma turmerones in an IL-1α-stimulated dermal fibroblast model showed >90% inhibition of PGE-2 release between 2 µg/mL and 30 µg/mL concentrations. These promising results position Acnocure as a natural alternative for the replacement of synthetic corticosteroids and antibiotics with potent anti-acne skincare properties.
Antioxidant and Photoprotective Activity of Bromelain Cream: An In Vitro and In Vivo Study
Authors: Marissa Zahra , Mita Soraya , Kusumawulan Cahya , Sriwidodo Sriwidodo
Published: February 2025

Bromelain, a natural enzyme derived from pineapple, is known for its antioxidant properties, and its potential as a photoprotective agent has garnered interest in skincare applications. The primary objective of this research was to evaluate and optimize the effectiveness of bromelain-based creams in providing antioxidant and photoprotective protection against ultraviolet (UV) radiation. Antioxidant activity was assessed using the DPPH radical scavenging assay, and the Sun Protection Factor (SPF) was determined in vitro and in vivo to evaluate photoprotective activity. The results revealed that bromelain exhibited strong antioxidant activity. Photoprotection, as measured by SPF, the formulation F3, which combined bromelain with other UV filters, exhibited the highest SPF values of 22.043 ± 0.277 (in vitro) and 21.3 ± 2.901 (in vivo), indicating enhanced photoprotective efficacy. This improvement in SPF was likely due to the synergistic effect of bromelain with the UV filters Octyl Methoxycinnamate (OMC). The findings suggest a positive correlation between antioxidant activity and photoprotection, with bromelain’s antioxidant properties contributing to its overall photoprotective effect. Bromelain may be used on people without causing skin or eye irritation. This study supports the potential of bromelain-based creams as dual-action skincare formulations, offering both antioxidant and UV protection.
Invasive Aquatic Weeds Suppress Predator–Prey Cascades: Evidence from a Mesocosm Study
Authors: Froneman Pierre William
Published: February 2025

Submerged macrophytes can profoundly influence interactions between aquatic predators and their prey due to changes in foraging efficiencies, pursuit time and swimming behaviors of predator–prey participants. Water hyacinth, Eichhornia crassipes (Mart.) Solms-Laub. (Pontederiaceae), is the most widely distributed of the aquatic invasive weeds in South Africa. This invasive weed contributes to changes in physicochemical (turbidity, temperature and water column stratification) and biological (total chlorophyll-a (Chl-a) concentrations and species composition and distribution of vertebrates and invertebrates) variables within freshwater systems of the region. The current study assessed the influence of varying levels of water hyacinth cover (0, 25, 50 and 100% treatments) on the total Chl-a concentration, size structure of the phytoplankton community and the strength of the interaction between a predatory notonectid, Enithares sobria, and zooplankton using a short-term 10-day long mesocosm study. There were no significant differences in selected physicochemical (temperature, dissolved oxygen, total nitrogen and total phosphate) variables in these different treatments over the duration of this study (ANOVA; p > 0.05 in all cases). Results of this study indicate that treatment had a significant effect on total Chl-a concentrations and total zooplankton abundances. The increased surface cover of water hyacinth contributed to a significant reduction in total Chl-a concentrations and a significant increase in total zooplankton abundances (ANCOVA; p < 0.05 in both cases). The increased habitat complexity conferred by the water hyacinth root system provided refugia for zooplankton. The decline in total Chl-a concentration and the size structure of the phytoplankton community under elevated levels of water hyacinth cover can therefore probably be related to both the unfavorable light environment conferred by the plant cover and the increased grazing activity of zooplankton. The presence of the water hyacinth thus suppressed a predator–prey cascade at the base of the food web. Water hyacinth may, therefore, have important implications for the plankton food web dynamics of freshwater systems by reducing food availability (Chl-a), changing energy flow and alternating the strength of interactions between predators and their prey.
Is the Transverse Colon Overlooked? Establishing a Comprehensive Colonoscopy Database from a Multicenter Cluster-Randomized Controlled Trial
Authors: Cold Kristoffer Mazanti , Vamadevan Anishan , Heen Amihai , Vilmann Andreas Slot , Rasmussen Morten , Konge Lars , Svendsen Morten Bo Søndergaard
Published: February 2025

Background and Study Aim: Colonoscopy holds the highest volume of all endoscopic procedures, allowing for large colonoscopy databases to serve as valuable datasets for quality assurance. We aimed to build a comprehensive colonoscopy database for quality assurance and the training of future AIs. Materials and Methods: As part of a cluster-randomized controlled trial, a designated, onsite medical student was used to acquire procedural and patient-specific data, ensuring a high level of data integrity. The following data were thereby collected for all colonoscopies: full colonoscopy vides, colonoscope position (XYZ-coordinates), intraprocedural timestamps, pathological report, endoscopist description, endoscopist planning, and patient-reported discomfort. Results: A total of 1447 patients were included from the 1st of February 2022 to the 21st of November 2023; 1191 colonoscopies were registered as completed, 88 were stopped due to inadequate bowel cleansing, and 41 were stopped due to patient discomfort. Of the 1191 completed colonoscopies, 601 contained polypectomies (50.4%), and 590 did not (49.6%). Comparing colonoscopies with polypectomies to those without the withdrawal time (caecum to extubating the scope) was significantly longer for all parts of the colon (p values < 0.001), except the transverse colon (p value = 0.92). The database was used to train an AI, automatically and objectively evaluating bowel preparation. Conclusions: We established the most thorough database in colonoscopy with previously inaccessible information, indicating that the transverse colon differs from the other parts of the colon in terms of withdrawal time for procedures with polypectomies. To further explore these findings and reach the full potential of the database, an AI evaluating bowel preparation was developed. Several research partners have been identified to collaborate in the development of future AIs.
Assessing Image Quality in Multiplexed Sensitivity-Encoding Diffusion-Weighted Imaging with Deep Learning-Based Reconstruction in Bladder MRI
Authors: Cha Seung Ha , Han Yeo Eun , Han Na Yeon , Kim Min Ju , Park Beom Jin , Sim Ki Choon , Sung Deuk Jae , Yoo Seulki , Lan Patricia , Guidon Arnaud
Published: February 2025

Background/Objectives: This study compared the image quality of conventional multiplexed sensitivity-encoding diffusion-weighted imaging (MUSE-DWI) and deep learning MUSE-DWI with that of vendor-specific deep learning (DL) reconstruction applied to bladder MRI. Methods: This retrospective study included 57 patients with a visible bladder mass. DWI images were reconstructed using a vendor-provided DL algorithm (AIRTM Recon DL; GE Healthcare)—a CNN-based algorithm that reduces noise and enhances image quality—applied here as a prototype for MUSE-DWI. Two radiologists independently assessed qualitative features using a 4-point scale. For the quantitative analysis, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), signal intensity ratio (SIR), and apparent diffusion coefficient (ADC) of the bladder lesions were recorded by two radiologists. The weighted kappa test and intraclass correlation were used to evaluate the interobserver agreement in the qualitative and quantitative analyses, respectively. Wilcoxon signed-rank test was used to compare the image quality of the two sequences. Results: DL MUSE-DWI demonstrated significantly improved qualitative image quality, with superior sharpness and lesion conspicuity. There were no significant differences in the distortion or artifacts. The qualitative analysis of the images by the two radiologists was in good to excellent agreement (κ ≥ 0.61). Quantitative analysis revealed higher SNR, CNR, and SIR in DL MUSE-DWI than in MUSE-DWI. The ADC values were significantly higher in DL MUSE-DWI. Interobserver agreement was poor (ICC ≤ 0.32) for SNR and CNR and excellent (ICC ≥ 0.85) for SIR and ADC values in both DL MUSE-DWI and MUSE-DWI. Conclusions: DL MUSE-DWI significantly enhanced the image quality in terms of lesion sharpness, conspicuity, SNR, CNR, and SIR, making it a promising tool for clinical imaging.
Optimal Timing of Colostomy Reversal Following Hartmann’s Procedure: A Retrospective Analysis of Postoperative Outcomes
Authors: Popazu Constantin , Voicu Dragoș , Firescu Dorel , Grigore Ionica , Toma Alexandra , Derihaci Răzvan Petru
Published: February 2025

Background/Objectives: Hartmann’s procedure is commonly employed to manage complications of acute sigmoid diverticulitis, such as perforation or abscess formation. However, determining the optimal timing for colostomy reversal remains a topic of debate. This study aims to evaluate the effect of early versus delayed colostomy reversal on postoperative outcomes, focusing on complications, hospital stay duration, and readmission rates. Methods: A retrospective cohort study was conducted on 148 patients who underwent Hartmann’s procedure for acute sigmoid diverticulitis at a single tertiary care center between 2014 and 2023. Participants were grouped based on the timing of colostomy reversal: early (45–120 days), intermediate (121–180 days), and late (>180 days). Data on complications, hospital stay length, and readmissions were analyzed. Results: Early reversal was associated with fewer postoperative complications, shorter hospital stays, and reduced readmissions compared to delayed reversal. The late reversal group had higher rates of complications, longer hospital stays, and a higher need for reintervention. Advanced age and comorbidities, such as cardiovascular disease and diabetes, were significant predictors of poor outcomes, contributing to delayed reversal. Logistic regression analysis indicated that late reversal was independently associated with higher complication rates. Conclusions: Early colostomy reversal within 45–120 days following Hartmann’s procedure is associated with improved postoperative outcomes, including fewer complications and a shorter hospital stay. The timing of colostomy reversal should be individualized based on patient health status, with early reversal preferred for those without significant comorbidities. Further prospective research is needed to confirm these findings and refine guidelines for optimal reversal timing.
The Severity of Carotid Calcifications, but Not Fibroblast Growth Factor 23, Is Associated with Mortality in Hemodialysis: A Single Center Experience
Authors: Moldovan Diana
Published: February 2025

Background. The study goal was to assess the mortality effect of carotid vascular calcifications (VC), of fibroblast growth factor 23 (FGF-23), mineral markers, and comorbidities in hemodialysis (HD) patients. Methods. The influence of carotid VC severity, FGF-23, laboratory markers, clinical features, and comorbidities on mortality was analyzed in a cohort of 88 HD patients. The follow-up period lasted 8 years. The cut-off value for carotid VC was 4 for all-cause and cardiovascular mortality. Results. Carotid VC, diabetes, low serum albumin, high serum C-reactive protein (CRP), and the presence of cardiovascular diseases are associated with all-cause and cardiovascular mortality. Carotid VC score over 4 was an independent predictor of all-cause and cardiovascular mortality, along with diabetes, low albumin, and high CRP. FGF-23 was not found to be predictable for the study outcomes. Conclusions. The study documented in a cohort of patients prevalent in chronic HD that carotid VC predicts all-cause and cardiovascular mortality at 8 years and improves risk stratification, but FGF-23 is not associated with mortality. Other risk factors for all-cause and cardiovascular mortality were diabetes, inflammation, and malnutrition. However, future efforts are needed to assess whether a risk-based approach, including VC screening, improves survival.
The Effect of MRI Exposure on the Shear Bond Strength and Adhesive Remnant Index of Different Bracket Types
Authors: Šimunović Luka , Stojanović Jakov , Tečić Katarina , Zadravec Dijana , Meštrović Senka
Published: February 2025

Background/Objectives: Magnetic resonance imaging (MRI) is widely used in diagnostics, but its effects on orthodontic materials remain a concern. This study aimed to evaluate the impact of MRI exposure at 1.5 T and 3 T on the shear bond strength (SBS) and adhesive remnant index (ARI) of different orthodontic bracket types (metal, self-ligating, and ceramic). Methods: A total of 90 extracted human premolars were divided into three groups (control, 1.5 T, and 3 T MRI exposure). The three bracket types were bonded using Transbond XT adhesive and subjected to standardized polymerization. MRI scans were conducted using 1.5 T and 3 T machines with clinically relevant sequences. SBS was measured using a universal testing machine, and the ARI was assessed under a stereomicroscope. Statistical analysis was performed using Kruskal–Wallis and chi-square tests. Results: MRI exposure influenced SBS and the ARI differently across bracket types. Firstly, 3 T MRI exposure significantly reduced SBS in self-ligating (p = 0.017) and ceramic brackets (p = 0.014) compared to the control, whereas metal brackets showed no significant changes. ARI scores varied across MRI conditions, with metal and self-ligating brackets showing increased adhesive retention at higher field strengths. No significant differences were observed in ARI scores for ceramic brackets across MRI conditions. Conclusions: The clinical importance of understanding these results is that both patients and clinicians must be aware of inevitable changes that occur in SBS during MRI, since exposure to high-field MRI, particularly 3 T, may alter bond strength and adhesive failure characteristics.
Energy Consumption Minimization for UAV-Assisted Network in Hotspot Area
Authors: Zhang Jinxi , Maimaiti Saidiwaerdi , Gao Weidong , Zhang Kaisa
Published: February 2025

Unmanned aerial vehicles (UAVs) play a crucial role in enhancing network coverage and capacity, especially in areas with high user density or limited infrastructure. This paper proposes an effective UAV-assisted offloading framework to minimize the energy consumption of both users and UAVs in an air-to-ground (A2G) network. First, UAVs are deployed by jointly considering the user distribution and guaranteeing the quality of service (QoS) of users. Further, user association, power control, and bandwidth allocation are jointly optimized, aiming to minimize the power consumption of users. Considering user mobility, the positions of UAVs are continuously refined using the double deep Q-network (DDQN) algorithm to reduce the weighted energy consumption of users and UAVs. Simulation results show that the proposed algorithm has better performance in reducing the total energy consumption compared with benchmark schemes.
Adaptive Path Guidance Law for a Small Fixed-Wing UAS with Bounded Bank Angle
Authors: Kim Suhyeon , Jung Dongwon
Published: February 2025

This study deals with the path-following guidance of a fixed-wing unmanned aerial system (UAS) in conjunction with parameter adaptation. Utilizing a backstepping control design approach, a path-following control algorithm is formulated for the roll command, accounting for the approximated closed-loop roll control. The inaccurate time constant is estimated by employing a parameter adaptation algorithm. The proposed guidance algorithm is first validated via the hardware-in-the-loop simulation environment, followed by flight tests on an actual UAV platform to demonstrate that both tracking performance and control robustness are improved over various shape of reference paths.
Commanded Filter-Based Robust Model Reference Adaptive Control for Quadrotor UAV with State Estimation Subject to Disturbances
Authors: Ahmed Nigar , Alrasheedi Nashmi
Published: February 2025

Unmanned aerial vehicles must achieve precise flight maneuvers despite disturbances, parametric uncertainties, modeling inaccuracies, and limitations in onboard sensor information. This paper presents a robust adaptive control for trajectory tracking under nonlinear disturbances. Firstly, parametric and modeling uncertainties are addressed using model reference adaptive control principles to ensure that the dynamics of the aerial vehicle closely follow a reference model. To address the effects of disturbances, a modified nonlinear disturbance observer is designed based on estimated state variables. This observer effectively attenuates constant, nonlinear disturbances with variable frequency and magnitude, and noises. In the next step, a two-stage sliding mode control strategy is introduced, incorporating adaptive laws and a commanded-filter to compute numerical derivatives of the state variables required for control design. An error compensator is integrated into the framework to reduce numerical and computational delays. To address sensor inaccuracies and potential failures, a high-gain observer-based state estimation technique is employed, utilizing the separation principle to incorporate estimated state variables into the control design. Finally, Lyapunov-based stability analysis demonstrates that the system is uniformly ultimately bounded. Numerical simulations on a DJI F450 quadrotor validate the approach’s effectiveness in achieving robust trajectory tracking under disturbances.
Three Optima of Thermoelectric Conversion: Insights from the Constant Property Model
Authors: Raux Paul , Goupil Christophe , Verley Gatien
Published: February 2025

Starting from Ioffe’s description of a thermoelectric converter, we recover the optimal working points of conversion: the point of maximum efficiency and the one of maximal power. Inspired by biological converters’ optimization, we compute a third optimal point associated with cost of energy (COE). This alternative cost function corresponds to the amount of heat exchanged with the cold reservoir per unit of electric current used. This work emphasizes the symmetry between the efficiency and performance coefficient of the electric generator and heat pump modes. It also reveals the relation between their optimal working points.
The Glass Transition: A Topological Perspective
Authors: Vesperini Arthur , Franzosi Roberto , Pettini Marco
Published: February 2025

Resorting to microcanonical ensemble Monte Carlo simulations, we study the geometric and topological properties of the state space of a model of a network glass-former. This model, a Lennard-Jones binary mixture, does not crystallize due to frustration. We have found two peaks in specific heat at equilibrium and at low energy, corresponding to important changes in local ordering. These singularities were accompanied by inflection points in geometrical markers of the potential energy level sets—namely, the mean curvature, the dispersion of the principal curvatures, and the variance of the scalar curvature. Pinkall’s and Overholt’s theorems closely relate these quantities to the topological properties of the accessible state-space manifold. Thus, our analysis provides strong indications that the glass transition is associated with major changes in the topology of the energy level sets. This important result suggests that this phase transition can be understood through the topological theory of phase transitions.
Game Mechanics and Artificial Intelligence Personalization: A Framework for Adaptive Learning Systems
Authors: Naseer Fawad , Khan Muhammad Nasir , Addas Abdullah , Awais Qasim , Ayub Nafees
Published: February 2025

The phenomenal growth of digital learning platforms has brought new learner engagement and retention challenges to higher education. This study proposes a framework that integrates game mechanics—leveling systems, badges, and timely feedback—with artificial intelligence (AI)-driven personalization to meet the challenges of enhanced adaptability, motivation, and learning outcomes in online environments. Key design elements were identified through literature reviews and consultations with instructional design experts, leading to the development an adaptive learning platform prototype. The prototype underwent an eight-week pilot study with 250 Prince Sattam Bin Abdulaziz University (PSAU) students randomly assigned to a control group (non-adaptive system) or an experimental group (adaptive system). Data sources included pre- and post-tests, platform engagement analytics, and learner perception surveys. The results showed that the adaptive group outperformed the control group in the post-test scores (M = 85.2, SD = 6.4 vs. M = 78.5, SD = 7.2) and motivation levels (M = 4.2, SD = 0.7 vs. M = 3.6, SD = 0.8). Additionally, 82% of the adaptive group achieved mastery-level performance compared to 64% in the control group. These findings demonstrate the potential of integrating game mechanics and AI-driven personalization to transform digital learning, offering a roadmap for scalable, data-driven adaptive platforms. Future research will address long-term retention and diverse subject applications.
Contribution of Microlearning in Basic Education: A Systematic Review
Authors: Silva Elaine Santana , Costa Woska Pires da , Lima Junio Cesar de , Ferreira Julio Cesar
Published: February 2025

This systematic review analyzed the role of microlearning in basic education, identifying the most widely used Digital Information and Communication Technologies, relevant learning theories, and the role of social technologies from a Science, Technology, Society, and Environment (STSE) perspective. Following PRISMA 2020, searches were conducted in Web of Science, Scopus, ERIC, and IEEE Xplore databases. Studies on microlearning were selected based on previously defined eligibility criteria. The review process in Rayyan involved deduplication, screening, and full-text analysis. Data were qualitatively analyzed using content analysis, and methodological quality was assessed with CASP and the Downs and Black. The findings highlight that microlearning, integrated with digital tools such as online platforms, mobile apps, and short videos, significantly enhances student motivation, performance, and interaction; content in short modules facilitates knowledge retention and connects concepts to real-life situations. Promising trends include mobile technologies and gamification, which foster active, meaningful learning. Grounded in theories like Self-Determination, Constructionism, and Constructivism, microlearning personalizes teaching and promotes engagement, critical thinking, and accessibility, contributing to inclusive and sustainable education. From a STSE perspective, social technologies enhance autonomy, social interaction, and ethical–environmental awareness. In Brazil, further research on digital platforms and gamified strategies is needed to drive innovative educational practices.
A Novel Reformulation of LRRM Calibration Algorithm Using the Pseudo Cascading Relationship
Authors: Zhao Wei , Wang Lingjia , Cheng Chunyue
Published: February 2025

In this paper, by combining the error network mapping model with the pseudo cascading relationship, the line-reflect-reflect-match (LRRM) calibration of a vector network analyzer (VNA) is reformulated in a novel and simple way. Based on the error network mapping model in the hybrid parameter representation, the raw measured waves are mapped to the equivalent voltages and currents at the test ports. To describe the mapping relationship, the hybrid parameter matrix is constructed by measurement of the thru standard, and only three calibration coefficients, namely x, y, and z, are required to be solved for the VNA calibration. Meanwhile, when measuring the double one-port standard, the pseudo cascading relationship is defined and used to establish the calibration equation in the form of matrix multiplication. By this means, an eigenvalue problem similar to that in thru-reflect-line (TRL) calibration is derived after a series of simple matrix operations. Solving the eigenvalue problem enables the calibration coefficients x and y to be calculated in a convenient manner. Then, by utilizing the lumped models of partial calibration standards, the last calibration coefficient z is determined. Once all three calibration coefficients are obtained, the LRRM calibration is completed. Finally, on-wafer experiments are performed to verify the formulated LRRM calibration.
LIMETREE: Consistent and Faithful Surrogate Explanations of Multiple Classes
Authors: Sokol Kacper , Flach Peter
Published: February 2025

Explainable artificial intelligence provides tools to better understand predictive models and their decisions, but many such methods are limited to producing insights with respect to a single class. When generating explanations for several classes, reasoning over them to obtain a comprehensive view may be difficult since they can present competing or contradictory evidence. To address this challenge, we introduce the novel paradigm of multi-class explanations. We outline the theory behind such techniques and propose a local surrogate model based on multi-output regression trees—called LIMETREE—that offers faithful and consistent explanations of multiple classes for individual predictions while being post-hoc, model-agnostic and data-universal. On top of strong fidelity guarantees, our implementation delivers a range of diverse explanation types, including counterfactual statements favored in the literature. We evaluate our algorithm with respect to explainability desiderata, through quantitative experiments and via a pilot user study, on image and tabular data classification tasks, comparing it with LIME, which is a state-of-the-art surrogate explainer. Our contributions demonstrate the benefits of multi-class explanations and the wide-ranging advantages of our method across a diverse set of scenarios.
Multiple Fault-Tolerant Control of DC Microgrids Based on Sliding Mode Observer
Authors: Sun Jian , Li Zewen , Yang Minsheng
Published: February 2025

Different locations and types of faults affect the safe and reliable operation of DC microgrids. Therefore, this paper proposes a secondary multiple fault-tolerant control scheme for a DC microgrid based on a sliding mode observer to ensure the voltage is restored to the rated value and realize the proportional current sharing of all sources. Firstly, the secondary control model of the DC microgrid is established, considering the multiple faults of actuators and sensors simultaneously. Secondly, the system model is transformed into two subsystems by bilinear coordinate transformation, and multiple faults decoupling between the sensor and actuator is realized. Then, two sliding mode observers are designed for the two transformed subsystems. The sliding mode variable structure equivalent principle is used to reconstruct the faults at different positions without knowing the fault models in advance, which is convenient for subsequent processing. Then, the fault-tolerant controller based on the sliding mode observer is designed, which uses the reconstructed value to offset the influence of sensor and actuator faults on the DC microgrid and realizes the fault-tolerant control of the DC microgrid. Finally, the effectiveness of the proposed control strategy is verified by experiments.
Compact Dual-Band Rectifier with Self-Matched Branches Using Comprehensive Impedance Control
Authors: Barakat Adel , Jordan Willy , Pokharel Ramesh K.
Published: February 2025

The use of multi-band matching for rectifiers leads to design complexity. Instead, recent advancements suggested self-matched branches combined in parallel to enable multi-band operation. However, this method controls only the imaginary part. In this paper, we propose an efficient dual-band rectifier with compact realization. The rectifier consists of two self-matched parallel branches. Each branch provides comprehensive impedance control over real and imaginary parts in the corresponding band independent of the design frequency. The branch impedance matching is analyzed theoretically, and design equations are presented. To verify the proposed theory, a compact dual-band rectifier was fabricated with a compact area of only 0.42 cm2 after excluding the area required for the RF connector. The measured RF-DC power conversion efficiency (PCE) was >50% for input power (Pin) ranging from −5.5 dBm to 11 dBm at 390 MHz with a peak of 69%. Also, the PCE was >50% for P_in ranging from −4 dBm to 12 dBm at 690 MHz with a peak of 68%. The fabricated rectifier operates with a wide load range from 0.5 KΩ to 3 KΩ with PCE > 50% at both bands when P_in = 5 dBm.
Face of Cross-Dissimilarity: Role of Competitors’ Online Reviews Based on Semi-Supervised Textual Polarity Analysis
Authors: Shan Siqing , Yang Yangzi , Li Yinong
Published: February 2025

Existing online review research has not fully captured consumer purchasing behavior in complex decision-making environments, particularly in contexts involving multiple product comparisons and conflicting review perspectives. This study thoroughly investigates the impact on focal product purchase decisions when consumers compare multiple products and face information inconsistency. Based on online review data from JD.com, we propose a semi-supervised deep learning model to analyze consumers’ sentiment polarity toward product attributes. The method establishes implicit relationships between labeled and unlabeled data through consistency regularization. Subsequently, we conceptualize three types of online review dissimilarity factors, rating-sentiment dissimilarity, cross-review dissimilarity, and brand dissimilarity, and employ regression models to examine the impact of competing products’ online reviews on focal product sales. The results indicate that by employing a semi-supervised deep learning approach, unlabeled data are annotated with pseudo-labels and utilized for model training, achieving more accurate sentiment classification than using labeled data alone. Moreover, positive (negative) sentiment attributes of competing products have a significant negative (positive) effect on focal product purchases. Online review dissimilarity moderates the spillover effects of competing products. Notably, these spillover effects are more pronounced when competing products are from the same brand compared to different brands. The research findings not only highlight the heterogeneous effects of positive and negative sentiments but also provide a new perspective for examining dissimilarity, enriching the understanding of online review spillover effects and the role of dissimilarity, while offering practical guidance for resource allocation decisions by companies and platforms.
Design and Performance Verification of A-HFM Signals for Simultaneous Frame Detection, Cell ID Assignment, and Doppler Estimation in AUVs Using Multiple Surface Buoys
Authors: Park Sae-Yong , Chung Tae-Geon , Im Tae-Ho
Published: February 2025

With the advancement of artificial intelligence, the inference capabilities of Autonomous Underwater Vehicles (AUVs) have significantly improved, leading to growing interest in AUV applications. To ensure reliable operations, the field of underwater communications demands robust schemes that account for AUV mobility and enable the formation of underwater cellular networks. Conventional approaches using Linear Frequency Modulation (LFM) and Zadoff–Chu sequence (ZCS) sequences for frame detection and Cell ID (CID) assignment degrade substantially under severe Doppler conditions. In particular, AUVs experience pronounced Doppler shifts due to their mobility in underwater channels. In this study, we propose a methodology in which distinct Superimposed Adjusted-HFM (SA-HFM) signals are assigned to multiple buoys, allowing AUVs to jointly perform frame detection, CID assignment, and Doppler estimation in challenging underwater environments. To validate the proposed scheme, an ocean experiment was conducted in the East Sea of the Republic of Korea. The results demonstrate that the SA-HFM-based signals successfully achieved frame detection, CID assignment, and Doppler estimation at distances ranging from 500 m to approximately 2 km, even when the AUV moved at speeds of 1.02–1.54 m/s. The experimental results indicate that the proposed approach can offer robust underwater communication and facilitate the deployment of underwater cellular networks for mobile AUV operations.
Machine Learning for Quality Diagnostics: Insights into Consumer Electronics Evaluation
Authors: Firza Najada , Bakiu Anisa , Monaco Alfonso
Published: February 2025

In the era of digital commerce, understanding consumer opinions has become crucial for businesses aiming to tailor their products and services effectively. This study investigates acoustic quality diagnostics of the latest generation of AirPods. From this perspective, the work examines consumer sentiment using text mining and sentiment analysis techniques applied to product reviews, focusing on Amazon’s AirPods reviews. Using the naïve Bayes classifier, a probabilistic machine learning approach grounded in Bayes’ theorem, this research analyzes textual data to classify consumer reviews as positive or negative. Data were collected via web scraping, following ethical guidelines, and preprocessed to ensure quality and relevance. Textual features were transformed using term frequency-inverse document frequency (TF-IDF) to create input vectors for the classifier. The results reveal that naïve Bayes provides satisfactory performance in categorizing sentiment, with metrics such as accuracy, sensitivity, specificity, and F1-score offering insight into the model’s effectiveness. Key findings highlight the divergence in consumer perception across ratings, identifying sentiment drivers such as noise cancellation quality and product integration. These insights underline the potential of sentiment analysis in enabling companies to address consumer concerns, improve offerings, and optimize business strategies. The study concludes that such methodologies are indispensable for leveraging consumer feedback in the rapidly evolving digital marketplace.
DeMatchNet: A Unified Framework for Joint Dehazing and Feature Matching in Adverse Weather Conditions
Authors: Liu Cong , Zhang Zhihao , He Yiting , Liu Min , Hu Sheng , Liu Hongzhang
Published: February 2025

Current advancements in image processing technologies have led to significant progress; however, adverse weather conditions, including haze, snow, and rain, often degrade image quality, which in turn impacts the performance of deep learning-based image matching algorithms. Most existing methods attempt to correct blurred images prior to target detection, which increases network complexity and may result in the loss of potentially crucial information. To better integrate image restoration and image matching tasks, this paper presents DeMatchNet, an end-to-end integrated network framework that seamlessly combines the feature fusion attention network for single image dehazing (FFA-Net) dehazing module with the detector-free local feature matching with transformers (LoFTR) feature matching module. The proposed framework first designs an attention-based feature fusion module (FFM), which effectively merges the original hazy features with the dehazed features. This ensures that the generated dehazed features not only have improved visual quality, but also provide higher-quality input for subsequent feature matching. Subsequently, a feature alignment module (FA) performs scale and semantic adjustments on the fused features, enabling efficient sharing with the LoFTR module. This deep collaboration between dehazing and feature matching significantly reduces computational redundancy and enhances the overall performance. Experimental results on synthetic hazy datasets (based on MegaDepth and ETH3D) and real-world hazy datasets demonstrate that DeMatchNet outperforms the existing methods in terms of matching accuracy and robustness, showcasing its superior performance under challenging weather conditions.
FPGA Implementation of Nonlinear Model Predictive Control for a Boost Converter with a Partially Saturating Inductor
Authors: Ravera Alessandro , Oliveri Alberto , Lodi Matteo , Storace Marco
Published: February 2025

Enhancing power density is a primary objective in electronic power converters. This can be accomplished by employing smaller inductors operating in partial magnetic saturation. In this study, an embedded digital controller is proposed, based on nonlinear model predictive control (NMPC), for the regulation of a DC–DC boost converter, exploiting a partially saturating inductor. The NMPC prediction model exploits a behavioral inductor model that accounts for magnetic saturation and losses and allows the converter regulation while enforcing constraints. The NMPC controller is implemented on a field programmable gate array (FPGA), demonstrating its real-time feasibility while successfully controlling a boost converter operating at switching frequencies up to 80 kHz. Hardware–software co-simulation results show accurate voltage regulation and constraint satisfaction, even under partial magnetic saturation.
A Measurement Model for Visual Complexity in HCI: Focusing on Visual Elements in Mobile GUI Design
Authors: Zhang Ruiqi , Son Oo-Sung
Published: February 2025

Visual complexity has significant research value across various fields, influencing both first impressions and user experiences. In the context of human–computer interactions, studies on visual complexity in user interface design have evolved progressively alongside technological advancements, and the quantification of visual complexity has been demonstrated to be significant. However, existing research has primarily focused on web interfaces. In contrast, mobile interfaces have less maneuverable space and rely on finger interactions, necessitating more targeted studies. Additionally, while model research has gradually emerged in recent years, there are still issues regarding the influence weight of each visual element in the mobile interface that need to be addressed. This study employed a multiple linear regression analysis to develop a measurement model based on the visual elements used in mobile GUI design, addressing the weighting of each metric from the designer’s perspective. Different from other studies, this study focused on the independent visual elements of the mobile phone interface. This measurement model offers designers intuitive and quantitative reference metrics, enhancing the efficiency of design iterations. Furthermore, it lays the groundwork for the development of visual-complexity measurement tools.
Precision Hotspot Mitigation in Wafer-Level Electroplating with Novel Auxiliary Electrode Design for Advanced Large-Scale Chip Packaging
Authors: Jiang Tao , Hu Huiyong , Wang He , Yin Qiongling , Lin Pengpeng , Wei Yongyan , Xu Yanan , Wang Yitian , Hong Feng
Published: February 2025

This study introduces a novel, non-rotationally symmetrical auxiliary electrode design aimed at mitigating localized hotspots and enhancing the deposition uniformity in wafer-level electroplating for advanced large-scale chip packaging. The formation of hotspots and deposition non-uniformity, particularly at the wafer edge and in regions with complex die layouts, presents significant challenges in electroplating processes. To address these issues, the proposed auxiliary electrode incorporates a dynamic angle control mechanism, which facilitates the precise, localized modulation of the current density. This innovative design improves the regulation of current distribution in hotspot-prone regions, without compromising the overall stability and uniformity of the wafer-level electroplating process. Extensive numerical simulations were conducted to assess the electrode’s effectiveness in redistributing current density, resulting in a marked reduction in current density at the wafer edge, thereby mitigating over-deposition and enhancing overall uniformity. The simulation results also demonstrated the electrode’s capability for dynamic current flow regulation, enabling localized adjustments only when necessary and minimizing disruptions to the electroplating process. Experimental validation further corroborated the simulation findings, with repeated trials confirming the electrode’s consistent performance in reducing localized over-deposition in hotspot regions while maintaining uniform plating in unaffected areas. These findings underscore the potential of the auxiliary electrode as a robust solution for addressing hotspot formation and deposition uniformity challenges in electroplating, providing a solid foundation for its industrial implementation in advanced chip packaging and related fields.
Similarities: The Key Factors Influencing Cross-Site Password Guessing Performance
Authors: Xie Zhijie , Shi Fan , Zhang Min , Rao Zhihong , Zhou Yuxuan , Ji Xiaoyu
Published: February 2025

Password guessing is a crucial research direction in password security, considering vulnerabilities like password reuse and data breaches. While research has extensively explored intra-site password guessing, the complexities of cross-site attacks, where attackers use leaked data from one site to target another, remain less understood. This study investigates the impact of dataset feature similarity on cross-site password guessing performance, revealing that dataset differences significantly influence guessing success more than model variations. By analyzing eight password datasets and four guessing methods, we identified eight key features affecting guessing success, including general data features like length distribution and specific semantic features like PCFG grammar. Our research reveals that syntactic and statistical patterns in passwords, particularly PCFG features, are most effective for cross-site password guessing due to their strong generalization across datasets. The Spearman correlation coefficient of 0.754 between PCFG feature similarity and guessing success rate indicates a significant positive correlation, unlike the minimal impact of length distribution features (0.284). These findings highlight the importance of focusing on robust semantic features like PCFG for improving password guessing techniques and security strategies. Additionally, the study underscores the importance of dataset selection for attackers and suggests that defenders can enhance security by mitigating feature similarity with commonly leaked data.
Harmonic Interference Resilient Backscatter Communication with Adaptive Pulse-Width Frequency Shifting
Authors: Liu Xu , Dong Wu , Yan Binyang , He Xiaomeng , Peng Linyu , Chen Xin , Chen Da , Wang Wei
Published: February 2025

The last few decades have witnessed the rapid development of passive backscatter technologies, which envision promising cost-efficient ambient Internet of Things (IoT) for various applications, such as distributed solar sensor networks. However, limited by the harmonic interference caused by the conventional frequency-shifting-based backscatter control methods, existing backscatter communication technologies cannot support the growing scale of the network. To tackle this issue, we propose a harmonic interference resilient frequency-shifting technique to compress the harmonics during backscatter communication. Different from conventional backscatter tags that shift the frequency with square waves with a constant pulse width, we dynamically modify the pulse width of the square wave to compress different parts of the harmonic waves. Furthermore, we propose a lightweight communication coding algorithm to enhance the compatibility of our system with backscatter applications. We implement the system with off-the-shelf components and conduct comprehensive experiments to evaluate the performance. The results demonstrate our harmonic interference resilient backscatter system can compress the harmonic interference and reduce the BER (bit error rate) by 70%.
Advances in Autism Spectrum Disorder (ASD) Diagnostics: From Theoretical Frameworks to AI-Driven Innovations
Authors: Syriopoulou-Delli Christine K.
Published: February 2025

This study provides a comprehensive analysis of the evolution of Autism Spectrum Disorder (ASD) diagnostics, tracing its progression from psychoanalytic origins to the integration of advanced artificial intelligence (AI) technologies. The study explores, through scientific data bases like Pub Med, Scopus, and Google Scholar, how theoretical frameworks, including psychoanalysis, behavioral psychology, cognitive development, and neurobiological paradigms, have shaped diagnostic methodologies over time. Each paradigm’s associated assessment tools, such as the Autism Diagnostic Observation Schedule (ADOS) and the Vineland Adaptive Behavior Scales, are discussed in relation to their scientific advancements and limitations. Emerging technologies, particularly AI, are highlighted for their transformative impact on ASD diagnostics. The application of AI in areas such as video analysis, Natural Language Processing (NLP), and biodata integration demonstrates significant progress in precision, accessibility, and inclusivity. Ethical considerations, including algorithmic transparency, data security, and inclusivity for underrepresented populations, are critically examined alongside the challenges of scalability and equitable implementation. Additionally, neurodiversity- informed approaches are emphasized for their role in reframing autism as a natural variation of human cognition and behavior, advocating for strength-based, inclusive diagnostic frameworks. This synthesis underscores the interplay between evolving theoretical models, technological advancements, and the growing focus on compassionate, equitable diagnostic practices. It concludes by advocating for continued innovation, interdisciplinary collaboration, and ethical oversight to further refine ASD diagnostics and improve outcomes for individuals across the autism spectrum.