bluuebunny/crossref_metadata_2025 huggingface dataset:
Precision of Photogrammetry and Intraoral Scanning in Full-Arch Implant Rehabilitation: An In Vitro Comparative Study
Authors: Faria João Carlos , Sampaio-Fernandes Manuel António , Oliveira Susana João , Malheiro Rodrigo , Sampaio-Fernandes João Carlos , Figueiral Maria Helena
Published: January 2025

The objective of this in vitro study is to evaluate and compare the precision of digital impressions obtained using intraoral scanners and photogrammetry devices for full-arch implant-supported oral rehabilitation. Three reference models were created with various spatial distributions of Straumann implants, according to the Caramês I Classification: (i) option A with six implants; (ii) option B with four implants; and (iii) option C with four implants. Thirty digital impressions were taken for each of the reference models: ten with the Intraoral 3Shape Trios 3 scanner, ten with the Medit i500 intraoral scanner, and ten with the PIC Dental photogrammetry device. Intra-group best-fit overlaps were performed between the virtual models obtained, and accuracy was evaluated using root mean square (RMS) values. A significance level of p = 0.05 was defined. Mean values were statistically analyzed using the Kruskal–Wallis test. All scanners studied showed high precision, with RMS values similar for each implant distribution. The PIC Dental photogrammetry device demonstrated the best results for the CCI A and B distributions, with mean values of 11.28 µm and 14.44 µm, respectively. For the CCI C distribution, the 3Shape Trios 3 scanner achieved the best result, with a mean value of 5.96 µm. Among all devices, the implant distribution showing the highest RMS values was the CCI B, with mean values between 14.44 µm and 16.96 µm. The PIC Dental device was the only method that did not exhibit statistically significant differences in RMS values across the different distributions studied, indicating that its performance is unaffected by distribution variations. No statistically significant differences (p < 0.05) were observed in the RMS values among the three types of scanners. Overall, a smaller number of implants and closer distribution between them resulted in improved precision for digital impressions in full-arch implant rehabilitation.
Fault Diagnosis of Rolling Bearings Based on Acoustic Signals in Strong Noise Environments
Authors: Wang Hengdi , Xie Jizhan
Published: January 2025

Compared to vibration sensors, microphones offer several advantages, including non-contact detection, high sensitivity, low cost, and ease of installation. To address the challenges posed by the complex components and significant interference in rolling bearing sound signals, we proposed a fault diagnosis method for rolling bearing acoustic signals based on Secretary Bird Optimization Algorithm (SBOA)-optimized Feature Mode Decomposition (FMD). Initially, a microphone is utilized to collect sound data while the bearing operates, followed by the application of S-FMD (Secretary Bird Optimization Algorithm-optimized Feature Mode Decomposition) to decompose the sound signal and extract components that may contain fault information related to the bearing. The SBOA is employed to adaptively optimize four influencing parameters of FMD: mode number n, filter length L, frequency band cutting number K, and cycle period m. By minimizing envelope entropy as the objective function, we achieve FMD of the bearing sound signal with the assistance of the SBOA. Additionally, this paper introduces an Integrated Signal Evaluation Index (ISEI) to extract potential bearing failure characteristics from the filtered components. Simulation experiments and test results indicate that, compared to Empirical Mode Decomposition, Complementary Ensemble Empirical Mode Decomposition, fixed-parameter FMD, and adaptive variational mode decomposition methods, the proposed approach more effectively extracts weak characteristic information related to early faults in bearing sound signals.
Evidence-Based Exercise Recommendations for the Reduction and Stabilization of Intraocular Pressure: A Practical Guide for Eye Care and Sport Specialists
Authors: Vera Jesús , Morenas-Aguilar María Dolores , García-Ramos Amador
Published: January 2025

The only proven method to manage glaucoma is the reduction and stabilization of intraocular pressure (IOP). A wide range of daily activities has been demonstrated to affect the IOP behavior, and eye care specialists should be aware of their effects for the prevention and treatment of glaucoma. Indeed, the impact of physical activity on IOP has gained attention in recent years, considering exercise prescription as a promising adjuvant strategy for controlling IOP in glaucoma patients. To integrate all the available information in this regard, we have conducted a clinical review based on a patient-centered approach. Previous studies have demonstrated that the IOP response to physical exercise is dependent on numerous factors such as the exercise type (e.g., endurance or resistance training), exercise intensity, subjects’ fitness level, body position (e.g., supine vs. standing) and breathing pattern adopted during exercise, underlying medical conditions, concomitant mental effort, or caffeine intake before exercise. This article summarizes the available scientific evidence on the positive and negative effects of physical exercise on IOP and provides practical recommendations for exercise prescription in glaucoma patients or those at risk. An active collaboration between eye care and sports medicine specialists would permit a better management of this ocular condition.
Planning for a Warmer Future: Heat Risk Assessment and Mitigation in Lahti, Finland
Authors: Negi Ankur , Emmanuel Rohinton , Aarrevaara Eeva
Published: January 2025

With global climate change causing temperature increases, even cooler regions like Finland are facing increasing heat risks. The city of Lahti is expected to experience a higher-than-average temperature increase, making heat risk mitigation essential. This study aims to assess present and future heat risks in Lahti using exposure and social vulnerability indicators to identify heat risk hotspots and provide strategies for mitigation within the city’s urban planning framework. The method utilizes a combination of Land Surface Temperature (LST) data (2014–2024), climate projections, and microclimate analysis to identify heat risk in the city. Geographic Information Systems (GIS) and ENVI-met modeling were employed to assess the relationship between land surface temperatures (LST), urban structure, and green infrastructure. Risk assessments were conducted using social and environmental vulnerability indicators, and future projections were based on a combined SSP2-4.5 scenario. The results show a significant increase in high-risk areas by 2040, rising from 9.79% to 23.65% of Lahti’s core urban area. Although the current urban planning framework of the city (Masterplan 2035) is effective in terms of maintaining exposure levels, the continued increase in projected air temperatures, as modeled based on outputs of the EC-Earth3-veg GCM, remains a concern. Microclimate modeling confirmed that urban greenery significantly reduces heat stress and improves thermal comfort. To address future heat risks, Lahti must integrate more green infrastructure into its urban design and identify seasonal heat mitigation methodologies. Additionally, the findings emphasize the need for adaptive planning strategies to mitigate rising temperatures and ensure urban resilience.
EM Algorithm in the Slash 2S-Lindley Distribution with Applications
Authors: Muñoz Héctor A. , Castillo Jaime S. , Gallardo Diego I. , Venegas Osvaldo , Gómez Héctor W.
Published: January 2025

In this work, we present a new distribution, which is a slash extension of the distribution of the sum of two independent Lindley random variables. This new distribution is developed using the slash methodology, resulting in a distribution with more flexible kurtosis, i.e., the ability to model atypical data. We study the density function of the new model and some of its properties, such as the cumulative distribution function, moments, and its asymmetry and kurtosis coefficients. The parameters are estimated by the maximum likelihood method with the EM algorithm. Finally, we apply the proposed model to two real datasets with high kurtosis, showing that it provides a better fit than two distributions known in the literature.
Scoping Review of Machine Learning and Patient-Reported Outcomes in Spine Surgery
Authors: Quinones Christian , Kumbhare Deepak , Guthikonda Bharat , Hoang Stanley
Published: January 2025

Machine learning is an evolving branch of artificial intelligence that is being applied in neurosurgical research. In spine surgery, machine learning has been used for radiographic characterization of cranial and spinal pathology and in predicting postoperative outcomes such as complications, functional recovery, and pain relief. A relevant application is the investigation of patient-reported outcome measures (PROMs) after spine surgery. Although a multitude of PROMs have been described and validated, there is currently no consensus regarding which questionnaires should be utilized. Additionally, studies have reported varying degrees of accuracy in predicting patient outcomes based on questionnaire responses. PROMs currently lack standardization, which renders them difficult to compare across studies. The purpose of this manuscript is to identify applications of machine learning to predict PROMs after spine surgery.
Integrated Clinomics and Molecular Dynamics Simulation Approaches Reveal the SAA1.1 Allele as a Biomarker in Alkaptonuria Disease Severity
Authors: Trezza Alfonso , Roncaglia Bianca , Visibelli Anna , Barletta Roberta , Peruzzi Luana , Marzocchi Barbara , Braconi Daniela , Spiga Ottavia , Santucci Annalisa
Published: January 2025

Alkaptonuria (AKU) is a rare metabolic disorder characterized by the accumulation of homogentisic acid (HGA), leading to progressive ochronosis and joint degeneration. While much is known about HGA’s role in tissue damage, the molecular mechanisms underlying acute inflammation in AKU remain poorly understood. Serum amyloid A (SAA) proteins are key mediators of the inflammatory response, yet their potential as biomarkers for inflammation in AKU has not been explored. This study investigated the role of the SAA1.1 allele as a biomarker for the severity of acute inflammation in AKU. Data from the ApreciseKUre Precision Medicine Ecosystem were analyzed to assess the relationship between SAA1 allelic variants and inflammatory markers. Molecular dynamics simulations compared the structural dynamics of SAA1.1 and SAA1.2 isoforms, with standard modeling and analysis pipelines employed. Using a clinomics approach, we showed that AKU patients expressing the SAA1.1 allele have significantly higher acute inflammation-related markers. Extensive molecular dynamics simulations revealed that the SAA1.1 isoform lent high structural instability of the C-terminal domain, accelerating the formation of amyloid fibrils and exacerbating the inflammatory condition. These findings would identify the SAA1.1 allele as a novel genetic biomarker for the progression of secondary amyloidosis in AKU and its severity. Furthermore, new molecular insights into the inflammatory mechanisms of AKU were provided, suggesting potential therapeutic approaches aimed at stabilizing SAA1.1 protein and preventing amyloid fibril formation, with significant implications in AKU and precision medicine strategies for SAA-related diseases.
Aromatic Amino Acids: Exploring Microalgae as a Potential Biofactory
Authors: Niraula Archana , Danesh Amir , Merindol Natacha , Meddeb-Mouelhi Fatma , Desgagné-Penix Isabel
Published: January 2025

In recent times, microalgae have emerged as powerful hosts for biotechnological applications, ranging from the production of lipids and specialized metabolites (SMs) of pharmaceutical interest to biofuels, nutraceutical supplements, and more. SM synthesis through bioengineered pathways relies on the availability of aromatic amino acids (AAAs) as an essential precursor. AAAs, phenylalanine, tyrosine, and tryptophan are also the building blocks of proteins, maintaining the structural and functional integrity of cells. Hence, they are crucial intermediates linking the primary and specialized metabolism. The biosynthesis pathway of AAAs in microbes and plants has been studied for decades, but not much is known about microalgae. The allosteric control present in this pathway has been targeted for metabolic engineering in microbes. This review focuses on the biosynthesis of AAAs in eukaryotic microalgae and engineering techniques for enhanced production. All the putative genes involved in AAA pathways in the model microalgae Chlamydomonas reinhardtii and Phaeodactylum tricornutum are listed in this review.
Performance-Based Multi-Objective Optimization of Four-Limb CFST Lattice Columns
Authors: He Junjie , Huang Zhi , Chen Juan , Zhou Wangbao , Huang Tao , Kang Xin , Chen Yohchia Frank
Published: January 2025

In this paper, the low-cycle reciprocating load test was carried out on four-limb concrete-filled steel tubular (CFST) lattice columns with different slenderness ratios and axial compression ratios, and the seismic performance was studied. Two performance indicators, namely damage and hysteretic energy dissipation, were defined as the objective functions, and the axial compression ratio was used as an optimization variable to perform the multi-objective optimization analysis of four-limb CFST lattice columns. Optimization using the max–min problem approach aims to optimize the axial compression ratio to minimize damage and maximize the dissipation of hysteresis energy. The seismic performances before and after optimization were determined using a restoring force model and were evaluated by the finite element method under different axial compression ratios. The results show that, under low-cycle reciprocating loads, the load–displacement hysteresis curve is a bow shape (Members 1 and 2), inverse S-shape (Member 3), and approximate shuttle shape (Member 4). Through multi-objective optimization, the optimized axial compression ratio is 0.25 and the finite element analysis indicates that the optimal seismic performance is at an axial compression ratio of 0.25. Through the optimized design, the maximum horizontal load of lattice columns, the elastic stiffness, the dissipation capacity, and the seismic performance are all improved, under the premise of satisfying the structural safety.
A Single Center Study of Genes Involved in Synchronous and Metachronous Multiple Early-Stage Gastric Cancers in Japanese Patients with Current or Former Helicobacter pylori Infection
Authors: Hashimoto Minami , Hikichi Takuto , Honma Reiko , Imai Jun-ichi , Takasumi Mika , Nakamura Jun , Kato Tsunetaka , Yanagita Takumi , Otsuka Mitsuru , Nemoto Daiki , Kobayakawa Masao , Watanabe Shinya , Ohira Hiromasa
Published: January 2025

Background: This study aimed to perform a comprehensive gene expression analysis in patients with early-stage gastric cancer (EGC) to identify gene expression profiles specific to gastric cancer (GC) lesions. Methods: Biopsy specimens were collected from one EGC lesion and three background mucosal areas of patients scheduled for endoscopic submucosal dissection (ESD). Lesion-specific gene profiles in these four biopsies were analyzed using DNA microarrays. Patients with concurrent EGCs at the time of an ESD or a history of GC were classified into the multiple GC group (n = 26), while those without such histories were assigned to the single GC group (n = 74). Results: After excluding patients with heterogeneous factors, 55 patients were analyzed. Twenty-one differential genes exhibiting distinct mean expression profiles stratified by background gastric mucosa were extracted between the single and multiple GC groups. A scoring system constructed using these genes to calculate the weighted expression values for each patient, with an optimal cutoff value of −2.574, yielded a sensitivity and specificity of 85.7%. Conclusions: This study identified the different gene expression profiles between synchronous and metachronous multiple GCs and single GCs in patients with EGC. The developed scoring system has potential to distinguish between single and multiple GCs.
Effects on Quality of Life and Self-Efficacy of Instant Messaging Services in Self-Management Programs for Prostate Cancer: A Systematic Review and Meta-Analysis
Authors: Raya Benítez Julia , Valenza Peña Geraldine , Martín Núñez Javier , Navas Otero Alba , Granados Santiago María , Heredia Ciuró Alejandro , Valenza Marie Carmen
Published: January 2025

Background: Prostate cancer is one of the most common cancers among men worldwide. Management options include active surveillance, surgery, radiation, and chemotherapy, while self-management and behavioral interventions have shown promise in improving health-promoting behaviors and addressing barriers to care. Mobile health interventions, particularly instant messaging platforms, offer a growing opportunity for effective therapeutic support, but evidence on their role in self-management remains limited. The objective was to investigate the efficacy of self-management interventions based on instant messaging on quality of life and self-efficacy in patients diagnosed with prostate cancer through a systematic review and meta-analysis. Methods: A search was conducted of three databases from their inception to November 2024. Randomized controlled trials were included. Two reviewers performed independent data extraction and methodologic quality assessment of the studies. Results: A total of seven studies were included in the review. Instant messaging interventions were recognized by a previously published taxonomy of collaborative technologies. The meta-analysis showed that self-management interventions based on instant messaging have an effect on quality of life and self-efficacy. Conclusions: This systematic review highlights the potential benefits of self-management interventions incorporating instant messaging for improving quality of life and self-efficacy in prostate cancer patients.
Subcellular Compartmentalization of Glucose Mediated Insulin Secretion
Authors: Wang Zhongying , Gurlo Tatyana , Satin Leslie S. , Fraser Scott E. , Butler Peter C.
Published: January 2025

Regulation of blood glucose levels depends on the property of beta cells to couple glucose sensing with insulin secretion. This is accomplished by the concentration-dependent flux of glucose through glycolysis and oxidative phosphorylation, generating ATP. The resulting rise in cytosolic ATP/ADP inhibits KATP channels, inducing membrane depolarization and Ca2+ influx, which prompts insulin secretion. Evidence suggests that this coupling of glucose sensing with insulin secretion may be compartmentalized in the submembrane regions of the beta cell. We investigated the subcellular responses of key components involved in this coupling and found mitochondria in the submembrane zone, some tethered to the cytoskeleton near capillaries. Using Fluorescent Lifetime Imaging Microscopy (FLIM), we observed that submembrane mitochondria were the fastest to respond to glucose. In the most glucose-responsive beta cells, glucose triggers rapid, localized submembrane increases in ATP and Ca2+ as synchronized ~4-min oscillations, consistent with pulsatile insulin release after meals. These findings are consistent with the hypothesis that glucose sensing is coupled with insulin secretion in the submembrane zone of beta cells. This zonal adaptation would enhance both the speed and energy efficiency of beta cell responses to glucose, as only a subset of the most accessible mitochondria would be required to trigger insulin secretion.
The Potential Mechanism of Cuproptosis in Hemocytes of the Pacific Oyster Crassostrea gigas upon Elesclomol Treatment
Authors: Zhang Yuxin , Sun Jiejie , Li Shurong , Wang Lingling , Song Linsheng
Published: January 2025

Cuproptosis is a novel cell death dependent on mitochondrial respiration and regulated by copper. While the study of it is mainly focused on tumor therapy, in the present study, two key cuproptosis-related genes, ferredoxin (FDX1) and dihydrolipoamide S-acetyltransferase (DLAT) homologs (designated as CgFDX1 and CgDLAT), were identified from Crassostrea gigas. CgFDX1 has a Fer2 domain with a 2Fe-2S cluster forming a unique ferredoxin. CgDLAT is composed of a biotin_lipoyl domain, an E3-binding domain, and a 2-oxoacid_dh domain. CgFDX1 and CgDLAT mRNA were expressed in all the examined tissues. After elesclomol treatment, both mRNA and protein expressions of them were reduced in the hemocytes. The mortality rate of the hemocytes increased significantly, and the hemocytes were accompanied with noticeable adhesive abnormalities and heightened secretion after elesclomol treatment. Additionally, the accumulation or depletion of actin was observed in the hemocytes. The integrity of the double membrane structure of the mitochondria was compromised, and the organization of mitochondrial cristae was disrupted. The contents of copper, malondialdehyde (MDA), pyruvic acid and mitoSOX as well as the ratio of cells with low mitochondrial potential increased significantly in the hemocytes upon elesclomol treatment and the content of citric acid decreased significantly. These findings suggest the potential presence of cuproptosis in oysters and its activation mechanism is relatively conserved in evolution.
Children, Adolescents and Urine Hydration Indices—A Systematic Literature Review on Athletes and Non-Athletes
Authors: Papaoikonomou Georgios , Apergi Kyriaki , Malisova Olga
Published: January 2025

Background/Objectives: The importance of adequate hydration in children and adolescents has raised significant attention, both for its health benefits and for its role in supporting cognitive and physical performance. This is particularly important for young athletes who have increased dehydration risk due to high sweat loss and often inadequate water intake. The aim of this review is to systematically assess the hydration status of children and adolescents, including athletes, and to identify notable differences in hydration levels between these groups. Methods: A systematic literature search was conducted following PRISMA guidelines. PubMed, Scopus, and Scholar were searched for articles published between 2004 and 2024 on hydration in children and adolescents, focusing on urinary biomarkers such as urine osmolality, urine specific gravity, and urine color. Two independent reviewers screened the studies, and clinical studies or those involving chronic diseases, disabilities, or missing data were excluded. Results: Fifty-one articles met the inclusion criteria and were categorized into competitive athletes (n = 26) and non-athletes (n = 25). The review revealed that both athletes and non-athletes were frequently dehydrated. Among athletes, 81% of studies reported dehydration, while 69% of studies observed dehydration in non-athlete children. Biomarkers consistently indicated failure to meet recommended hydration guidelines in both groups. Conclusions: Despite existing hydration guidelines, dehydration is prevalent among children and adolescents, irrespective of athletic involvement. This underscores the urgent need for targeted interventions to improve hydration education and practices in schools, homes, and extracurricular settings.
Insights on the Anti-Inflammatory and Anti-Melanogenic Effects of 2′-Hydroxy-2,6′-dimethoxychalcone in RAW 264.7 and B16F10 Cells
Authors: Bae Sung-Min , Hyun Chang-Gu
Published: January 2025

Chalcones are recognized for their diverse pharmacological properties, including anti-inflammatory and anti-melanogenic effects. However, studies on 2′-hydroxy-2-methoxychalcone derivatives remain limited. This study investigated the anti-inflammatory and melanin synthesis-inhibitory effects of three derivatives: 2′-hydroxy-2,4-dimethoxychalcone (2,4-DMC), 2′-hydroxy-2,5′-dimethoxychalcone (2,5′-DMC), and 2′-hydroxy-2,6′-dimethoxychalcone (2,6′-DMC). In lipopolysaccharide (LPS)-stimulated RAW 264.7 macrophages, 2,6′-DMC demonstrated a superior inhibition of nitric oxide (NO) production, pro-inflammatory cytokines, and the expression of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) compared to the other derivatives. A mechanistic analysis revealed that 2,6′-DMC modulates the NF-κB and MAPK signaling pathways to attenuate inflammation. Additionally, 2,6′-DMC exhibited a significant inhibition of α-melanocyte-stimulating hormone (α-MSH)-induced melanin synthesis in B16F10 melanoma cells by downregulating tyrosinase, TRP-1, TRP-2, and MITF expression. This regulation was achieved through the suppression of the Wnt/β-catenin, PI3K/AKT, MAPK, and PKA/CREB pathways. Compared to 2,4-DMC and 2,5′-DMC, 2,6′-DMC’s structural configuration, characterized by methoxy groups at the 2- and 6′-positions, contributed to its enhanced molecular stability and binding affinity, amplifying its inhibitory effects. A primary skin irritation test confirmed that 2,6′-DMC exhibited minimal irritation, demonstrating its safety for dermal applications. These findings suggest that 2,6′-DMC holds promise as a dual-function agent for managing inflammatory conditions and hyperpigmentation-related disorders.
CyberKnife in Pediatric Oncology: A Narrative Review of Treatment Approaches and Outcomes
Authors: Donati Costanza M. , Medici Federica , Zamfir Arina A. , Galietta Erika , Cammelli Silvia , Buwenge Milly , Masetti Riccardo , Prete Arcangelo , Strigari Lidia , Forlani Ludovica , D’Angelo Elisa , Morganti Alessio G.
Published: January 2025

Pediatric cancers, while rare, pose unique challenges due to the heightened sensitivity of developing tissues and the increased risk of long-term radiation-induced effects. Radiotherapy (RT) is a cornerstone in pediatric oncology, but its application is limited by concerns about toxicity, particularly secondary malignancies, growth abnormalities, and cognitive deficits. CyberKnife (CK), an advanced robotic radiosurgery system, has emerged as a promising alternative due to its precision, non-invasiveness, and ability to deliver hypofractionated, high-dose RT while sparing healthy tissues. This narrative review explores the existing evidence on CK application in pediatric patients, synthesizing data from case reports, small series, and larger cohort studies. All the studies analyzed reported cases of tumors located in the skull or in the head and neck region. Findings suggest CK’s potential for effective tumor control with favorable toxicity profiles, especially for complex or inoperable tumors. However, the evidence remains limited, with the majority of studies involving small sample sizes and short follow-up periods. Moreover, concerns about the “dose-bath” effect and limited long-term data on stochastic risks warrant cautious adoption. Compared to Linac-based RT and proton therapy, CK offers unique advantages in reducing session numbers and enhancing patient comfort, while its real-time tracking provides superior accuracy. Despite these advantages, CK is associated with significant limitations, including a higher potential for low-dose scatter (often referred to as the “dose-bath” effect), extended treatment times in some protocols, and high costs requiring specialized expertise for operation. Emerging modalities like π radiotherapy further underscore the need for comparative studies to identify the optimal technique for specific pediatric cases. Notably, proton therapy remains the benchmark for minimizing long-term toxicity, but its cost and availability limit its accessibility. This review emphasizes the need for balanced evaluations of CK and highlights the importance of planning prospective studies and long-term follow-ups to refine its role in pediatric oncology. A recent German initiative to establish a CK registry for pediatric CNS lesions holds significant promise for advancing evidence-based applications and optimizing treatment strategies in this vulnerable population.
Explainable AI for Bipolar Disorder Diagnosis Using Hjorth Parameters
Authors: Saghab Torbati Mehrnaz , Zandbagleh Ahmad , Daliri Mohammad Reza , Ahmadi Amirmasoud , Rostami Reza , Kazemi Reza
Published: January 2025

Background: Despite the prevalence and severity of bipolar disorder (BD), current diagnostic approaches remain largely subjective. This study presents an automatic diagnostic framework using electroencephalography (EEG)-derived Hjorth parameters (activity, mobility, and complexity), aiming to establish objective neurophysiological markers for BD detection and provide insights into its underlying neural mechanisms. Methods: Using resting-state eyes-closed EEG data collected from 20 BD patients and 20 healthy controls (HCs), we developed a novel diagnostic approach based on Hjorth parameters extracted across multiple frequency bands. We employed a rigorous leave-one-subject-out cross-validation strategy to ensure robust, subject-independent assessment, combined with explainable artificial intelligence (XAI) to identify the most discriminative neural features. Results: Our approach achieved remarkable classification accuracy (92.05%), with the activity Hjorth parameters from beta and gamma frequency bands emerging as the most discriminative features. XAI analysis revealed that anterior brain regions in these higher frequency bands contributed most significantly to BD detection, providing new insights into the neurophysiological markers of BD. Conclusions: This study demonstrates the exceptional diagnostic utility of Hjorth parameters, particularly in higher frequency ranges and anterior brain regions, for BD detection. Our findings not only establish a promising framework for automated BD diagnosis but also offer valuable insights into the neurophysiological basis of bipolar and related disorders. The robust performance and interpretability of our approach suggest its potential as a clinical tool for objective BD diagnosis.
LCSC-UAVNet: A High-Precision and Lightweight Model for Small-Object Identification and Detection in Maritime UAV Perspective
Authors: Wang Yanjuan , Liu Jiayue , Zhao Jun , Li Zhibin , Yan Yuxian , Yan Xiaohong , Xu Fengqiang , Li Fengqi
Published: January 2025

Unmanned Aerial Vehicle (UAV) object detection is crucial in various fields, such as maritime rescue and disaster investigation. However, due to small objects and the limitations of UAVs’ hardware and computing power, detection accuracy and computational overhead are the bottleneck issues of UAV object detection. To address these issues, a novel convolutional neural network (CNN) model, LCSC-UAVNet, is proposed, which substantially enhances the detection accuracy and saves computing resources. To address the issues of low parameter utilization and insufficient detail capture, we designed the Lightweight Shared Difference Convolution Detection Head (LSDCH). It combines shared convolution layers with various differential convolution to enhance the detail capture ability for small objects. Secondly, a lightweight CScConv module was designed and integrated to enhance detection speed while reducing the number of parameters and computational cost. Additionally, a lightweight Contextual Global Module (CGM) was designed to extract global contextual information from the sea surface and features of small objects in maritime environments, thus reducing the false negative rate for small objects. Lastly, we employed the WIoUv2 loss function to address the sample imbalance issue of the datasets, enhancing the detection capability. To evaluate the performance of the proposed algorithm, experiments were performed across three commonly used datasets: SeaDroneSee, AFO, and MOBdrone. Compared with the state-of-the-art algorithms, the proposed model showcases improvements in mAP, recall, efficiency, where the mAP increased by over 10%. Furthermore, it utilizes only 5.6 M parameters and 16.3 G floating-point operations, outperforming state-of-the-art models such as YOLOv10 and RT-DETR.
Analyzing Barriers to Mentee Activity in a School-Based Talent Mentoring Program: A Mixed-Method Study
Authors: Daunicht Tina-Myrica , Emmerdinger Kathrin Johanna , Stoeger Heidrun , Ziegler Albert
Published: January 2025

Studies on talent development show that attaining expertise relies on long-term active engagement with a domain. Mentoring plays a key role in this, but it usually takes place outside of school in informal mentoring relationships, and research on formal school-based talent development mentoring programs is lacking. In the present research, we examined which factors in a novel school-based Learning Pathway Mentoring program hinder mentees’ active engagement with their domain. Due to a lack of corresponding research, we employed an exploratory sequential mixed-methods design (QUAL → quan). We first explored factors affecting mentees’ engagement from the perspective of 55 mentors in the Learning Pathway Mentoring program. The results of these qualitative analyses served to derive research questions about variables associated with decreased mentee domain activity, which we then studied in auxiliary quantitative analyses based on a sample of 48 mentees of the same program. Our combined analyses suggest that reasons for decreased mentee engagement may, in fact, be very heterogeneous and nuanced. As talent development places a significant demand on mentees in terms of extracurricular engagement, difficulties might occur specifically when mentees are expected to set priorities regarding the implementation of learning activities in their talent domain and simultaneously meet increasing school demands.
Resource and Trajectory Optimization in RIS-Assisted Cognitive UAV Networks with Multiple Users Under Malicious Eavesdropping
Authors: Li Juan , Wang Gang , Jin Hengzhou , Zhou Jing , Li Wei , Hu Hang
Published: January 2025

Unmanned aerial vehicles (UAVs) have shown significant advantages in disaster relief, emergency communication, and Integrated Sensing and Communication (ISAC). However, the escalating demand for UAV spectrum is severely restricted by the scarcity of available spectrum, which in turn significantly limits communication performance. Additionally, the openness of the wireless channel poses a serious threat, such as wiretapping and jamming. Therefore, it is necessary to improve the security performance of the system. Recently, Reconfigurable Intelligent Surfaces (RIS), as a highly promising technology, has been integrated into Cognitive UAV Network. This integration enhances the legitimate signal while suppressing the eavesdropping signal. This paper investigates a RIS-assisted Cognitive UAV Network with multiple corresponding receiving users as cognitive users (CUs) in the presence of malicious eavesdroppers (Eav), in which the Cognitive UAV functions as the mobile aerial Base Station (BS) to transmit confidential messages for the users on the ground. Our primary aim is to attain the maximum secrecy bits by means of jointly optimizing the transmit power, access scheme of the CUs, the RIS phase shift matrix, and the trajectory. In light of the fact that the access scheme is an integer, the original problem proves to be a mixed integer non-convex one, which falls into the NP-hard category. To solve this problem, we propose block coordinate descent and successive convex approximation (BCD-SCA) algorithms. Firstly, we introduce the BCD algorithm to decouple the coupled variables and convert the original problem into four sub-problems for the non-convex subproblems to solve by the SCA algorithm. The results of our simulations indicate that the joint optimization scheme we have put forward not only achieves robust convergence but also outperforms conventional benchmark approaches.
Evaluation of Deep Learning Techniques in PV Farm Cyber Attacks Detection
Authors: Hassan Ghufran F. , Ahmed Oday A. , Sallal Muntadher
Published: January 2025

Integrating intelligent grids with the internet increases the amount of unauthorized input data which directly or indirectly influences electrical system control and decision-making. Photovoltaic (PV) farms that are linked to the power grid are susceptible to cyber attacks which may disrupt energy infrastructure and compromise the security, stability, and resilience of the electrical system. This research proposes a new model for cyber threat detection in PV farm, named as Cyber Detection in PV farm (CDPV), which makes use of deep learning methods based solely on point-of-common coupling (PCC) detectors. In this paper, a thorough cyber attack model for a photovoltaic (PV) farm is developed, where the simulation of four kinds of cyber attacks is provided. Furthermore, this paper evaluates the role of three deep learning techniques including convolutional neural network (CNN), artificial neural network (ANN), and long short-term memory (LSTM), in PV cyber threat detection. The findings demonstrate that, at the DC/DC converter and DC/AC inverter sides, the proposed CDPV model based on deep learning techniques (CNN, ANN, and LSTM) can improve the cyber detection accuracy and resilience under various attack scenarios.
Advanced Hybrid Models for Air Pollution Forecasting: Combining SARIMA and BiLSTM Architectures
Authors: Necula Sabina-Cristiana , Hauer Ileana , Fotache Doina , Hurbean Luminița
Published: January 2025

This study explores a hybrid forecasting framework for air pollutant concentrations (PM10, PM2.5, and NO2) that integrates Seasonal Autoregressive Integrated Moving Average (SARIMA) models with Bidirectional Long Short-Term Memory (BiLSTM) networks. By leveraging SARIMA’s strength in linear and seasonal trend modeling and addressing nonlinear dependencies using BiLSTM, the framework incorporates Box-Cox transformations and Fourier terms to enhance variance stabilization and seasonal representation. Additionally, attention mechanisms are employed to prioritize temporal features, refining forecast accuracy. Using five years of daily pollutant data from Romania’s National Air Quality Monitoring Network, the models were rigorously evaluated across short-term (1-day), medium-term (7-day), and long-term (30-day) horizons. Metrics such as RMSE, MAE, and MAPE revealed the hybrid models’ superior performance in capturing complex pollutant dynamics, particularly for PM2.5 and PM10. The SARIMA combined with BiLSTM, Fourier, and Attention configuration demonstrated consistent improvements in predictive accuracy and interpretability, with attention mechanisms proving effective for extreme values and long-term dependencies. This study highlights the benefits of combining statistical preprocessing with advanced neural architectures, offering a robust and scalable solution for air quality forecasting. The findings provide valuable insights for environmental policymakers and urban planners, emphasizing the potential of hybrid models for improving air quality management and decision-making in dynamic urban environments.
Pairing-Free Searchable Encryption for Enhancing Security Against Frequency Analysis Attacks
Authors: Wu Wanqing , Shi Hangyu
Published: January 2025

With the rapid development of cloud technology, a growing volume of encrypted data is being stored on cloud servers. Public key searchable encryption (PEKS) has emerged as a solution to the challenge of retrieving encrypted data. However, most PEKS schemes are vulnerable to frequency analysis attacks (FAA), which can potentially expose sensitive information. To address this issue, we propose a pairing-free public key searchable encryption scheme that is specifically designed to resist such attacks. Our scheme is built upon the decisional Diffie–Hellman (DDH) assumption, and effectively mitigates the risk of malicious adversaries extracting keyword information through trapdoor search frequencies. The pairing-free nature of our approach not only enhances the security against FAA but also improves the retrieval efficiency compared to traditional PEKS schemes. We formally prove that our scheme satisfies both chosen keyword attack (CKA) security and keyword guessing attack (KGA) security. Additionally, we perform comprehensive theoretical and experimental evaluations to assess the scheme’s efficiency, showcasing its practical applicability in real-world scenarios.
Numerical Simulation of the Transition to Detonation in a Hydrogen–Air Mixture Due to Shock Wave Focusing on a 90-Degree Wedge
Authors: De La Hoz Jose Bermudez , Rudy Wojciech , Teodorczyk Andrzej
Published: January 2025

This study numerically explores the initiation of detonation through shock wave reflection and focusing on a 90-degree wedge in varying mixtures of hydrogen–air. The simulations were conducted using the ddtFoam code, an integral part of the OpenFOAM open-source Computational Fluid Dynamics (CFD) package of density-based code for solving the unsteady, compressible Navier–Stokes equations. The simulation results unveil three potential outcomes in the corner post-reflection: deflagrative ignition in the corner, deflagrative ignition with intermediate transient phases leading to a delayed transition to detonation in the trailing combustion zone close to the apex of the wedge, and ignition with an immediate transition to detonation, resulting in the formation of a detonation wave in the corner tip. In the experimental investigation, the transition velocity for the stoichiometric mixture stood at approximately 719 m/s. In contrast, the numerical simulation indicated a transition velocity of 664 m/s for the same stoichiometric mixture, reflecting a 5.5% decrease in velocity. Such an underestimation level of 5–8% by the simulation results was observed for mixtures of 25–45% H2 in air.
Dual-Stage Energy Recovery from Internal Combustion Engines
Authors: Di Battista Davide , Di Prospero Federico , Di Giovine Giammarco , Fatigati Fabio , Cipollone Roberto
Published: January 2025

Waste heat recovery is one of the most investigated solutions for increasing the efficiency of powertrains in the transportation sector. A major portion of thermal energy is wasted via exhaust gases. Almost one third of fuel energy is lost, and its recovery as propulsion energy is a promising goal. Moreover, this enables the increased electrification or hybridization of powertrains, assuming the energy recovered is converted into electrical form and used to fulfill different vehicles’ needs. The present study focuses on a dual-stage energy recovery system designed to enhance the efficiency of internal combustion engines (ICEs) in heavy-duty vehicles (HDVs). The system combines a turbocompound unit for direct heat recovery (DHR) and an organic Rankine cycle (ORC) for indirect heat recovery (IHR). These technologies aim to exploit waste heat from exhaust gases, converting it into electrical energy. In this regard, electrical energy can be stored in a battery for it to be available for the energy needs of powertrains that use hybrid propulsion and for driving pumps and compressors on board, following recent technologies of auxiliaries on demand. The proposed setup was modeled and analyzed under off-design conditions to evaluate energy recovery potential and engine performance impacts. From this point of view, in fact, any device that operates on exhaust gas introduces a pressure loss, increasing engine backpressure, whose effect is an increase in specific fuel consumption. An estimate of this negative effect is presented in this paper based on experimental data measured in a F1C IVECO™ engine. An average net recovery of 5–6% of engine power has been demonstrated, with an important prevalence of the turbocompound with respect to the ORC section. The results demonstrate the viability of integrating DHR and IHR stages, with implications for advancing sustainable transportation technologies.
Modeling and Evaluation of Forecasting Models for Energy Production in Wind and Photovoltaic Systems
Authors: Benrabia Imene , Söffker Dirk
Published: January 2025

The comprehensive change from known, classical energy production methods to the increased use of renewable energy requires new methods in the field of efficient application and use of renewable energy. The urban energy supply presents complex challenges in improving efficiency; therefore, the prediction of the dynamical availability of energy is required. Several approaches have been explored, including statistical models and machine learning using historical data and numerical weather prediction models using mathematical models of the atmosphere and weather conditions. Accurately forecasting renewable energy production involves analyzing factors such as related weather conditions, conversion systems, and their locations, which influence both energy availability and yield. This study focuses on the short-term forecasting of wind and photovoltaic (PV) energy using historical data and machine learning approaches, aiming for accurate 8 h predictions. The goal is to develop models capable of producing accurate short-term forecasts of energy production from both resources (solar and wind), suitable for later use in a model predictive control scheme where generation and demand, as well as storage, must be considered together. Methods include regression trees, support vector regression, and regression neural networks. The main idea in this work is to use past and future information in the model. Inputs for the PV model are past PV generation and future solar irradiance, while the wind model uses past wind generation and future wind speed data. The performance of the model is evaluated over the entire year. Two scenarios are tested: one with perfect future predictions of wind speed and solar irradiance, and another considered realistic situation where perfect future prediction is not possible, and uncertain prediction is accounted for by incorporating noise models. The results of the second scenario were further improved using the output filtering method. This study shows the advantages and disadvantages of different methods, as well as the accuracy that can be expected in principle. The results show that the regression neural network has the best performance in predicting PV and wind generation compared to other methods, with an RMSE of 0.1809 for PV and 5.3154 for wind, and a Pearson coefficient of 0.9455 for PV and 0.9632 for wind.
Review on Lithium-Ion Battery Heat Dissipation Based on Microchannel–PCM Coupling Technology
Authors: Chen Jun , Xu Wanli , Tian Hao , Cao Yichao , Gu Jincheng , Zhou Haijun , Jin Yong
Published: January 2025

Lithium-ion battery heat dissipation difficulties seriously affect the efficient and stable operation of electronic devices and electric vehicles. Faced with the increasing heat dissipation demand, traditional liquid cooling systems cannot ensure the quality of heat dissipation and are seriously limited in the ability to provide uniform temperature adjustments. Combining microchannels with PCMs is beneficial. For the hybrid system here described, the maximum and average values of the temperature field were 10.35 K and 1 K, respectively, surpassing those of a liquid cooling system. By introducing suitable PCMs, the maximum value could be reduced by 5.6 K (under a 2C discharge rate) and by 16.2 K (under a 3C discharge rate). This article briefly introduces the development status and main problems of the technology combining microchannels and PCMs in BTMSs, then reviews the research progress for lithium-ion battery heat dissipation achieved by using the technology coupling microchannels and PCMs, analyzes its performance advantages, and finally prospects the future development direction of the microchannel–PCM coupling technology.