bluuebunny/crossref_metadata_2025 huggingface dataset:
Pharmacokinetic Analysis of an Isoniazid Suspension Among Spanish Children Under 6 Years of Age
Authors: Noguera-Julian Antoni , Wilhelmi Emma , Cussó Maria , Aarnoutse Rob , Colbers Angela , Martorell Loreto , López-Ramos Maria Goretti , Vinent Joan , Farré Rosa , Soy Dolors , Simó-Nebot Sílvia , Fortuny Clàudia
Published: January 2025

Background: Isoniazid (INH) remains a first-line drug for the treatment of tuberculosis (TB) in young children. In 2010, the WHO recommended an increase in the daily dose of INH up to 10 (7–15) mg/kg. Currently, there are no INH suspensions available in Europe. Methods: We aimed to characterize the pharmacokinetics of a licensed INH suspension (10 mg/mL, Pharmascience Inc., Montreal, QC, Canada) in children receiving INH daily at 10 mg/kg in a single-center, open-label, non-randomized, phase IIa clinical trial (EudraCT Number: 2016-002000-31) in Barcelona (Spain). Samples were analyzed using a validated UPLC-UV assay. The N-acetyltransferase 2 gene was examined to determine the acetylation status. A non-compartmental pharmacokinetic analysis was conducted. Results: Twenty-four patients (12 females) were included (primary chemoprophylaxis, n = 12; TB treatment, n = 9; and TB infection preventive treatment, n = 3). The acetylator statuses were homozygous fast (n = 3), heterozygous intermediate (n = 18), and homozygous slow (n = 2; unavailable in one patient). The INH median (IQR) Cmax and AUC0–24h values were 6.1 (4.5–8.2) mg/L and 23.0 (11.2–35.4) h∙mg/L; adult targets (>3 mg/L and 11.6–26.3 h∙mg/L) were not achieved in three and six cases, respectively. Gender, age at assessment (<2 or >2 years), and INH monotherapy (vs. combined TB treatment) had no impact on pharmacokinetic parameters. Significant differences in Cmax (p = 0.030) and AUC0–24h (p = 0.011) values were observed based on acetylator status. Treatment was well tolerated, and no severe adverse events were observed; three patients developed asymptomatic mildly elevated alanine aminotransferase levels. Conclusions: In infants and children receiving a daily INH suspension at 10 mg/kg, no safety concerns were raised, and the target adult levels were reached in the majority of patients.
A Longer Duration of Intravenous Antibiotic Treatment for Patients with Early Periprosthetic Joint Infections Is Not Associated with a Lower Failure Rate
Authors: Meijer Janneke , Soriano Alex , Zijlstra Wierd , ten Have Bas , Tarabichi Saad , Jutte Paul , Parvizi Javad , Wouthuyzen-Bakker Marjan
Published: January 2025

Background: In recent years, many studies have demonstrated the efficacy of an early switch to oral antibiotics after surgical treatment in orthopedic-related infections. However, large analyses on periprosthetic joint infections (PJIs) are lacking. Material and Methods: We conducted a retrospective observational multicenter study in patients diagnosed with an early post-operative PJI, defined as one occurring <3 months after the index arthroplasty and treated with debridement, antibiotics, and implant retention (DAIR). Patients from Europe and the USA were included. We took advantage of the fact that an early oral antibiotic switch is routine practice in Europe as opposed to a long duration of intravenous (IV) antibiotic treatment in the USA. Failure was defined as the clinical need for (i) a second unintended DAIR procedure, (ii) implant removal, (iii) suppressive antibiotic treatment, or (iii) PJI-related death, all within one year after DAIR. Results: A total of 668 patients were included. A total of 277 received IV antibiotics for <14 days, 232 between 14 and 27 days, and 159 for >27 days. The overall 1-year failure rate within the 3 groups was 41.5%, 44.4%, and 42.1%, respectively (p = 0.80). This observation remained when excluding patients who failed during IV therapy. A longer duration of IV therapy seemed beneficial for those patients with a high pre-operative C-reactive protein level and lack of modular component exchange. Conclusions: In early post-operative PJIs, a longer duration of IV therapy is not associated with a lower failure rate but may be continued until a sufficient bacterial load reduction has been achieved.
A Classification Model Based on Interval Rule Inference Network with Interpretability
Authors: Zhang Yunxia , Zhong Yiming , Wu Xiaochang , Bai Jing
Published: January 2025

Interpretability requirements, complex uncertain data processing, and limited training data are characteristics of classification in some real industry applications. The interval belief rule base (IBRB) can deal with various types of uncertainty and provides high interpretability. However, there is a large number of parameters in IBRB, which makes it difficult for experts to accurately set them manually, limiting its application scope. To address this issue, this paper proposes an interval rule inference network (IRIN) with interpretability for classification models to automatically generate IBRB through integrating the ideas of the IBRB and the neural network. Firstly, hybrid data with different types are transformed into an interval belief distribution for automatic generation processing. Secondly, the interval evidence reasoning method is utilized as the inference engine to transfer information ensuring the process’s interpretability. Finally, a reasonable IBRB is generated automatically by updating the parameters by employing the learning engine in the neural network. Moreover, the differentiability of the interval evidence reasoning method in the IRIN is proved as a theoretical foundation of the IRIN, and an interpretability analysis of the IRIN’s structures is discussed. Experimental results demonstrate that the proposed method possesses high interpretability, enhancing the reliability of classification and maintaining the accuracy. Its application in an actual engineering case illustrates that it is particularly suitable for engineering problems where the explanation of results is a critical requirement.
The Role of Technical Car Features in Managing and Promoting New Peer-to-Peer Car-Sharing Systems: Insights from Potential Users and Strategic Implications for Service Providers
Authors: Turoń Katarzyna , Kubik Andrzej , Folęga Piotr , Wilk Andrzej , Bindzar Peter , Bui Truong M. N.
Published: January 2025

Peer-to-peer car-sharing systems are an evolving branch of urban mobility, aligning with global trends focused on sustainable development and reducing congestion in cities. A research gap has been identified concerning the specific vehicle attributes that would encourage the public to potentially use these services. Addressing this gap, and in the context of launching a new peer-to-peer car-sharing service in Katowice, Poland, this article investigates the technical features influencing the choice of vehicles in peer-to-peer car-sharing systems, particularly from the perspective of individuals who currently do not use such platforms. The study employs Social Network Analysis (SNA) to examine the interrelationships between vehicle attributes. The analysis reveals that key factors influencing users’ decisions include fuel/energy consumption, safety features, and technological advancement, with a particular emphasis on driver assistance systems, including autonomous driving capabilities. The network structure, characterized by a relatively low density (0.2536) and a short average path length (1.872), suggests that a few central vehicle features dominate user decisions, and improvements in these key areas can quickly propagate through the decision-making process, enhancing overall user satisfaction. To validate the findings, a Gradient Boosting Regression (GBR) analysis was conducted, confirming the significance of the key factors identified by the SNA, such as fuel efficiency, battery capacity, and safety systems, thus strengthening the reliability of the results. This study underscores the growing importance of sustainability and technological innovation in the automotive industry, particularly in the context of the sharing economy. It suggests that car-sharing platforms and vehicle manufacturers should prioritize these features to meet user expectations and preferences. These findings provide valuable insights for the strategic and operational management of peer-to-peer car-sharing services, emphasizing the importance of targeted vehicle selection and user-centered innovations to improve platform performance and scalability.
Automated Tree Detection Using Image Processing and Multisource Data
Authors: Dziczkowski Grzegorz , Probierz Barbara , Juszczuk Przemysław , Stefański Piotr , Jach Tomasz , Głowania Szymon , Kozak Jan
Published: January 2025

This paper presents a method for the automatic detection and assessment of trees and tree-covered areas in Katowice, the capital of the Upper Silesian Industrial Region in southern Poland. The proposed approach utilizes satellite imagery and height maps, employing image-processing techniques and integrating data from various sources. We developed a data pipeline for gathering and pre-processing information, including vegetation data and numerical land-cover models, which were used to derive a new method for tree detection. Our findings confirm that automatic tree detection can significantly enhance the efficiency of urban tree management processes, contributing to the creation of greener and more resident-friendly cities.
Study on the Adhesion Properties of Raw Coal and Adhesive Clogging Characteristics of Underground Coal Bunkers
Authors: Jiang Chongyang , Wang Lianguo , Pan Zhiyuan , Guo Jiaxing , Wang Shuai
Published: January 2025

The automation and continuous operation of coal production are fundamental to the construction of high-yielding and efficient mines. Underground coal bunkers, serving as the pivotal link between various production and transportation segments, are vital for the seamless operation of mines. Nonetheless, the adhesive properties of raw coal can lead to increasingly severe issues, such as the adhesive clogging of coal bunkers. To address this issue, this paper first employs a self-designed raw coal shear testing apparatus to conduct experiments under varying conditions of shear interfaces, moisture content in raw coal, and compaction forces. Obtaining the adhesion behavior characteristics and adhesion parameter variation patterns of raw coal at coal-coal and coal-wall interfaces under various influencing factors. Subsequently, leveraging the adhesion property parameters of raw coal and the engineering conditions of the 1011 roadway coal bunker in Taoyuan Coal Mine II, a numerical model for coal bunker discharge using irregular particles was developed with the PFC2D numerical simulation software. Based on these, we obtained the influence patterns of various factors, such as coal bunker convergence angle, coal storage height, and coal moisture content, on the coal particle flow pattern, bunker wall pressure, and adhesive clogging distribution characteristics of the coal bunker during the discharge process, thereby revealing the mechanisms underlying the adhesive clogging phenomenon. The findings offer significant insights for optimizing solutions to the adhesive clogging issues in underground coal bunkers and ensuring their safe and efficient operation.
Enhancing Mandibular Reconstruction with Surgeon–Bioengineer Collaboration: A Protocol for Virtual Surgical Planning
Authors: Shin Dong-Ho , Kim Hyo-Joon , Oh Ji-Su , Moon Seong-Yong
Published: January 2025

Background/Objectives: Computer-assisted mandibular reconstruction requires sophisticated technical expertise alongside surgical knowledge. This study aims to establish and validate an efficient collaborative protocol between oral and maxillofacial surgeons and bioengineers for virtual surgical planning in mandibular reconstruction. Methods: We developed a structured protocol with four sequential phases: (1) generation of 3D models from CT data, (2) virtual resection planning, (3) reconstruction design, and (4) surgical guide fabrication. Protocol efficiency was assessed through seven simulation trials measuring planning duration and required revisions. Clinical validation was performed in four mandibular reconstruction cases. Accuracy was evaluated by comparing virtual surgical plans to postoperative outcomes using 3-matic 13.0 software analysis. Results: Protocol implementation showed consistent efficiency across simulations with a mean planning duration of 2.86 working days (SD = 1.35). Only two of seven simulations required design revisions. Clinical application in four cases (three ameloblastomas, one odontogenic myxoma) demonstrated high precision with a mean virtual-to-actual discrepancy of 0.90 mm (SD = 0.34). Successful reconstructions were achieved across varying defect spans (29–53 mm) using both bicortical deep circumflex iliac artery (DCIA) flaps and monocortical iliac block bone grafts. The collaborative workflow resulted in optimized surgical guide design, reduced planning iterations, and improved surgical precision. Conclusions: The established surgeon–bioengineer collaborative protocol enhances the efficiency and accuracy of computer-assisted mandibular reconstruction while making advanced surgical planning techniques more accessible. While initial results are promising, future studies with larger patient cohorts and extended follow-up periods are needed to fully validate the protocol’s long-term benefits and broader applicability.
Correcting Ski Resort Trajectories Extracted from Video
Authors: Zhang Buchuan , Schadschneider Andreas
Published: January 2025

Accidents at ski resorts present a significant safety concern, underscoring the urgency of addressing these issues. This study aims to enhance safety protocols by providing resort operators with more effective data analysis methodologies. We present and test methods for analyzing video footage from downhill ski areas where detailed information needed to correct errors, due to perspective, lens distortion, etc., is not available. This can be the case, e.g., for webcam footage and accidental videos (e.g., on YouTube). As much of this kind of video is available and could be used for statistical analysis, methods are needed that allow for at least for an approximate consideration of such aspects. Using video footage obtained from various ski resorts, we developed and tested several methods for analyzing and correcting the trajectories of skiers captured in the videos. Our analysis revealed that using five reference lines, along with the most appropriate x and y coordinate corrections, is an effective approach for achieving precise calibration of the video data. The corrected trajectory data, adjusted for perspective distortions and scaling inaccuracies, provide a detailed basis for analyzing skier behavior and identifying high-risk zones prone to collisions.
Grid Anchor Lane Detection Based on Attribute Correlation
Authors: Feng Qiaohui , Chi Cheng , Chen Fei , Shen Jianhao , Xu Gang , Wen Huajie
Published: January 2025

The detection of road features is a necessary approach to achieve autonomous driving. And lane lines are important two-dimensional features on roads, which are crucial for achieving autonomous driving. Currently, research on lane detection mainly focuses on the positioning detection of local features without considering the association of long-distance lane line features. A grid anchor lane detection model based on attribute correlation is proposed to address this issue. Firstly, a grid anchor lane line expression method containing attribute information is proposed, and the association relationship between adjacent features is established at the data layer. Secondly, a convolutional reordering upsampling method has been proposed, and the model integrates the global feature information generated by multi-layer perceptron (MLP), achieving the fusion of long-distance lane line features. The upsampling and MLP enhance the dual perception ability of the feature pyramid network in detail features and global features. Finally, the attribute correlation loss function was designed to construct feature associations between different grid anchors, enhancing the interdependence of anchor recognition results. The experimental results show that the proposed model achieved first-place F1 scores of 93.05 and 73.27 in the normal and curved scenes on the CULane dataset, respectively. This model can balance the robustness of lane detection in both normal and curved scenarios.
A Virtual Synchronous Generator Control Strategy Based on Transient Damping Compensation and Virtual Inertia Adaptation
Authors: Xia Yan , Chen Yang , Wang Yao , Chen Renzhao , Li Ke , Shi Jinhui , Yang Yiqiang
Published: January 2025

To mitigate the challenges posed by transient oscillations and steady-state deviations in the traditional virtual synchronous generator (TVSG) that is subjected to active power and grid frequency disturbances, a VSG control strategy based on Transient Damping Compensation and Virtual Inertia Adaptation is presented. Initially, a closed-loop small-signal model for the grid-connected active power loop (APL) of the TVSG is constructed, which highlights the contradiction between the dynamic and static characteristics of TVSG output power through the analysis of root locus distribution trends. Secondly, a VSG control strategy based on Transient Damping Compensation (TDC) is proposed. The influence of APL system parameters introduced by TDC on system stability is qualitatively analyzed based on pole distribution trends and frequency response, and a comprehensive parameter design scheme is presented. In addition, based on the TDC algorithm, an improved virtual inertia adaptive strategy utilizing the Inverse Square Root Unit (ISRU) approach is designed, and the tuning range of parameters is provided. Finally, simulations and experiments verify that the proposed strategy exhibits superior active response performance and transient oscillation suppression capabilities, effectively eliminating active steady-state deviations caused by frequency disturbances in the power grid.
A New Transformation Method of the T2 Spectrum Based on Ordered Clustering—A Case Study on the Pore-Throat Utilization Rule of Supercritical CO2 Flooding in Low Permeability Cores
Authors: Su Yanchun , Zhao Chunhua , Li Xianjie , Wang Xiujun , Zhang Jian , Huang Bo , Tian Xiaofeng , Liu Mingxi , Song Kaoping
Published: January 2025

Nuclear magnetic resonance (NMR) and high-pressure mercury injection (HPMI) have been widely used as common characterization methods of pore-throat. It is generally believed that there is a power function relationship between transverse relaxation time (T2) and pore-throat radius (r), but the segmentation process of the pore-throat interval is subjective, which affects the conversion accuracy. In this paper, ordered clustering is used to improve the existing segmentation method of the pore-throat interval, eliminate the subjectivity in the segmentation process, and obtain a more accurate distribution curve of the pore-throat. For the three kinds of cores with ordinary-low permeability (K > 1 mD), ultra-low permeability (0.1 mD < K < 1 mD), and super-low permeability (K < 0.1 mD), the pore-throat distribution curves of the cores were obtained by using the improved T2 conversion method. Then, the oil and gas two-phase displacement experiment was carried out to investigate the degree of recovery and cumulative gas–oil ratio changes during the displacement process. Finally, the converted T2 spectrum was used to quantify the utilization of different pore sizes. The improved T2 conversion method not only has better accuracy but also is not limited by the pore-throat distribution types (such as unimodal, bimodal, and multi-modal, etc.) and is suitable for any core with measured HPMI pore-throat distribution and an NMR T2 spectrum. Combined with the results of core displacement and the degree of pore-throat utilization, it is found that the potential of miscible flooding to improve the recovery degree is in the order of ordinary-low permeability core (18–22%), ultra-low permeability core (25–29%), and super-low permeability core (8–12%). The utilization degree of immiscible flooding to the <10 nm pore-throat is low (up to 35%), while miscible flooding can effectively use the <3.7 nm pore-throat (up to 73%). The development effect of supercritical CO2 flooding on K < 0.1 mD reservoirs is not good, the seepage resistance of CO2 is large, the miscible flooding makes it difficult to improve the recovery degree, and the utilization effect of pore-throat is poor.
Research on the Parameter Prediction Model for Fully Mechanized Mining Equipment Selection Based on RF-WOA-XGBoost
Authors: Wu Yue , Sang Wenlong , Cao Xiangang , He Longlong
Published: January 2025

Fully mechanized mining equipment is core to the coal mining process. The selection process for this type of equipment is complex and heavily relies on experts’ experience for determining equipment parameters. This paper proposes a fully mechanized mining equipment parameter prediction model based on Extreme Gradient Boosting Regression Trees (XGBoost), which is developed based on the mapping relationships among geological parameters, fully mechanized mining face conditions, and the parameters of fully mechanized mining equipment. Feature selection is performed based on the feature importance ranking obtained through the Random Forest (RF) method, thereby reducing the model complexity. Different optimization algorithms are used to optimize the hyperparameters of XGBoost, and the results show that the Whale Optimization Algorithm (WOA) outperforms other algorithms in terms of convergence speed and optimization effectiveness. By comparing different prediction algorithms, it is found that the WOA-XGBoost model achieves higher prediction accuracy on the test set, with an average absolute error of 0.0458, root mean square error of 0.1610, and a coefficient of determination (R2) of 0.9451. Finally, a RF-WOA-XGBoost-based parameter prediction model for fully mechanized mining equipment is established, which is suitable for lightly inclined mining faces. This model reduces input complexity, improves the selection speed, minimizes reliance on experts, and ensures prediction accuracy, providing an effective reference for the parameter selection of fully mechanized mining equipment.
Interior Peak Solutions for a Semilinear Dirichlet Problem
Authors: Alharbi Hissah , Alkhuzayyim Hibah , Ben Ayed Mohamed , El Mehdi Khalil
Published: January 2025

In this paper, we consider the semilinear Dirichlet problem (Pε):−Δu+V(x)u=un+2n−2−ε, u>0 in Ω, u=0 on ∂Ω, where Ω is a bounded regular domain in Rn, n≥4, ε is a small positive parameter, and V is a non-constant positive C2-function on Ω¯. We construct interior peak solutions with isolated bubbles. This leads to a multiplicity result for (Pε). The proof of our results relies on precise expansions of the gradient of the Euler–Lagrange functional associated with (Pε), along with a suitable projection of the bubbles. This projection and its associated estimates are new and play a crucial role in tackling such types of problems.
Enhanced Detection Performance of Acute Vertebral Compression Fractures Using a Hybrid Deep Learning and Traditional Quantitative Measurement Approach: Beyond the Limitations of Genant Classification
Authors: Lee Jemyoung , Kim Minbeom , Park Heejun , Yang Zepa , Woo Ok Hee , Kang Woo Young , Kim Jong Hyo
Published: January 2025

Objective: This study evaluated the applicability of the classical method, height loss ratio (HLR), for identifying major acute compression fractures in clinical practice and compared its performance with deep learning (DL)-based VCF detection methods. Additionally, it examined whether combining the HLR with DL approaches could enhance performance, exploring the potential integration of classical and DL methodologies. Methods: End-to-End VCF Detection (EEVD), Two-Stage VCF Detection with Segmentation and Detection (TSVD_SD), and Two-Stage VCF Detection with Detection and Classification (TSVD_DC). The models were evaluated on a dataset of 589 patients, focusing on sensitivity, specificity, accuracy, and precision. Results: TSVD_SD outperformed all other methods, achieving the highest sensitivity (84.46%) and accuracy (95.05%), making it particularly effective for identifying true positives. The complementary use of DL methods with HLR further improved detection performance. For instance, combining HLR-negative cases with TSVD_SD increased sensitivity to 87.84%, reducing missed fractures, while combining HLR-positive cases with EEVD achieved the highest specificity (99.77%), minimizing false positives. Conclusion: These findings demonstrated that DL-based approaches, particularly TSVD_SD, provided robust alternatives or complements to traditional methods, significantly enhancing diagnostic accuracy for acute VCFs in clinical practice.
Lipopolymers as the Basis of Non-Viral Delivery of Therapeutic siRNA Nanoparticles in a Leukemia (MOLM-13) Model
Authors: Yotsomnuk Panadda , Rajendran Amarnath Praphakar , Sundaram Daniel Nisakar Meenakshi , Morales Luis Carlos , Kucharski Cezary , Nasrullah Mohammad , Skolpap Wanwisa , Jiang Xiaoyan , Gibson Spencer B. , Brandwein Joseph , Uludağ Hasan
Published: January 2025

Small interfering RNA (siRNA) therapy in acute myeloid leukemia (AML) is a promising strategy as the siRNA molecule can specifically target proteins involved in abnormal cell proliferation. The development of a clinically applicable method for delivering siRNA molecules is imperative due to the challenges involved in effectively delivering the siRNA into cells. We investigated the delivery of siRNA to AML MOLM-13 cells with the use of two lipid-substituted polyethyleneimines (PEIs), a commercially available reagent (Prime-Fect) and a recently reported reagent with improved lipid substitution (PEI1.2k-PHPA-Lin9). The siRNAs utilized in this study were targeting the oncogenes FLT3 and KMT2A::MLLT3. Both lipopolymers gave similar-size siRNA complexes (210–220 nm) with positive ζ-potentials (+17 to +25 mV). While the binding efficiency of both lipopolymers to siRNA were similar, PEI1.2k-PHPA-Lin9 complexes were more resistant to heparin-induced dissociation. The quantitative analysis of gene silencing performed by qPCR as well as immunostaining/flow cytometry indicated significant reduction in both FLT3 expression and FLT3 protein after specific siRNA delivery. The desired inhibition of cell growth was attained with both FLT3 and KMT2A::MLLT3 siRNAs, and the combination provided more potent effects in both cell growth and colony formation assays. Induction of apoptosis was confirmed after specific siRNA treatments using the Annexin V assay. Using Luc(+) MOLM-13 cells, the growth of the xenografted cells was shown to be retarded with Prime-Fect-delivered FLT3 siRNA, unlike the siRNA delivered with PEI1.2k-PHPA-Lin9. These results demonstrate the potential of designed lipopolymers in implementing RNAi (via delivery of siRNA) for inhibition of leukemia growth and provide evidence for the feasibility of targeting different oncogenes using siRNA-mediated therapy.
Analysis of Procurement Routes and Contract Types for Housing Retrofit in the United Kingdom
Authors: Panakaduwa Chamara , Coates Paul , Munir Mustapha
Published: January 2025

The study focused on the procurement routes and the forms of contract, currently used in the UK housing retrofit industry. Importantly, the contracts between the clients and the contractors were investigated. This study used a qualitative approach with a critical literature review and semi-structured interview data collection for the methodology. The findings show that the housing retrofit industry does not use any standard form of contract in general. Most contractors use guarantees, warranties, and invoices instead of standard forms of contract. They usually use bespoke contracts if contract administration is required. This is because there is nothing wrong with the existing way of addressing contract administration in retrofit. As the UK needs to retrofit 30.1 million houses, there can be contractual disputes aggregating to 8.1 million properties or GBP 221.4 billion project value. The study recommends using standard forms of contract to avoid and reduce the detrimental effects of contractual disputes in housing retrofit in the future.
Exploring Optimisation Pathways for Underground Space Quality Under the Synergy of Multidimensional Perception and Environmental Parameters
Authors: Yao Tianning , Sun Liang , Geng Lin , Xu Yao , Xu Ziqi , Hu Kuntao , Chen Xing , Liao Pan , Wang Jin
Published: January 2025

With the acceleration of urbanisation and the increased utilisation of underground space, providing a comfortable and healthy environment in public underground areas has emerged as a significant research topic. This study constructs a comprehensive decision-making framework for underground space environments by integrating human perception evaluations with physical environmental parameters. Using Shanghai Wujiaochang as a case study, field data collection and questionnaire surveys were conducted to evaluate key factors such as temperature (22.63 °C–26.39 °C), wind speed (0.26 m/s–0.67 m/s), and sound levels (59.68 dB–61.21 dB) for commercial-oriented spaces, and 63.15 dB–75.45 dB for transport-oriented spaces) to users’ perceived experiences. The appropriate ranges for key parameters were identified through single-indicator fitted regression analysis and the XGBoost machine-learning model, revealing the relationship between environmental parameters and human perception. The results indicated significant differences in user needs across various functional spaces, with commercial-oriented areas emphasising environmental attractiveness and comfort, while transport-oriented spaces prioritised access efficiency and safety. This study provided quantitative design benchmarks for underground spaces’ dynamic regulation and sustainable management, proposing a precise and adaptive environmental decision-making framework that combines physical parameters with user-perception feedback.
‘Archeology’ of Hidden Values of Underutilized Historic Industrial Sites in Context of Urban Regeneration and Nature-Based Solutions
Authors: Grazuleviciute-Vileniske Indre , Zmejauskaite Diana
Published: January 2025

Today, cities and their heritage function under so-called BANI (Brittle, Anxious, Nonlinear, and Incomprehensible) conditions. Climate change, military conflicts, and the urge to comply with sustainability requirements and to implement green transformation and urban regeneration cause a threat to rapid decision making, which may result in the loss of more subtle, intangible values in the urban environment. Such loss may particularly affect industrial heritage with its contested values and identities. This encourages searching for approaches to capture, analyze, and employ these invisible values for future developments. The methods of research used in this study include qualitative analysis of the literature, the theoretical conceptualization and development of a methodology to capture and record these invisible and intangible values, and the application of the developed methodology to the case study of a historical industrial site in Kaunas (Lithuania). The main findings of this research include a theoretical framework for the analysis of hidden values of historic industrial sites revolving around the concepts of place identity, spirit of place, symbolic potential of place, embedded values, and time depth and its application to the case study of the historic industrial site in Kaunas. This research has demonstrated that historic industrial sites existing in urban settings contain a multiplicity of aspects and meanings, which are not visible at first glance and require thorough qualitative analysis. These invisible aspects constitute important resources for the future development of the site and can contribute to the identity and local character of regeneration efforts and nature-based solutions.
Mechanical Properties, Impermeability, and Microstructure of Mortar Containing Recycled Fine Aggregates Modified by Graphene Oxide After High-Temperature Exposure
Authors: Xiang Zhao , Chen Guofu , Cai Zhiwei , Shao Jiaxing
Published: January 2025

Graphene oxide (GO) has been verified to ameliorate the performance attenuation of cement-based materials exposed to high temperatures; however, its efficacy on cement-based materials containing recycled fine aggregates (RFAs) remains ambiguous. Therefore, this study assesses the macro/microperformance variations in mortar containing RFAs with diverse GO dosages (0~0.08%) after high-temperature exposure. The hydration products, pore structure, and microstructure of mortar containing RFAs and GO were analyzed using an X-ray diffractometer, mercury intrusion porosimetry, and scanning electron microscopy, respectively. The study finds that GO can reduce the mass loss rate of mortar exposed to high temperatures and enhance the residual compressive strength and flexural strength. The incorporation of 0.06% GO leads to a 10.4% increase in compressive strength and a 32.3% increase in the flexural strength of mortar containing 50% RFA after high-temperature exposure at 500 °C. Furthermore, the incorporation of GO refined the pore size and improved the densification of the interfacial transition zone (ITZ) of the mortar after high-temperature exposure. Taking into account the mechanical properties, water absorption, and microstructure of the mortar containing RFAs, a GO dosage of 0.04~0.06% could be recommended as the optimum engineering dosage.
Proximity Labeling-Based Identification of MGAT3 Substrates and Revelation of the Tumor-Suppressive Role of Bisecting GlcNAc in Breast Cancer via GLA Degradation
Authors: Wang Bowen , He Xin , Zhou Yue , Tan Zengqi , Li Xiang , Guan Feng , Lei Lei
Published: January 2025

Glycosylation plays a critical role in various biological processes, yet identifying specific glycosyltransferase substrates remains a challenge due to the complexity of glycosylation. Here, we employ proximity labeling with biotin ligases BASU and TurboID to map the proximitome of MGAT3, a glycosyltransferase responsible for the biosynthesis of the bisecting GlcNAc structure, in HEK293T cells. This approach enriched 116 and 189 proteins, respectively, identifying 17 common substrates shared with bisecting GlcNAc-bearing proteome obtained via intact glycopeptide enrichment methods. Gene ontology analysis revealed that the enriched proteins were predominantly localized in the exosome, endoplasmic reticulum, and Golgi apparatus, consistent with subcellular localization of MGAT3 substrates. Notably, four novel substrates, GOLM2, CCDC134, ASPH, and ERO1A, were confirmed to bear bisecting GlcNAc modification, validating the utility of the proximity labeling method. Furthermore, we observed that bisecting GlcNAc modification inhibits breast cancer progression by promoting the degradation of α-galactosidase A (GLA). These findings demonstrate the efficacy of proximity labeling in identifying glycosyltransferase substrates and provide insights into the functional impact of bisecting GlcNAc modification.
Plant Coping with Cold Stress: Molecular and Physiological Adaptive Mechanisms with Future Perspectives
Authors: Feng Yan , Li Zengqiang , Kong Xiangjun , Khan Aziz , Ullah Najeeb , Zhang Xin
Published: January 2025

Cold stress strongly hinders plant growth and development. However, the molecular and physiological adaptive mechanisms of cold stress tolerance in plants are not well understood. Plants adopt several morpho-physiological changes to withstand cold stress. Plants have evolved various strategies to cope with cold stress. These strategies included changes in cellular membranes and chloroplast structure, regulating cold signals related to phytohormones and plant growth regulators (ABA, JA, GA, IAA, SA, BR, ET, CTK, and MET), reactive oxygen species (ROS), protein kinases, and inorganic ions. This review summarizes the mechanisms of how plants respond to cold stress, covering four main signal transduction pathways, including the abscisic acid (ABA) signal transduction pathway, Ca2+ signal transduction pathway, ROS signal transduction pathway, and mitogen-activated protein kinase (MAPK/MPK) cascade pathway. Some transcription factors, such as AP2/ERF, MYB, WRKY, NAC, and bZIP, not only act as calmodulin-binding proteins during cold perception but can also play important roles in the downstream chilling-signaling pathway. This review also highlights the analysis of those transcription factors such as bHLH, especially bHLH-type transcription factors ICE, and discusses their functions as phytohormone-responsive elements binding proteins in the promoter region under cold stress. In addition, a theoretical framework outlining plant responses to cold stress tolerance has been proposed. This theory aims to guide future research directions and inform agricultural production practices, ultimately enhancing crop resilience to cold stress.
Using Hybrid Deep Learning Models to Predict Dust Storm Pathways with Enhanced Accuracy
Authors: Yarmohamadi Mahdis , Alesheikh Ali Asghar , Sharif Mohammad
Published: January 2025

As a potential consequence of climate change, the intensity and frequency of dust storms are increasing. A dust storm arises when strong winds blow loose dust from a dry surface, transporting soil particles from one place to another. The environmental and human health impacts of dust storms are substantial. Accordingly, studying the monitoring of this phenomenon and predicting its pathways for early decision making and warning are vital. This study employs deep learning methods to predict dust storm pathways. Specifically, hybrid CNN-LSTM and ConvLSTM models have been proposed for the 24 h-ahead prediction of dust storms in the region under study. The Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) product that includes the dust particles and the meteorological information, such as surface wind speed and direction, relative humidity, surface air temperature, and skin temperature, is used to train the proposed models. These contextual features are selected utilizing the random forest feature importance method. The results indicate an improvement in the performance of both models by considering the contextual information. Moreover, a 0.2 increase in the Kappa coefficient criterion across all forecast hours indicates the CNN-LSTM model outperforms the ConvLSTM model when contextual information is considered.
Design of a System for Driver Drowsiness Detection and Seat Belt Monitoring Using Raspberry Pi 4 and Arduino Nano
Authors: Alvarez Oviedo Anthony , Mamani Villanueva Jhojan Felipe , Echaiz Espinoza German Alberto , Villanueva Juan Moises Mauricio , Salazar Andrés Ortiz , Villarreal Elmer Rolando Llanos
Published: January 2025

This research explores the design of a system for monitoring driver drowsiness and supervising seat belt usage in interprovincial buses. In Peru, road accidents involving long-distance bus transportation amounted to 5449 in 2022, and the human factor plays a significant role. It is essential to understand how the use of non-invasive sensors for monitoring and supervising passengers and drivers can enhance safety in interprovincial transportation. The objective of this research is to develop a system using a Raspberry Pi 4 and Arduino Nano that allows for the storage of monitoring data. To achieve this, a conventional camera and MediaPipe were used for driver drowsiness detection, while passenger supervision was carried out using a combination of commercially available sensors as well as custom-built sensors. RS485 communication was utilized to store data related to both the driver and passengers. The simulations conducted demonstrate a high level of reliability in detecting driver drowsiness under specific conditions and the correct operation of the sensors for passenger supervision. Therefore, the proposed system is feasible and can be implemented for real-world testing. The implications of this research suggest that the system’s cost is not a barrier to its implementation, thus contributing to improved safety in interprovincial transportation.
Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG Signals
Authors: Tasci Gulay , Barua Prabal Datta , Tanko Dahiru , Keles Tugce , Tas Suat , Sercek Ilknur , Kaya Suheda , Yildirim Kubra , Talu Yunus , Tasci Burak , Ozsoy Filiz , Gonen Nida , Tasci Irem , Dogan Sengul , Tuncer Turker
Published: January 2025

Background: Electroencephalography (EEG) signal-based machine learning models are among the most cost-effective methods for information retrieval. In this context, we aimed to investigate the cortical activities of psychotic criminal subjects by deploying an explainable feature engineering (XFE) model using an EEG psychotic criminal dataset. Methods: In this study, a new EEG psychotic criminal dataset was curated, containing EEG signals from psychotic criminal and control groups. To extract meaningful findings from this dataset, we presented a new channel-based feature extraction function named Zipper Pattern (ZPat). The proposed ZPat extracts features by analyzing the relationships between channels. In the feature selection phase of the proposed XFE model, an iterative neighborhood component analysis (INCA) feature selector was used to choose the most distinctive features. In the classification phase, we employed an ensemble and iterative distance-based classifier to achieve high classification performance. Therefore, a t-algorithm-based k-nearest neighbors (tkNN) classifier was used to obtain classification results. The Directed Lobish (DLob) symbolic language was used to derive interpretable results from the identities of the selected feature vectors in the final phase of the proposed ZPat-based XFE model. Results: To obtain the classification results from the ZPat-based XFE model, leave-one-record-out (LORO) and 10-fold cross-validation (CV) methods were used. The proposed ZPat-based model achieved over 95% classification accuracy on the curated EEG psychotic criminal dataset. Moreover, a cortical connectome diagram related to psychotic criminal detection was created using a DLob-based explainable artificial intelligence (XAI) method. Conclusions: In this regard, the proposed ZPat-based XFE model achieved both high classification performance and interpretability. Thus, the model contributes to feature engineering, psychiatry, neuroscience, and forensic sciences. Moreover, the presented ZPat-based XFE model is one of the pioneering XAI models for investigating psychotic criminal/criminal individuals.
The Practical Implications of Re-Referencing in ERP Studies: The Case of N400 in the Picture–Word Verification Task
Authors: Jovanović Vojislav , Petrušić Igor , Ković Vanja , Savić Andrej M.
Published: January 2025

Background: The selection of an optimal referencing method in event-related potential (ERP) research has been a long-standing debate, as it can significantly influence results and lead to data misinterpretation. Such misinterpretation can produce flawed scientific conclusions, like the inaccurate localization of neural processes, and in practical applications, such as using ERPs as biomarkers in medicine, it may result in incorrect diagnoses or ineffective treatments. In line with the development and advancement of good scientific practice (GSP) in ERP research, this study sought to address several questions regarding the most suitable digital reference for investigating the N400 ERP component. Methods: The study was conducted on 17 neurotypical participants. Based on previous research, the references evaluated included the common average reference (AVE), mean earlobe reference (EARS), left mastoid reference (L), mean mastoids reference (MM), neutral infinity reference (REST), and vertex reference (VERT). Results: The results showed that all digital references, except for VERT, successfully elicited the centroparietal N400 effect in the picture–word verification task. The AVE referencing method showed the most optimal set of metrics in terms of effect size and localization, although it also produced the smallest difference waves. The most similar topographic dynamics in the N400 window were observed between the AVE and REST referencing methods. Conclusions: As the most optimal regions of interest (ROI) for the picture–word elicited N400 effect, nine electrode sites spanning from superior frontocentral to parietal regions were identified, showing consistent effects across all referencing methods except VERT.
Impact of Sex-Related Differences in Infrarenal Aortic Neck Morphology on Outcomes of Endovascular Aneurysm Repair for Similar-Sized Aortic Aneurysm
Authors: Martinelli Ombretta , Marzano Antonio , Bellini Maria Irene , Gattuso Roberto , Di Marzo Luca , Gonta Valeria , Jabbour Jihad , Mansour Wassim , Cuozzo Simone
Published: January 2025

Objectives: This study aimed to evaluate whether gender-related anatomical differences in proximal aortic neck morphology affect the feasibility and outcomes of endovascular aortic aneurysm repair (EVAR) in women with abdominal aortic aneurysms (AAA). Methods: This study performed a retrospective analysis of patients electively treated by EVAR for infrarenal AAA between January 2019 and December 2023. Demographics, anatomical characteristics, and stent graft details were analyzed. The primary endpoint was technical success. Secondary endpoints included freedom from aortic and neck-related reinterventions, endoleak rate, and freedom from aneurysm-related mortality during follow-up. Technical aspects, including adherence to the instructions for use (IFUs), were retrospectively analyzed. Results: One-hundred-seventeen patients (fifty-six females; mean age 76.2 ± 5.3 years) underwent elective EVAR for AAA. Demographics and comorbidities were homogeneous across genders. Female patients (Group A) demonstrated a higher prevalence of hostile proximal aortic neck features, including neck length < 10 mm and angulation > 60° (p = 0.009, p = 0.029, respectively) and a higher frequency of off-label EVAR procedures (28.6% vs. 11.5%; p = 0.034). The overall technical success rate was 98.3%, with no significant differences between genders in terms of stent graft selection, use of suprarenal fixation, or incidence of type 1–3 endoleaks. The median follow-up period was 35.2 ± 12.7 months, showing comparable rates of neck-related reinterventions, open conversions, and aneurysm-related mortality between genders. Notably, off-label EVAR was identified as an independent risk factor for type 1A endoleaks, reinterventions, and aneurysm-related mortality (p < 0.00001, p < 0.0001, and p = 0.001, respectively). Conclusions: Female patients undergoing EVAR often present with hostile proximal aortic neck features and are treated at an older age than males. Despite these differences, technical success rates and mid- to long-term outcomes were comparable between genders, with no variation in stent graft selection or suprarenal fixation use. Effective procedural planning, device selection, and surgical expertise appear to mitigate historical gender-related anatomical challenges. Further large-scale studies are needed to confirm whether anatomical factors alone drive outcomes, irrespective of gender.