bluuebunny/crossref_metadata_2025 huggingface dataset:
Fertility-Related Concerns in Long-Term Survivors of Childhood Cancer: A Canadian Cohort Study
Authors: Holmer Pauline , Henry Brianna , Duong Jenny , Lawal Oluwaseyi A. , Fidler-Benaoudia Miranda M. , Reynolds Kathleen , Michel Gisela , Lehmann Vicky , Schulte Fiona S. M.
Published: December 2024

Survivors of childhood cancer face reduced fertility, which can be a significant cause for concern. Our study aimed to assess the prevalence of fertility-related concerns and identify associated factors. Self-report data were collected with the Long-Term Survivor Questionnaire at the Alberta Children’s Hospital’s Long-Term Survivor Clinic (LTSC) between January 2021 and September 2023. Eligible participants were diagnosed before the age of 21 years, ≥2 years off therapy, and ≥13 years old. We analyzed fertility-related concerns cross-sectionally for the whole sample and longitudinally for a subsample of participants that completed three questionnaires. We included N = 311 survivors (49.2% female; mean age = 22.7 years, range = 13.9–42.1; mean time since diagnosis = 14.5 years, range = 2.7–38.4), of whom 21.2% reported fertility-related concerns. Those with additional health concerns and those who were sexually active were more likely to report fertility-related concerns, whereas lymphoma survivors were least likely to report concerns. In the subsample who completed three questionnaires (n = 80), 30% reported having concerns at least once, whereas 9% expressed persistent concerns. Fertility-related concerns are highly prevalent among young survivors of childhood cancer and warrant attention from healthcare professionals. Services that systematically address fertility-related concerns throughout long-term follow-up are urgently needed. These services should provide a space to discuss concerns, provide education, and initiate fertility consultations if desired.
Personalized Ultra-Fractionated Stereotactic Adaptive Radiotherapy for Non-Small Cell Lung Cancer Using Varian Ethos Therapy System
Authors: Leipold Vanda , Jakšić Blanka , Avdičević Asmir , Kosmina Domagoj , Kaučić Hrvoje , Alerić Ivana , Schwarz Karla , Mlinarić Mihaela , Ursi Giovanni , Čehobašić Adlan , Schwarz Dragan
Published: December 2024

We present a patient treated with personalized ultra-fractionated stereotactic adaptive radiotherapy (PULSAR) for non-small cell lung cancer (NSCLC) using the adaptive Varian Ethos™ system equipped with the novel HyperSight imaging platform. Three pulses of 12 Gy were separated by a pause of four weeks during which the tumor was given enough time to respond to treatment. Only initial planning computed tomography (CT) was acquired on a CT simulator (Siemens Somatom Definition Edge), whereas other pulses were adapted using online cone beam computed tomography (CBCT) images (iCBCT Acuros reconstruction) acquired while the patient was lying on the treatment couch and delivered immediately. Significant tumor reduction was achieved between pulses, resulting in improved organs-at-risk sparing. In addition, the on-couch plan optimization based on CBCT greatly reduced the patient’s stay at the clinic and the duration of treatment preparation.
Canadian Consensus Recommendations for Predictive Biomarker Testing in Gastric and Gastroesophageal Junction Adenocarcinoma
Authors: Brezden-Masley Christine , Fiset Pierre O. , Cheung Carol C. , Arnason Thomas , Bateman Justin , Borduas Martin , Evaristo Gertruda , Ionescu Diana N. , Lim Howard J. , Sheffield Brandon S. , Soldera Sara V. , Streutker Catherine J.
Published: December 2024

Gastric cancer is common globally and has a generally poor prognosis with a low 5-year survival rate. Targeted therapies and immunotherapies have improved the treatment landscape, providing more options for efficacious treatment. The use of these therapies requires predictive biomarker testing to identify patients who can benefit from their use. New therapies on the horizon, such as CLDN18.2 monoclonal antibody therapy, require laboratories to implement new biomarker tests. A multidisciplinary pan-Canadian expert working group was convened to develop guidance for pathologists and oncologists on the implementation of CLDN18.2 IHC testing for gastric and gastroesophageal junction (G/GEJ) adenocarcinoma in Canada, as well as general recommendations to optimize predictive biomarker testing in G/GEJ adenocarcinoma. The expert working group recommendations highlight the importance of reflex testing for HER2, MMR and/or MSI, CLDN18, and PD-L1 in all patients at first diagnosis of G/GEJ adenocarcinoma. Testing for NTRK fusions may also be included in reflex testing or requested by the treating clinician when third-line therapy is being considered. The expert working group also made recommendations for pre-analytic, analytic, and post-analytic considerations for predictive biomarker testing in G/GEJ adenocarcinoma. Implementation of these recommendations will provide medical oncologists with accurate, timely biomarker results to use for treatment decision-making.
Planned Abscopal Effect with Concurrent Pembrolizumab and Ablative Radiotherapy to Pulmonary Metastasis: A Case Report and Review of the Literature
Authors: Lee Yu-Ting , Chen Chien-Chin , Chao Hsiya , Chang Chih-Chia , Lee Cheng-Yen
Published: December 2024

Most abscopal effects are reported as sporadic and unpredictable events following radiotherapy at symptomatic sites. Herein, we report a case in which a planned abscopal effect was induced following deliberate radiotherapy and concurrent systemic immunotherapy. A 53-year-old man with a combined positive score ≥10 developed extensive metastatic bladder cancer after progressing on conventional chemotherapy. Extensive metastases were identified in his liver, lungs, and bones. He later had four cycles of single-agent pembrolizumab and planned hypofractionated radiotherapy at an ablative dose to selected metastatic lung tumors and developed complete remission of disease even when pembrolizumab was discontinued. This is a clear demonstration that the abscopal effect could be harnessed in a systematic manner with a combined positive score and aggressive local radiotherapy.
Additions to the Marine Mastogloia (Bacillariophyceae) from Cuban Coasts; Remarks on Misidentified Taxa
Authors: Siqueiros Beltrones David Alfaro , Echevarría Herrera Erisbel , López-Fuerte Francisco Omar
Published: December 2024

During a floristic inspection of sediment samples from Playa Las Gaviotas, 69 specific and infra-specific taxa of Mastogloia were identified in the diatom taxocenosis, including 42 unrecorded taxa for the Cuban coasts. These are added to the 59 Mastogloia taxa previously listed. In this way, currently, the 101 Mastogloia comprised in the benthic diatom associations of the Cuban coast constitute the highest number of taxa ever recorded in similar floristic studies, which also indicates their tropical affinity. Furthermore, observations are made on misidentified taxa such as Mastogloia (Orthoneis) aspera, M. asperuloides, M. (Orthoneis) clevei, M. floridensis, M. latecostata, M. laterorostrata, M. ovata, M. sigillata, M. pseudolatecostata, and others.
Review of Case Study Results: Assessing the Effectiveness of Curcumin, St. John’s Wort, Valerian Root, Milk Thistle, and Ashwagandha in the Intervention for Obsessive-Compulsive Disorder
Authors: Sathvika Veerabhadrappa Pallavi , Subhas Prathibha Guttal , Bhattacharjee Debayan , Koppad Vejetha Nagaraj , Samrat Uday , Karibasappa Sindhu Bindapla , Sagar Kadappara Mallikarjun
Published: December 2024

Background: The recurring actions and intrusive thoughts that characterise obsessive–compulsive disorder (OCD), a long-term mental illness, are known as compulsions. The cornerstones of conventional treatment are psychological counselling and prescribed medication; nonetheless, interest in complementary therapies has grown. This review looks at how well Ashwagandha, curcumin, milk thistle, valerian root, and St. John’s wort may help with OCD symptoms. Methods: To assess these herbal supplements’ potential for therapeutic benefit, case reports and clinical trials were examined. Results: Curcumin, which contains anti-inflammatory and neuroprotective properties, has shown promise in reducing the severity of OCD symptoms. Traditionally used to treat depression, St. John’s wort has shown some potential in lowering anxiety and compulsive behaviour. The herb milk thistle, which is mainly used for liver protection, did not affect OCD symptoms. Anxiolytic valerian root barely offered relief from symptoms associated with anxiety; it has only little effect on obsessions and compulsions. The adaptive Ashwagandha has demonstrated a great deal of promise in lowering stress and enhancing general well-being, which may help with symptom alleviation. Although the initial results are favourable, larger scale randomised controlled trials are necessary to validate reliability and efficacy. Conclusion: This study illustrates how herbal supplements may be used in addition to conventional medications as adjunctive treatments for OCD.
Predictive Factors and Risk Assessment for Hospitalization in Chest Pain Patients Admitted to the Emergency Department
Authors: Kagansky Nadya , Mazor David , Wajdi Ayashi , Maler Yaron Yulia , Sharfman Miya , Ziv Baran Tomer , Kagansky Dana , Pachys Gal , Levy Yochai , Trotzky Daniel
Published: December 2024

Background: Chest pain is one of the most common reasons for emergency department (ED) visits. Patients presenting with inconclusive symptoms complicate the diagnostic process and add to the burden upon the ED. This study aimed to determine factors possibly influencing ED decisions on hospitalization versus discharge for patients with the diagnosis of chest pain. Methods: In the cohort study including 400 patients admitted to the emergency unit with a working diagnosis of chest pain, data on demographics, medical history, symptoms, lab results, and risk scores were collected from the medical records of patients admitted to the ED with a working diagnosis of chest pain. To reduce potential bias, the analysis was restricted to 330 patients who were referred to the ED by a primary care provider or clinic for chest pain. Results: Of 330 patients admitted to the ED, 58.5% were discharged, and 41.5% were hospitalized. Hospitalized patients were significantly older, with a median age of 70 versus 57 years for those discharged (p < 0.001). A higher proportion of hospitalizations occurred during the late-night shift. Significant predictors of hospitalization included hyperlipidemia (OR 3.246), diaphoresis (OR 8.525), dyspnea (OR 2.897), and hypertension (OR 1.959). Nursing home residents had a lower risk of hospitalization (OR 0.381). The area under the ROC curve for this model was 0.801 (95% CI: 0.753–0.848), indicating the predictive accuracy of the model in estimating the probability of admission. The HEART (history, ECG, age, risk factors, and troponin level) score was more effective than the TIMI (Thrombolysis in Myocardial Infarction) score in predicting the need for hospitalization, with an area under the curve (AUC) of 0.807 compared to 0.742 for TIMI. Conclusions: The HEART score in comparison with TIMI score proved especially valuable for quick risk assessment for hospitalization. The model that included hyperlipidemia, diaphoresis, dyspnea, and hypertension was the most predictive for the risk of hospitalization. Further research with larger populations is needed to validate these findings.
Efficacy of the Geriatric Trauma Outcome Score (GTOS) in Predicting Mortality in Trauma Patients: A Retrospective Cross-Sectional Study
Authors: Huang Ching-Ya , Wu Shao-Chun , Lin Tsan-Shiun , Kuo Pao-Jen , Yang Johnson Chia-Shen , Hsu Shiun-Yuan , Hsieh Ching-Hua
Published: December 2024

Background: Trauma has a profound impact on mortality as well as short- and long-term health outcomes. For trauma patients to receive medical care in a timely manner, early identification and risk assessment are essential. The Geriatric Trauma Outcome Score (GTOS), which was created by combining age, the Injury Severity Score (ISS), and the requirement for packed red blood cell transfusion, has proven to be a valuable prognostic tool for elderly trauma patients, though its applicability to general trauma patients is still understudied. Methods: This retrospective study analyzed data from the Trauma Registry System at a Level I trauma center in southern Taiwan, covering the period from 1 January 2009 to 31 December 2021. This study included 40,068 trauma patients aged 20 years and older. Statistical analyses included chi-square tests, ANOVA, Mann–Whitney U tests, and multivariate analyses to identify independent risk factors for mortality. The predictive performance of the GTOS was assessed using the area under the curve (AUC) of the receiver operating characteristic curve. Results: The final study population included 40,068 patients, with 818 deaths and 39,250 survivors. Deceased patients had higher GTOS scores (mean 132.8 vs. 76.1, p < 0.001) and required more blood transfusions (mean 4.0 vs. 0.3 units, p < 0.001) compared to survivors. The optimal GTOS cut-off value for predicting mortality was 104.5, with a sensitivity of 82.6% and a specificity of 84.3% (AUC = 0.917). A high GTOS score was associated with increased mortality (9.6 vs. 0.4%, p < 0.001) compared with a low GTOS score, even after adjusting for confounding factors (adjusted mortality rate of 2.86, p < 0.001), and a longer hospital stay (14.0 vs. 7.7 days, p < 0.001). Conclusions: The GTOS is a valuable prognostic tool for predicting mortality in trauma patients, providing a simple and rapid assessment method. Its high predictive accuracy supports its use in broader trauma patient populations beyond the elderly. Further studies are recommended to refine and validate the GTOS in diverse trauma settings to enhance its clinical utility.
Nosocomial Bacterial Bronchopneumonia and SARS-CoV-2 Pneumonia in Patients with Traumatic Injuries: Imaging Aspects and Macroscopic and Microscopic Findings of Lung Tissue
Authors: Gavriliţă Georgiana-Denisa , Ungureanu Ştefania , Dăescu Ecaterina , Gavriliță Mircea-Nicu , Ţîncu Cristian-Cosmin , Enache Alexandra
Published: December 2024

Background: Patients with traumatic injuries often represent the best hosts for healthcare-associated infections, especially pneumonia or bronchopneumonia. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic raised serious problems in the diagnosis and treatment of patients that had a SARS-CoV-2 infection and associated nosocomial bacterial bronchopneumonia. In forensic medicine, these aspects need to be considered when establishing the cause of death and the distinction between the two types of bronchopneumonia is of particular importance. Methods: We present nine cases that were autopsied at the Institute of Forensic Medicine Timisoara between 1 June 2020 and 31 December 2021, that presented traumatic injuries, a SARS-CoV-2 infection, and bronchopneumonia. Results: We focused on the main findings of the macroscopic and microscopic aspects of lung tissues. Conclusions: We consider that the aspects we highlighted in this study, can be very useful in forensic practice in cases with a pluri-factorial pathology.
An Efficient Deep Learning Approach for Malaria Parasite Detection in Microscopic Images
Authors: Boit Sorio , Patil Rajvardhan
Published: December 2024

Background: Malaria is a life-threatening disease spread by infected mosquitoes, affecting both humans and animals. Its symptoms range from mild to severe, including fever, muscle discomfort, coma, and kidney failure. Accurate diagnosis is crucial but challenging, relying on expert technicians to examine blood smears under a microscope. Conventional methods are inefficient, while machine learning approaches struggle with complex tasks and require extensive feature engineering. Deep learning, however, excels in complex tasks and automatic feature extraction. Objective: This paper presents EDRI, which is a novel hybrid deep learning model that integrates multiple architectures for malaria detection from red blood cell images. The EDRI model is designed to capture diverse features and leverage multi-scale analysis. Methods: The proposed EDRI model is trained and evaluated on the NIH Malaria dataset comprising 27,558 labeled microscopic red blood cell images. Results: Experiments demonstrate its effectiveness, achieving an accuracy of 97.68% in detecting malaria, making it a valuable tool for clinicians and public health professionals. Conclusions: The results demonstrate the effectiveness of proposed model’s ability to detect malaria parasite from red blood cell images, offering a robust tool for rapid and reliable malaria diagnosis.
Two-Step Image Registration for Dual-Layer Flat-Panel Detectors
Authors: Kim Dong Sik , Lee Dayeon
Published: December 2024

Background: For a single exposure in radiography, a dual-layer flat-panel detector (DFD) can provide spectral images and efficiently utilize the transmitted X-ray photons to improve the detective quantum efficiency (DQE) performance. In this paper, to acquire high DQE performance, we present a registration method for X-ray images acquired from a DFD, considering only spatial translations and scale factors. The conventional registration methods have inconsistent estimate accuracies depending on the captured object scene, even when using entire pixels, and have deteriorated frequency performance because of the interpolation method employed. Methods: The proposed method consists of two steps; the first step is conducting a spatial translation according to the Fourier shift theorem with a subpixel registration, and the second step is conducting a scale transformation using cubic interpolation to process the X-ray projections. To estimate the subpixel spatial translation, a maximum-amplitude method using a small portion of the slant-edge phantom is used. Results: The performance of the proposed two-step method is first theoretically analyzed and then observed by conducting extensive experiments and measuring the noise power spectrum and DQE. An example for registering chest images is also shown. For a DFD, the proposed method shows a better registration result than the conventional one-step registration. The DQE improvement was more than 56% under RQA 9 compared to the single flat-panel detector case. Conclusions: The proposed two-step registration method can efficiently provide aligned image pairs from the DFD to improve the DQE performance at low doses and, thus, increase the accuracy of clinical diagnosis.
GPU-Enabled Volume Renderer for Use with MATLAB
Authors: Scheible Raphael
Published: December 2024

Traditional tools, such as 3D Slicer, Fiji, and MATLAB®, often encounter limitations in rendering performance and data management as the dataset sizes increase. This work presents a GPU-enabled volume renderer with a MATLAB® interface that addresses these issues. The proposed renderer uses flexible memory management and leverages the GPU texture-mapping features of NVIDIA devices. It transfers data between the CPU and the GPU only in the case of a data change between renderings, and uses texture memory to make use of specific hardware benefits of the GPU and improve the quality. A case study using the ViBE-Z zebrafish larval dataset demonstrated the renderer’s ability to produce visualizations while managing extensive data effectively within the MATLAB® environment. The renderer is available as open-source software.
Pijolavirus UFJF_PfSW6 Infection in Pseudomonas fluorescens Induces a Prophage Belonging to a Novel Genus in Peduoviridae Family
Authors: Vidigal Pedro Marcus Pereira , Brum João Mattos , Lopez Maryoris Elisa Soto , Mantovani Hilário Cuquetto , Hungaro Humberto Moreira
Published: December 2024

Background/Objectives: This study explores the genome sequencing data from the infection of Pseudomonas fluorescens UFV 041 by the bacteriophage Pijolavirus UFJF_PfSW6, aiming to identify and characterize prophages induced in the host bacterium during the infection. Methods: Scaffolds from sequencing data were analyzed, and reads were mapped to identify potential prophages using phage-to-host coverage metrics. The putative prophage scaffold was annotated, taxonomically classified, and its integration in the host bacterium was verified by PCR amplification of two target genes. We also tested whether mitomycin treatment could induce the prophage to enter the lytic cycle. Results: The prophage UFJF_PfPro was identified with a high phage-to-host coverage ratio. Its genome is 32,700 bp in length, containing 42 genes, 3 terminators, and 11 promoters, with 98.84% completeness. PCR confirmed its integration into P. fluorescens UFV 041, but mitomycin treatment did not induce the lytic cycle. The UFJF_PfPro genome shares 38.60% similarity with the closest lytic phages in the Phitrevirus genus, below genus and species assignment thresholds. A viral proteomic tree clustered UFJF_PfPro with Phitrevirus in a clade representing the Peduoviridae family. Conclusions: The UFJF_PfPro is a prophage integrated into the P. fluorescens UFV 041 genome, but we were unable to induce it to enter the lytic cycle using mitomycin treatment. The genome of UFJF_PfPro encodes all structural proteins typical of the Caudoviricetes class and shares low genomic similarity with species of the genus Phitrevirus, suggesting that UFJF_PfPro represents a new genus and species within the Peduoviridae family.
The Discharge-Induced Polarity-Dependent Propagation Characteristics of a Strong Shock Wave
Authors: Markhotok Anna
Published: December 2024

The specifics of a shock wave propagation down a positive column of a DC discharge in molecular chemically inert gases has been investigated. It was shown that axial gradients caused by the imbalance in the charged particle momentum transfer to the gas molecules can be a reason for the shock velocity dependence on the electric field direction. In pure nitrogen gas, the calculated shock velocity difference of up to 13.5% is in good agreement with the 12% value obtained in the experiment. A returning gas flow organizing in the discharge as a possible mechanism for an extended shock structure and a number of kinetical factors capable of affecting the shock motion are discussed.
Joint User Association, Power Allocation and Beamforming for NOMA-Based Integrated Satellite–Terrestrial Networks
Authors: Xin Peizhe , Fu Zihao , Chen Zhiyi , Jiang Jing , Zou Jing , Zhang Yu , Hu Xinyue
Published: December 2024

This paper investigated a non-orthogonal multiple access (NOMA)-based integrated satellite–terrestrial network (ISTN), where each user can select to access a terrestrial base station (BS) or the satellite according to the capacity of BS and their individual transmission requirements. A two-stage algorithm is proposed to solve the achievable sum rate maximizing resource optimization problem. In the first stage, user associations are determined based on individual preference lists and the backhaul capacities of the access points (APs). In the second stage, the power allocation, and the receiving beamforming vectors are optimized alternately. Within each iteration, the closed-form solution for the transmit power is derived. Simulation results show the effectiveness of the proposed algorithm and the benefits brought by NOMA. When the backhaul link capacity of terrestrial BSs is sufficient, users (UEs) prefer to access these BSs. Otherwise, the satellite can offer QoS guarantees to UEs. Furthermore, the overall system performance reaches its optimum when the number of UEs in the system matches the number of receive antennas at the APs.
Contraction Heuristics for Tensor Decision Diagrams
Authors: Larsen Christian Bøgh , Olsen Simon Brun , Larsen Kim Guldstrand , Schilling Christian
Published: December 2024

In this paper, we study the equivalence problem for quantum circuits: Given two quantum circuits, are they equivalent? We reduce this problem to the contraction problem of a tensor network. The order in which the contraction operations between tensors are applied has a crucial impact on efficiency, which is why many heuristics have been proposed. In this work, we use an efficient representation of tensors as a tensor decision diagram. Since existing contraction heuristics do not perform well in combination with these diagrams, we propose two new contraction heuristics. We demonstrate experimentally that our heuristics outperform other state-of-the-art heuristics. We also demonstrate that our framework yields state-of-the-art performance for equivalence checking.
Exploring Grade 12 Learners’ Understanding of Geometric Transformations Through the STAD Cooperative Learning Model
Authors: Mukuka Angel , Tatira Benjamin
Published: December 2024

Research has shown that both learners and teachers struggle to understand and teach geometric transformations meaningfully. This mixed-methods case study examined the efficacy of the Student Teams Achievement Division (STAD) cooperative learning model in fostering learners’ conceptual understanding of geometric transformations. This study involved 28 Grade 12 learners from one intact class. In addition to pre- and post-intervention tests, which measured learners’ conceptual understanding of geometric transformations, participants completed a feedback questionnaire at the end of the intervention. The results show that the STAD model significantly improved learners’ grasp of geometric transformations, as demonstrated by higher test scores in the post-test. Participants also highlighted the importance of well-crafted questions during group discussions and oral quizzes, teacher explanations during whole-class presentations, and the motivational impact of criteria for selecting and awarding top-performing groups. Based on Shapiro’s intervention evaluation criteria, the STAD model was found to be effective, with high levels of acceptability, integrity, and social validity. While this study confirms the STAD model’s effectiveness in enhancing conceptual understanding and social learning, it also emphasizes the importance of considering contextual factors, such as group dynamics and classroom resource availability, when implementing this cooperative learning model. Teachers are encouraged to tailor this learning strategy to their specific classroom environments and learners’ needs.
EEG and fNIRS Signal-Based Emotion Identification by Means of Machine Learning Algorithms During Visual Stimuli Exposure
Authors: Sánchez-Reolid Daniel , García-Pérez Eloy , Borja Alejandro L. , Fernández-Caballero Antonio , Sánchez-Reolid Roberto
Published: December 2024

This paper presents the identification of arousal and valence during visual stimuli exposure using electroencephalograms (EEGs) and functional near-infrared spectroscopy (fNIRS) signals. Specifically, various images were shown to several volunteers to evoke different emotions defined by their level of arousal and valence, such as happiness, sadness, fear, and anger. Brain activity was recorded using the Emotiv EPOC X and NIRSport2 devices separately. The recorded signals were then processed and analyzed to identify the primary brain regions activated during the trials. Next, machine learning methods were employed to classify the evoked emotions with highest accuracy values of 71.3% for EEG data with a Multi-Layer Perceptron (MLP) method and 64.0% for fNIRS data using a Bagging Trees (BAG) algorithm. This approach not only highlights the effectiveness of using EEG and fNIRS technologies but also provides insights into the complex interplay between different brain areas during emotional experiences. By leveraging these advanced acquisition techniques, this study aims to contribute to the broader field of affective neuroscience and improve the accuracy of emotion recognition systems. The findings could have significant implications for developing intelligent systems capable of more empathetic interactions with humans, enhancing applications in areas such as mental health, human–computer interactions, or adaptive learning environments, among others.
Molecularly Imprinted QCM Sensor Based on Hollow ZIF-67@PDA for Sparfloxacin Detection
Authors: Liao Shuang , Wang Liling , Chen Cheng , Xu Lin , Wang Houjun , Ye Peng , Sun Mengmeng , Tan Feng
Published: December 2024

In this study, we present the design of a molecularly imprinted polymer (MIP) for the detection of the antibiotic sparfloxacin (SPFK), combined with a high-precision quartz crystal microbalance (QCM) sensor. Green-synthesized ZIF-67@PDA-MIP was directly immobilized on the gold electrode surface of the QCM. This configuration takes advantage of the selective recognition capabilities of the MIP and the high-sensitivity response characteristics of the QCM sensor. It overcomes the limitations of traditional SPFK detection methods, offering both accurate detection and rapid responses for practical applications. The MIP-QCM sensor demonstrated enhanced sensitivity and affinity, achieving a detection limit as low as 10.3 ng/mL. Additionally, the selectivity of the sensor for SPFK was superior to that of other non-template molecules. It effectively detected SPFK residues in milk with acceptable accuracy, as indicated by recoveries ranging from 96.16% to 104.64% and a relative standard deviation (RSD) of less than 4.64%. These results suggest that the proposed MIP-QCM sensor provides an accurate, sensitive, rapid, and cost-effective method for detecting SPFK residues in animal products, and this advancement is expected to promote the widespread adoption of high-precision sensors in food safety testing.
Design Strategies of Integrated Metal-Oxide Semiconductor-Based Resistive Sensor Systems for Ammonia Detection
Authors: Yan Yingzhan , Xu Jing , Peng Zhilong , Ji Zhe , Gao Yuan , Jia Lu , Xu Qian
Published: December 2024

Chemical production activities cause large amounts of ammonia to evaporate into the atmosphere, degrading air quality and even endangering public health, so monitoring ammonia in real time is significant. Traditional detection techniques, including spectrometers, chromatography, and pumping methods, are characterized by high costs, complex operation, significant delays, and limited compatibility, which obstructs the immediate identification of ammonia and the timely provision of information. Due to their distinct benefits such as compact size, affordability, quick response time, and lack of need for manual operation, resistive ammonia sensors hold significant promise for the real-time tracking of ammonia emissions in chemical manufacturing processes. In recent years, sensors utilizing metal-oxide semiconductor (MOS) nanomaterials have become a popular area of research due to their high sensitivity, strong stability, and acceptable response and recovery times. However, the interface circuits of existing MOS gas sensors mainly focus on sensor configuration and data acquisition. These interface circuits lack the functions of array timing control and data processing; gas detection and identification cannot be realized directly by them, which reduces the system integration and increases the application complexity. This paper begins by examining key design strategies for MOS-based resistive sensors aimed at enhancing ammonia sensing capabilities, offering researchers a foundation for their work in creating high-performance ammonia sensors. Based on this, a complete measuring system and a programmable interface circuit for an MOS gas sensor are introduced, which can integrate sensor configuration, signal acquisition, data processing, and output of recognition results. Finally, the current challenges and future opportunities of MOS-based resistive ammonia sensor systems are presented. The purpose of this review is to offer researchers suggestions for creating high-performance MOS-based resistive ammonia sensor systems and to promote the use of these sensors in upcoming chemical manufacturing processes.
Intrinsic Regularity Analysis and Optimization Control of Current Stress and RMS for Dual-Active Bridge Converter
Authors: Zhang Laiyong , Tu Chunming , Xiao Fan , Liu Bei , Li Peiqiang
Published: December 2024

Currently, current stress optimization control and current effective value optimization control have become the mainstream methods for real-time optimization control of dual-active bridge converters. However, there is still a lack of systematic research on the internal optimization regularities between the two and their respective internal optimization regularities, as well as problems such as the complex derivation of the effective value optimization control variable function. To address these issues, this paper analyzes the optimization results of stress optimization, effective value optimization, and double-objective entropy weight method optimization, and it explores the regularities and connections between the optimization variables of three optimizations in different power segments in different local optimal modes. Based on these regularities, the trigonometric function polar coordinate method is innovatively employed to derive the function expression of the RMS optimization control variable. Firstly, the four local optimal modes obtained from existing research are optimized by double-objective optimization. According to the weight settings, the optimization results of stress optimization, effective value optimization, and double-objective entropy weight method optimization are obtained. Subsequently, the optimization results are analyzed and compared, and the regularities between the optimization variables of these four local modes in different power segments under three optimizations are derived. Then, based on these regularities, the function expressions of the minimum current stress and the minimum effective value, as well as the function expressions of the optimized control variables, are derived by using the trigonometric function polar coordinate method. Finally, by comparing the current stress values and effective values of these four local optimal modes in the full power range, the global optimal mode and global optimal control variables in this range are selected, thereby achieving real-time global optimal control under power or voltage fluctuations. Simulations and experiments confirm the correctness of the theoretical analysis and the effectiveness of the optimization control strategy.
Robustness Against Data Integrity Attacks in Decentralized Federated Load Forecasting
Authors: Shabbir Attia , Manzoor Habib Ullah , Manzoor Muhmmand Naisr , Hussain Sajjad , Zoha Ahmed
Published: December 2024

This study examines the impact of data integrity attacks on Federated Learning (FL) for load forecasting in smart grid systems, where privacy-sensitive data require robust management. While FL provides a privacy-preserving approach to distributed model training, it remains susceptible to attacks like data poisoning, which can impair model performance. We compare Centralized Federated Learning (CFL) and Decentralized Federated Learning (DFL), using line, ring and bus topologies, under adversarial conditions. Employing a three-layer Artificial Neural Network (ANN) with substation-level datasets (APEhourly,PJMEhourly, and COMEDhourly), we evaluate the system’s resilience in the absence of anomaly detection. Results indicate that DFL significantly outperforms CFL in attack resistance, achieving Mean Absolute Percentage Errors (MAPEs) of 0.48%, 4.29% and 0.702% across datasets, compared to the CFL MAPEs of 6.07%, 18.49% and 10.19%. This demonstrates the potential of DFL as a resilient, secure solution for load forecasting in smart grids, minimizing dependence on anomaly detection to maintain data integrity.
A Study of Device Parameters Affecting the Current Error Rate in a Low-Temperature Polycrystalline Silicon Thin-Film Transistor Pixel Circuit for Active-Matrix Organic Light-Emitting Diode Display Applications
Authors: Moon Kook Chul , Jeon Jae-Hong , Park KeeChan
Published: December 2024

In active-matrix organic light-emitting diode (AMOLED) displays, conventional pixel circuits that compensate for the non-uniformity of the threshold voltage (VT) of low-temperature polycrystalline silicon thin-film transistors (TFTs) can hardly compensate for variations in other TFT parameters, such as carrier mobility (μ0), subthreshold swing (SS) and the various effects of parasitic capacitance. In recent high-resolution AMOLED displays, as the current required for OLED pixel driving decreases, the current error rate (CER) caused by the non-uniform TFT parameters increases. In this study, we analyzed the influence of each TFT parameter on the CER using SPICE simulation. Based on our analysis, the origin of the increased CER can be classified into two categories: the charging capability of driving TFT and the capacitive coupling effect of the switching TFT. The SS of the driving TFT and the parasitic capacitance of the switching TFT are major factors that affect the CER in terms of the charging capability and capacitive coupling effect, respectively. Our analysis results can be summarized as follows: The SS value of the driving TFT should be high, and its variation should be small to minimize the CER. The variation in the parasitic capacitance of the switching TFT possibly occurs due to long-term bias conditions, as well as process non-uniformity. Therefore, the stability of TFT should also be confirmed for the prevention of anomalous CER caused by long-term bias stress.
Regional Dimensions of Energy Poverty in Households of the Masovian Voivodeship in Poland: Genesis, Factors, Self-Assessment
Authors: Szczygieł Oskar , Wojciechowska Adrianna , Krupin Vitaliy , Skorokhod Iryna
Published: December 2024

This study addresses methodical and empirical dimensions of energy poverty based on the case study of the Masovian Voivodeship (also referred to as Mazovia) of Poland, focusing on socioeconomic, technical, and infrastructural factors affecting the thermal comfort of households, while taking into account the local contexts. Using both objective and subjective indicators, this study analyses selected conditions and perceptions of household groups defined as “energy poor”. The representative study group surveyed during the research process includes 2000 residents of the Masovian Voivodeship of Poland. The study was conducted utilising the computer-assisted web interview (CAWI) and computer-assisted telephone interview (CATI) methods. Analysis results show significant regional differences: the prevalence of energy poverty varies significantly depending on energy costs, housing conditions, and heating sources. Results indicate that older buildings and single-family houses, especially those still using coal or wood heating, are characterised by the greatest vulnerability to energy poverty, often related to insufficient insulation and outdated heating infrastructure. Conclusions highlight the urgent need to implement targeted policy interventions, advocating for thermal efficiency programmes and support for low-income households to alleviate the wider socioeconomic and health consequences of energy poverty in the region.
The Crossroads of the Knowledge Economy and Renewable Energy: Recommendations for Poland
Authors: Okulich-Kazarin Valery , Artyukhov Artem , Skowron Łukasz , Wołowiec Tomasz
Published: December 2024

The knowledge economy is becoming a key factor in the sustainable development of various sectors, including energy. One of the central elements in the energy of the future is renewable energy, which is becoming increasingly important in the global economy, especially in the context of achieving Sustainable Development Goal 7.a (SDG 7.a). In the last decade, Poland, like many other countries, has faced energy security challenges; a strong dependence on fossil energy sources, including imported ones; and the need to modernize its energy infrastructure. The development of renewable energy sources in Poland is becoming a priority in the state energy policy, facilitated by global trends and international commitments, including participation in achieving the SDGs. The knowledge economy is based on the efficient use of intellectual resources, innovative technologies, and scientific data. This article analyzes the role of scientific publications in forming innovative solutions for the energy sector, including renewable energy. The authors used modern research methods: scientometric, bibliometric, and correlation analyses of publications in the Scopus database and a specially created prompt for the processing of an array of 1,731,987 information units and z-statistics. The authors found six hidden reasons limiting the publication activity of Polish energy scientists. These reasons led Poland to leave the top 20 leading countries in the world market for scientific products in the energy sector. As a result, the authors rejected three research hypotheses and formulated management recommendations. This study emphasizes the importance of the knowledge economy in developing sustainable energy in Poland and the world.
Analysis of Grid-Scale Photovoltaic Plants Incorporating Battery Storage with Daily Constant Setpoints
Authors: Tejero-Gómez Juan A. , Bayod-Rújula Ángel A.
Published: December 2024

A global energy transition is crucial to combat climate change, involving a shift from fossil fuels to renewable sources and low-emission technologies. Solar photovoltaic technology has grown exponentially in the last decade, establishing itself as a cost-effective and sustainable option for electricity generation. However, its large-scale integration faces challenges due to its intermittency and lack of dispatchability. This study evaluates, from an energy perspective, the case of hybrid photovoltaic (PV) plants with battery storage systems. It addresses an aspect little explored in the literature: the sizing of battery storage to maintain a steady and constant 24 h power supply, which is usually avoided due to its high cost. Although the current economic feasibility is limited, the rapidly falling price of lithium batteries suggests that this solution could be viable in the near future. Using Matlab simulations, the system’s ability to deliver a constant energy production of electricity is assessed. Energy indicators are used to identify the optimal system size under different scenarios and power setpoints. The results determine the optimal storage size to supply a constant power that covers all or a large part of the daily PV generation, achieving steady and reliable electricity production. In addition, the impact of using setpoints at different time horizons is assessed. This approach has the potential to redefine the perception of solar PV, making it a dispatchable energy source, improving its integration into the electricity grid, and supporting the transition to more sustainable and resilient energy systems.
An Intelligent SARIMAX-Based Machine Learning Framework for Long-Term Solar Irradiance Forecasting at Muscat, Oman
Authors: Baloch Mazhar , Honnurvali Mohamed Shaik , Kabbani Adnan , Jumani Touqeer Ahmed , Chauhdary Sohaib Tahir
Published: December 2024

The intermittent nature of renewable energy sources (RES) restricts their widespread applications and reliability. Nevertheless, with advancements in the field of artificial intelligence, we can predict the variations in parameters such as wind speed and solar irradiance for the short, medium and long terms. As such, this research attempts to develop a machine learning (ML)-based framework for predicting solar irradiance at Muscat, Oman. The developed framework offers a methodological way to choose an appropriate machine learning model for long-term solar irradiance forecasting using Python’s built-in libraries. The five different methods, named linear regression (LR), seasonal autoregressive integrated moving average with exogenous variables (SARIMAX), support vector regression (SVR), Prophet, k-nearest neighbors (k-NN), and long short-term memory (LSTM) network are tested for a fair comparative analysis based on some of the most widely used performance evaluation metrics, such as the mean square error (MSE), mean absolute error (MAE), and coefficient of determination (R2) score. The dataset utilized for training and testing in this research work includes 24 years of data samples (from 2000 to 2023) for solar irradiance, wind speed, humidity, and ambient temperature. Before splitting the data into training and testing, it was pre-processed to impute the missing data entries. Afterward, data scaling was conducted to standardize the data to a common scale, which ensures uniformity across the dataset. The pre-processed dataset was then split into two parts, i.e., training (from 2000 to 2019) and testing (from 2020 to 2023). The outcomes of this study revealed that the SARIMAX model, with an MSE of 0.0746, MAE of 0.2096, and an R2 score of 0.9197, performs better than other competitive models under identical datasets, training/testing ratios, and selected features.
An FPGA-Based Trigonometric Kalman Filter Approach for Improving the Measurement Quality of a Multi-Head Rotational Encoder
Authors: Janiszewski Dariusz
Published: December 2024

This article introduces an advanced theoretical approach, named the Trigonometric Kalman Filter (TKF), to enhance measurement accuracy for multi-head rotational encoders. Leveraging the processing capabilities of a Field-Programmable Gate Array (FPGA), the proposed TKF algorithm uses trigonometric functions and sophisticated signal fusion techniques to provide highly accurate real-time angle estimation with rapid response. The inclusion of the Coordinate Rotation Digital Computer (CORDIC) algorithm enables swift and efficient computation of trigonometric values, facilitating precise tracking of angular position and rotational speed. This approach represents a notable advancement in control systems, where high accuracy and minimal latency are essential for optimal performance. The paper addresses key challenges in angle measurement, particularly the signal fusion inaccuracies that often impede precision in high-demand applications. Implementing the TKF with an FPGA-based pure fixed-point method not only enhances computational efficiency but also significantly reduces latency when compared to conventional software-based solutions. This FPGA-based implementation is particularly advantageous in real-time applications where processing speed and accuracy are critical, and it demonstrates the effective integration of hardware acceleration in improving measurement fidelity. To validate the effectiveness of this approach, the TKF was rigorously tested on a precision drive control system, configured for a direct PMSM drive in an astronomical telescope mount equipped with a standard 0.5m telescope frequently used by astronomers. This real-world application highlights the TKF’s ability to meet the stringent positioning and measurement accuracy requirements characteristic of astronomical observation, a field where minute angular adjustments are critical. The FPGA-based design enables high-frequency updates, essential for managing the minor, precise adjustments required for telescope control. The study includes a comprehensive computational analysis and experimental testing on an Altera Stratix FPGA board, presenting a detailed comparison of the TKF’s performance with other known methods, including fusion techniques such as differential methods, α–β filters, and related Kalman filtering applied to one sensors. The study demonstrates that the four-head fusion configuration of the TKF outperforms traditional methods in terms of measurement accuracy and responsiveness.