bluuebunny/crossref_metadata_2025 huggingface dataset:
Innovative Combinations, Cellular Therapies and Bispecific Antibodies for Chronic Lymphocytic Leukemia: A Narrative Review
Authors: Visentin Andrea , Frazzetto Sara , Trentin Livio , Chiarenza Annalisa
Published: March 2024

In the last few years, several agents targeting molecules that sustain the survival and the proliferation of chronic lymphocytic leukemia (CLL) cells have become clinically available. Most of these drugs target surface proteins, such as CD19 or CD20, via monoclonal or bispecific monoclonal antibodies (BsAbs), CAR T cells, intracellular proteins like BTK by using covalent or non-covalent inhibitors or BCL2 with first or second generation BH3-mimetics. Since the management of CLL is evolving quickly, in this review we highlighted the most important innovative treatments including novel double and triple combination therapies, CAR T cells and BsAbs for CLL. Recently, a large number of studies on novel combinations and newer strategic options for CLL therapy have been published or presented at international conferences, which were summarized and linked together. Although the management of treatment with a single continuous agent is easier, the emergence of protein mutations, long-term toxicities and costs are important concerns that favor the use of a fixed duration therapy. In the future, a measurable residual disease (MRD)-guided treatment cessation and MRD-based re-initiation of targeted therapy seems to be a more feasible approach, allowing identification of the patients who might benefit from continuous therapy or who might need a consolidation with BsAbs or CAR T cells to clear the neoplastic clone.
Perioperative Chemotherapy for Gastro-Esophageal or Gastric Cancer: Anthracyclin Triplets versus FLOT
Authors: Geerts Julie F. M. , van der Zijden Charlène J. , van der Sluis Pieter C. , Spaander Manon C. W. , Nieuwenhuijzen Grard A. P. , Rosman Camiel , van Laarhoven Hanneke W. M. , Verhoeven Rob H. A. , Wijnhoven Bas P. L. , Lagarde Sjoerd M. , Mostert Bianca
Published: March 2024

Background: The FLOT4-AIO trial (2019) showed improved survival with perioperative fluorouracil, leucovorin, oxaliplatin, and docetaxel (FLOT) compared to anthracyclin triplets in gastric cancer treatment. It is unclear whether these results extend to real-world scenarios in the Netherlands. This study aimed to compare outcomes of perioperative FLOT to anthracyclin triplets in a real-world Dutch gastric cancer population. Methods: Patients diagnosed with resectable (cT2-4a/cTxN0-3/NxM0) gastric or gastro-esophageal junction carcinoma between 2015–2021 who received neoadjuvant FLOT or anthracyclin triplets were selected from the Netherlands Cancer Registry. The primary outcome was overall survival (OS), analyzed through multivariable Cox regression. Secondary outcomes included pathological complete response (pCR), neoadjuvant chemotherapy cycle completion, surgical resection rates, and adjuvant therapy. Results: Adjusted OS showed no significant survival benefit (HR = 0.88, 95% CI 0.77–1.01, p = 0.07), even though the median OS was numerically improved by 8 months with FLOT compared to anthracyclin triplets (48.1 vs. 39.9 months, p = 0.16). FLOT patients were more likely to undergo diagnostic staging laparoscopies (74.2% vs. 44.1%, p < 0.001), had higher rates of completing neoadjuvant chemotherapy (OR = 1.35, 95% CI 1.09–1.68, p = 0.007), receiving adjuvant therapy (OR = 1.34, 95% CI 1.08–1.66, p = 0.08), and achieving pCR (OR = 1.52, 95% CI 1.05–2.20, p = 0.03). No significant differences were observed in (radical) resection rates. Conclusion(s): Real-world data showed no significant OS improvement for FLOT-treated patients compared to anthracyclin triplets, despite more staging laparoscopies. However, FLOT patients demonstrated higher rates of neoadjuvant therapy completion, proceeding to adjuvant therapy, and increased pCR rates. Therefore, we recommend the continued use of neoadjuvant FLOT therapy in the current clinical setting.
The Role of Natural Killer Cells in the Tumor Immune Microenvironment of EBV-Associated Nasopharyngeal Carcinoma
Authors: Li Shuzhan , Dai Wei , Kam Ngar-Woon , Zhang Jiali , Lee Victor H. F. , Ren Xiubao , Kwong Dora Lai-Wan
Published: March 2024

Endemic nasopharyngeal carcinoma (NPC) is closely associated with the Epstein–Barr virus (EBV), which contributes to tumor development and influences the tumor immune microenvironment (TIME) in NPC. Natural killer (NK) cells, as part of the innate immune system, play a crucial role in responding to viral infections and malignant cell transformations. Notably, NK cells possess a unique ability to target tumor cells independent of major histocompatibility complex class I (MHC I) expression. This means that MHC I-deficient tumor cells, which can escape from effective T cell attack, are susceptible to NK-cell-mediated killing. The activation of NK cells is determined by the signals generated through inhibitory and activating receptors expressed on their surface. Understanding the role of NK cells in the complex TIME of EBV+ NPC is of utmost importance. In this review, we provide a comprehensive summary of the current understanding of NK cells in NPC, focusing on their subpopulations, interactions, and cytotoxicity within the TIME. Moreover, we discuss the potential translational therapeutic applications of NK cells in NPC. This review aims to enhance our knowledge of the role of NK cells in NPC and provide valuable insights for future investigations.
Detection and Classification of Hysteroscopic Images Using Deep Learning
Authors: Raimondo Diego , Raffone Antonio , Salucci Paolo , Raimondo Ivano , Capobianco Giampiero , Galatolo Federico Andrea , Cimino Mario Giovanni Cosimo Antonio , Travaglino Antonio , Maletta Manuela , Ferla Stefano , Virgilio Agnese , Neola Daniele , Casadio Paolo , Seracchioli Renato
Published: March 2024

Background: Although hysteroscopy with endometrial biopsy is the gold standard in the diagnosis of endometrial pathology, the gynecologist experience is crucial for a correct diagnosis. Deep learning (DL), as an artificial intelligence method, might help to overcome this limitation. Unfortunately, only preliminary findings are available, with the absence of studies evaluating the performance of DL models in identifying intrauterine lesions and the possible aid related to the inclusion of clinical factors in the model. Aim: To develop a DL model as an automated tool for detecting and classifying endometrial pathologies from hysteroscopic images. Methods: A monocentric observational retrospective cohort study was performed by reviewing clinical records, electronic databases, and stored videos of hysteroscopies from consecutive patients with pathologically confirmed intrauterine lesions at our Center from January 2021 to May 2021. Retrieved hysteroscopic images were used to build a DL model for the classification and identification of intracavitary uterine lesions with or without the aid of clinical factors. Study outcomes were DL model diagnostic metrics in the classification and identification of intracavitary uterine lesions with and without the aid of clinical factors. Results: We reviewed 1500 images from 266 patients: 186 patients had benign focal lesions, 25 benign diffuse lesions, and 55 preneoplastic/neoplastic lesions. For both the classification and identification tasks, the best performance was achieved with the aid of clinical factors, with an overall precision of 80.11%, recall of 80.11%, specificity of 90.06%, F1 score of 80.11%, and accuracy of 86.74 for the classification task, and overall detection of 85.82%, precision of 93.12%, recall of 91.63%, and an F1 score of 92.37% for the identification task. Conclusion: Our DL model achieved a low diagnostic performance in the detection and classification of intracavitary uterine lesions from hysteroscopic images. Although the best diagnostic performance was obtained with the aid of clinical data, such an improvement was slight.
Clinical Validation of the Vitro HPV Screening Assay for Its Use in Primary Cervical Cancer Screening
Authors: Bellosillo Beatriz , Ibáñez Raquel , Roura Esther , Monfil Laura , Asensio-Puig Laura , Álvarez Isabel , Muset Mercè , Florencia Yolanda , Paytubi Sonia , de Andrés-Pablo Álvaro , Calvo Susana , Serrano-Munné Laia , Pavón Miguel Ángel , Lloveras Belen
Published: March 2024

Many scientific societies have issued guidelines to introduce population-based cervical cancer screening with HPV testing. The Vitro HPV Screening assay is a fully automatic multiplex real-time PCR test targeting the L1 GP5+/GP6+ region of HPV genome. The assay detects 14 high risk (HR) HPV genotypes, identifying individual HPV16 and HPV18 genotypes, and the HPV-positive samples for the other 12 HR HPV types are subsequently genotyped with the HPV Direct Flow Chip test. Following international guidelines, the aim of this study was to validate the clinical accuracy of the Vitro HPV Screening test on ThinPrep-collected samples for its use as primary cervical cancer screening, using as comparator the validated cobas® 4800 HPV test. The non-inferiority analysis showed that the clinical sensitivity and specificity of the Vitro HPV Screening assay for a diagnosis of cervical intraepithelial neoplasia of grade 2 or worse (CIN2+) were not inferior to those of cobas® 4800 HPV (p = 0.0049 and p < 0.001 respectively). The assay has demonstrated a high intra- and inter-laboratory reproducibility, also among the individual genotypes. The Vitro HPV Screening assay is valid for cervical cancer screening and it provides genotyping information on HPV-positive samples without further sample processing in a fully automated workflow.
Prevention Science Can’t Wait: An Interview with Dr. Diana H. Fishbein
Authors: Logan Alan C. , Fishbein Diana H.
Published: March 2024

In an ongoing series of interviews, Challenges Advisory Board member and Nova Institute for Health Fellow Alan C. Logan meets with thought leaders, scientists, scholars, healthcare professionals, artisans, and visionaries concerned about health at the scale of persons, places, and the planet. Here, Dr. Diana H. Fishbein responds to a set of questions posed by Challenges. For over forty years, Dr. Fishbein, a neuroscientist and criminologist by training, has been at the forefront of research examining the intersections of biological, environmental, social, and physical factors as they relate to brain development, functioning, risky behavior, and life outcomes. Within this broad-ranging career, Dr. Fishbein was among the very first to conduct a dietary intervention study (eliminating refined carbohydrate foods) examining behavioral outcomes (i.e., nutritional psychiatry). This, combined with related research endeavors and experiences, led to a wider-lens view of prevention research, a desire to understand the physiological mechanisms that explain heterogeneity in positive and/or unfavorable outcomes in prevention programs, and a dynamic career devoted to the science of prevention. Here, Dr. Fishbein reflects on her career and its many twists and turns through a range of interdisciplinary work. Shediscusses prevention science through the lens of future possibilities and the need for scientists to lean toward advocacy and supporting evidence-based policy changes. Prevention science, as Dr. Fishbein explains, is at the heart of the many interconnected challenges of our time.
Blocking Microglial Proliferation by CSF-1R Inhibitor Does Not Alter the Neuroprotective Effects of Adoptive Regulatory T Cells in 3xTg Alzheimer’s Disease Mice
Authors: Park Seon-Young , Cha Nari , Kim Soyoung , Chae Songah , Lee Won-jun , Jung Hyunjae , Bae Hyunsu
Published: March 2024

Alzheimer’s disease (AD) is a chronic neurodegenerative disease that causes cognitive impairment. Neuroinflammation induced by activated microglia exacerbates AD. Regulatory T cells (Tregs) play roles in limiting neuroinflammation by converting microglial polarization. Therefore, adoptive Treg therapy is considered an attractive option for neurodegenerative disorders. However, the mechanism underlying Treg therapy via microglial modulation is not fully understood. In this study, we sought to determine whether adoptively transferred Tregs were effective when microglia proliferation was inhibited by using GW2580, which is an inhibitor of CSF1R. We found that inhibition of microglial proliferation during Treg transfer did not alter the therapeutic effects of Tregs on cognitive deficits and the accumulation of Aβ and pTAU in 3xTg-AD mice. The expression of pro- and anti-inflammatory markers in the hippocampus of 3xTg mice showed that GW2580 did not affect the inhibition of neuroinflammation by Treg transfer. Additionally, adoptively transferred Tregs were commonly detected in the brain on day 7 after transfer and their levels decreased slowly over 100 days. Our findings suggest that adoptively transferred Tregs can survive longer than 100 days in the brain, suppressing microglial activation and thus alleviating AD pathology. The present study provides valuable evidence to support the prolonged efficacy of adoptive Treg therapy in AD.
A Review of Concrete Carbonation Depth Evaluation Models
Authors: Wang Xinhao , Yang Qiuwei , Peng Xi , Qin Fengjiang
Published: March 2024

Carbonation is one of the critical issues affecting the durability of reinforced concrete. Evaluating the depth of concrete carbonation is of great significance for ensuring the quality and safety of construction projects. In recent years, various prediction algorithms have been developed for evaluating concrete carbonation depth. This article provides a detailed overview of the existing prediction models for concrete carbonation depth. According to the data processing methods used in the model, the existing prediction models can be divided into mathematical curve models and machine learning models. The machine learning models can be further divided into the following categories: artificial neural network model, decision tree model, support vector machine model, and combined models. The basic idea of the mathematical curve model is to directly establish the relationship between the carbonation depth and age of concrete by using certain function curves. The advantage of the mathematical curve model is that only a small amount of experimental data is needed for curve fitting, which is very convenient for engineering applications. The limitation of the curve model is that it can only consider the influence of some factors on the carbonation depth of concrete, and the prediction accuracy cannot be guaranteed. The advantage of using the machine learning model to predict the carbonation depth of concrete is that many factors can be considered at the same time. When there are sufficient experimental data, the trained machine learning model can give more accurate prediction results than the mathematical curve model. The main defect of the machine learning model is that it needs a lot of experimental data as training samples, so it is not as convenient as the mathematical curve model in engineering applications. A future research direction may be to combine a machine learning model with a mathematical curve model to evaluate the carbonation depth of concrete more accurately.
Redescription and First Nucleotide Sequences of Opecoeloides pedicathedrae (Digenea: Opecoelidae), a Parasite of Cynoscion leiarchus (Cuvier, 1830) (Eupercaria: Sciaenidae) from Brazil
Authors: Cárdenas Melissa Querido , Cohen Simone Chinicz , Oliveira Amanda Gleyce Lima de , Justo Marcia Cristina Nascimento , Santos Cláudia Portes
Published: March 2024

Opecoeloides Odhner, 1928, is represented by 19 valid species found in marine fish, of which five have been reported in Brazil. Specimens of Opecoeloides pedicathedrae Travassos, Freitas & Bührnheim, 1966, were collected from the intestine of smooth weakfish Cynoscion leiarchus, a new host record, from off the coast of Rio de Janeiro, Brazil. They were examined using light and confocal laser microscopy. New partial sequences of 18S and 28S rDNA genes of O. pedicathedrae were obtained. Bayesian inference analysis on the partial 28S rDNA dataset resulted in a phylogram in which O. pedicathedrae formed a well-supported clade with Opecoeloides fimbriatus and Opecoeloides furcatus. The K2p distance between O. pedicathedrae and O. fimbriatus was 0.34%, with 3 divergent nucleotides; and between O. pedicathedrae and O. furcatus was 4.18%, with 38 divergent nucleotides. A Bayesian-inference phylogenetic tree based on the 18S rDNA recovered two main clades with five subfamilies. A clade of Opecoelinae showed that O. pedicathedrae was closer to Pseudopecoeloides tenuis; the K2p distance between these species was 2.14%, with 28 divergent nucleotides. The new nucleotide sequences presented inclusion of a phylogenetic analysis that can help to clarify the understanding of this complex taxon.
The Millepora Zone Is Back: Recent Findings from the Northernmost Region of the Maldives
Authors: Pancrazi Irene , Ahmed Hassan , Chimienti Giovanni , Montefalcone Monica
Published: March 2024

Three species of the tropical hydrocoral genus Millepora were common and abundant in the Maldives before 1998, characterizing extensive shallow reef areas known as ‘Millepora zones’. The 1998 heat wave resulted in mass mortality of all the Millepora species, the characteristic Millepora zones disappeared, and only a few observations of isolated colonies were reported in the years ahead. A recent expedition (January 2024) to the northernmost region of the Maldives (Ihavandhippolhu Atoll) revealed new Millepora zones at 7–13 m depth, suggesting a potential repopulation of a vulnerable genus considered regionally extinct.
Why Do Nasolabial Folds Appear? Exploring the Anatomical Perspectives and the Role of Thread-Based Interventions
Authors: Hong Gi-Woong , Song Sehyun , Park Soo Yeon , Lee Sang-Bong , Wan Jovian , Hu Kyung-Seok , Yi Kyu-Ho
Published: March 2024

The classification of nasolabial folds into three types, each with distinct causative factors and mechanisms, is explored. Age-related changes in facial skin and connective tissues are examined in detail, revealing variations across different facial regions due to variances in tissue firmness and thickness. The innovative ‘Reverse Technique,’ involving cog threads to enhance tissue traction and effectiveness in thread-lifting procedures, is introduced. Detailed technical guidelines, anatomical considerations, and safety measures are provided, emphasizing the importance of identifying optimal vectors and fixing points to achieve maximum lifting effects while minimizing potential risks, particularly those associated with vascular structures. Additionally, the ‘Cross Technique using volumizing thread’ is discussed, designed to smooth tissue boundaries and rejuvenate sagging areas. Facial anatomy, including the positioning of arteries and ligaments, is underscored as essential for ensuring the safety and efficacy of procedures. In conclusion, this review stands as a comprehensive guide for practitioners, offering insights into innovative thread-lifting methods and their applications in addressing nasolabial folds. The primary focus is on achieving optimal aesthetic results while prioritizing patient safety.
Deceased Kidney Donor Biomarkers: Relationship between Delayed Kidney Function and Graft Function Three Years after Transplantation
Authors: Maslauskiene Rima , Vaiciuniene Ruta , Tretjakovs Peteris , Gersone Gita , Radzeviciene Aurelija , Bura Andrejus , Stankevicius Edgaras , Bumblyte Inga Arune
Published: March 2024

With an increasing number of marginal donors, additional methods for the evaluation of cadaveric kidney quality are required. This study aimed to evaluate pretransplant deceased donor serum (s) and urine (u) biomarkers, including neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), interleukin-18, and C-X-C motif chemokine 10 (CXCL10) for predicting early and late graft function. In total, 43 deceased kidney donors and 76 corresponding recipients were enrolled. Delayed graft function (DGF) occurred in 27.6% of cases. sIL-18, sKIM-1, uNGAL, and uKIM-1 were predictors of DGF. A model incorporating sIL-18, uKIM-1, and clinical factors was developed to predict DGF (AUROC 0.863). Univariate analysis showed a negative association between uKIM and graft eGFR at 6, 12, 24, and 36 months, but this was not confirmed in the multivariate analysis. In conclusion, we report a superior performance of donor biomarkers for predicting DGF and later graft function over serum creatinine. Higher levels of donor sIL-18 and uKIM in conjunction with expanded-criteria donors and longer cold ischemia times predicted DGF. With no renal tubular damage in zero-time donor biopsies, higher pretransplant urine and serum NGAL levels were associated with better allograft function one year after transplantation, and sNGAL with graft function three years after transplantation.
Entropic Bounds on the Average Length of Codes with a Space
Authors: Bruno Roberto , Vaccaro Ugo
Published: March 2024

We consider the problem of constructing prefix-free codes in which a designated symbol, a space, can only appear at the end of codewords. We provide a linear-time algorithm to construct almost-optimal codes with this property, meaning that their average length differs from the minimum possible by at most one. We obtain our results by uncovering a relation between our class of codes and the class of one-to-one codes. Additionally, we derive upper and lower bounds to the average length of optimal prefix-free codes with a space in terms of the source entropy.
Revisiting the Surface Brightness Profile of the Stellar Disk with the Statistical Mechanics of the Self-Gravitating System with the Central Body
Authors: Kang Dong-Biao
Published: March 2024

We have explored the exponential surface brightness profile (SBP) of stellar disks, a topic extensively discussed by many authors yet seldom integrated with the study of correlations between black holes, bulges, and entire disks. Building upon our prior work in the statistical mechanics of disk-shaped systems and aligning with methodologies from other research, we analyze the influence of the central body. This analysis reveals analytical relationships among black holes, bulges, and the entire stellar disk. Additionally, we incorporate a specific angular momentum distribution (SAMD) that aligns more closely with observational data, showing that for the self-gravitating disk, with the same surface density, a reduction in its spin results in only a slight decrease in its radius, whereas with the same SAMD, an increment in its spin significantly limits its extent. A key feature of our model is its prediction that the surface density profile of an isolated disk will invariably exhibit downbending at a sufficient distance, a hypothesis that future observations can test. Our refined equations provide a notably improved fit for SBPs, particularly in the central regions of stellar disks. While our findings underscore the significance of statistical mechanics in comprehending spiral galaxy structures, they also highlight areas in our approach that warrant further discussion and exploration.
Centering Educators’ Voices in the Development of Professional Learning for Data-Rich, Place-Based Science Instruction
Authors: Wong Nicole , Elsayed Rasha , Nilsen Katy , Perez Leticia R. , Daehler Kirsten R.
Published: March 2024

This self-reflective case study describes our project team’s efforts to promote equity in science professional learning (PL) by centering the voices of educators in the PL design process and within the course itself. We believe that educators’ experiences, priorities, and expertise are essential to developing professional learning that meets the needs of teachers and their students. We have a particular interest in amplifying the voices of those in historically underrepresented communities. Toward that end, we engaged science educators who work with Indigenous students and recent immigrants as collaborators in developing PL to support data-rich, place-based Earth Science instruction. In this case study, we share and critique the practices and tools that we have employed to center educator voices, rather than those of the PL designers and researchers. Our strategies for developing more equitable science professional learning include the use of: (a) equity-focused research methods, such as asset-based needs-sensing questions and peer-to-peer interviews; (b) a humanistic stance toward data-rich science learning, which emphasizes the typically unnamed sociocultural inputs and outputs that permeate all aspects of data; (c) a participatory design process that centers educators’ voices; and (d) a model of professional learning that uses representations of educator and student experiences as objects for reflection.
Roles and Effect of Digital Technology on Young Children’s STEM Education: A Scoping Review of Empirical Studies
Authors: Hu Xinyun , Fang Yuan , Liang Yutong
Published: March 2024

Digital technology is increasingly used in STEM education for young children aged 0–8 years. An extensive literature search was conducted using seven databases to systematically investigate the effect of digital technology on young children’s STEM education. Twenty-two eligible articles published from 2010 to 2021 were identified. Results showed that robotics, programming, and multimedia were used to support young children’s STEM education. Digital technology plays different roles in the process of STEM education. Outcomes also showed that digital technology positively affected young children’s STEM education in terms of STEM knowledge or skill acquisition and learning engagement. This was regardless of gender but relevant to age and the learning condition. Participating children and teachers reported high acceptance and satisfaction with the included programs. However, many difficulties, challenges and criticisms were revealed by the extracted data, including how digital technology is used in young children’s STEM education, the nature of young children, the requirements placed upon educators, and different types of adult–child interactions. We also look at the limitations of the study design within included studies and provide recommendations accordingly.
H5P-Based Matching Game for Training Graphs of Internal Forces in Structural Analysis
Authors: De Santos-Berbel César , Hernando García José Ignacio , Vázquez-Greciano Andrea
Published: March 2024

The teaching of structural analysis is essential in the training of undergraduate students who will be qualified in structural calculations. The use of games in learning can motivate students and improve their performances in evaluations. To this end, H5P-based matching games have been adopted in Moodle for a structural analysis course as an optional assignment to train graphs of internal forces (GIFs). Although the students knew that participating in the games would positively impact their grades, they were not informed of the exact number of extra points they would receive based on their performances. The engagement, motivation and performance of the students were analyzed using various statistics. Furthermore, the effectiveness of the game in facilitating knowledge acquisition was evaluated by comparing the students’ performances in the games to their performances in the GIF exercises during face-to-face examinations. The study found that the students who participated in the games exhibited high levels of motivation and engagement. In addition, the results indicate that the participants had a moderately improved understanding of GIFs when taking the course examinations.
Against Jamming Attack in Wireless Communication Networks: A Reinforcement Learning Approach
Authors: Ma Ding , Wang Yang , Wu Sai
Published: March 2024

When wireless communication networks encounter jamming attacks, they experience spectrum resource occupation and data communication failures. In order to address this issue, an anti-jamming algorithm based on distributed multi-agent reinforcement learning is proposed. Each terminal observes the spectrum state of the environment and takes it as an input. The algorithm then employs Q-learning, along with the primary and backup channel allocation rules, to finalize the selection of the communication channel. The proposed algorithm designs primary and backup channel allocation rules for sweep jamming and smart jamming strategies. It can predict the behavior of jammers while reducing decision conflicts among terminals. The simulation results demonstrate that, in comparison to existing methods, the proposed algorithm not only enhances data transmission success rates across multiple scenarios but also exhibits superior operational efficiency when confronted with jamming attacks. Overall, the anti-jamming performance of the proposed algorithm outperforms the comparison methods.
A Formal Model for Reliable Data Acquisition and Control in Legacy Critical Infrastructures
Authors: Blanco José Miguel , Del Alamo Jose M. , Dueñas Juan C. , Cuadrado Felix
Published: March 2024

The digital transformation of critical infrastructures, such as energy or water distribution systems, is essential for their smart management. Faster issue identification and smoother services enable better adaptation to consumers’ evolving demands. However, these large-scale infrastructures are often outdated. Their digital transformation is crucial to enable them to support societies. This process must be carefully planned, providing guidance that ensures that the data they rely on are dependable and that the system remains fully operational during the transition. This paper presents a formal model that supports reliable data acquisition in legacy critical infrastructures, facilitating their evolution towards a data-driven smart system. Our model provides the foundation for a flexible transformation process while generating dependable data for system management. We demonstrate the model’s applicability in a use case within the water distribution domain and discuss its benefits.
GAN-Based Data Augmentation with Vehicle Color Changes to Train a Vehicle Detection CNN
Authors: Ayub Aroona , Kim HyungWon
Published: March 2024

Object detection is a challenging task that requires a lot of labeled data to train convolutional neural networks (CNNs) that can achieve human-level accuracy. However, such data are not easy to obtain, as they involve significant manual work and costs to annotate the objects in images. Researchers have used traditional data augmentation techniques to increase the amount of training data available to them. A recent trend in object detection is to use generative models to automatically create annotated data that can enrich a training set and improve the performance of the target model. This paper presents a method of training the proposed ColorGAN network, which is used to generate augmented data for the target domain of interest with the least compromise in quality. We demonstrate a method to train a GAN with images of vehicles in different colors. Then, we demonstrate that our ColorGAN can change the color of vehicles of any given vehicle dataset to a set of specified colors, which can serve as an augmented training dataset. Our experimental results show that the augmented dataset generated by the proposed method helps enhance the detection performance of a CNN for applications where the original training data are limited. Our experiments also show that the model can achieve a higher mAP of 76% when the model is trained with augmented images along with the original training dataset.
Anomaly Detection in Connected and Autonomous Vehicle Trajectories Using LSTM Autoencoder and Gaussian Mixture Model
Authors: Wang Boyu , Li Wan , Khattak Zulqarnain H.
Published: March 2024

Connected and Autonomous Vehicles (CAVs) technology has the potential to transform the transportation system. Although these new technologies have many advantages, the implementation raises significant concerns regarding safety, security, and privacy. Anomalies in sensor data caused by errors or cyberattacks can cause severe accidents. To address the issue, this study proposed an innovative anomaly detection algorithm, namely the LSTM Autoencoder with Gaussian Mixture Model (LAGMM). This model supports anomalous CAV trajectory detection in the real-time leveraging communication capabilities of CAV sensors. The LSTM Autoencoder is applied to generate low-rank representations and reconstruct errors for each input data point, while the Gaussian Mixture Model (GMM) is employed for its strength in density estimation. The proposed model was jointly optimized for the LSTM Autoencoder and GMM simultaneously. The study utilizes realistic CAV data from a platooning experiment conducted for Cooperative Automated Research Mobility Applications (CARMAs). The experiment findings indicate that the proposed LAGMM approach enhances detection accuracy by 3% and precision by 6.4% compared to the existing state-of-the-art methods, suggesting a significant improvement in the field.
Digital Twin Technology-Based Networking Solution in Low Earth Orbit Satellite Constellations
Authors: He Ci , Zhang Yasheng , Ke Jia , Yao Mingwu , Chen Chen
Published: March 2024

Digital twin technology provides a reliable paradigm to address the high trial-and-error costs and limited perception capabilities in satellite networking. However, the dynamic constellation topology and real-time twin applications remain significant challenges in satellite network design. This paper proposes a network topology simulation approach that dynamically analyzes the inter-satellite topology based on pre-calculated ephemeris and orbital information. Furthermore, the paper introduces a digital twin algorithm based on network virtualization, cloud platform management, and software-defined networking to validate and analyze the twin requirements at different stages. Finally, a low Earth orbit (LEO) constellation twin validation environment is constructed to verify the networking protocols at various stages. The experimental results demonstrate the performance of the proposed twin systems at different stages.
Evaluation of Energy Performance Indicators and Energy Saving Opportunities for the Italian Rubber Manufacturing Industry
Authors: Piccioni Matteo , Martini Fabrizio , Martini Chiara , Toro Claudia
Published: March 2024

The objective of this work is the energy characterisation and evaluation of the energy efficiency potential of the rubber manufacturing industry in Italy, exploiting the detailed data included in energy audits by large and energy-intensive companies. This sector is divided into two sub-activities: the manufacture of rubber products and the production of tyres. Existing studies are focused mainly on tyre production, and there is a lack of quantitative evaluation of energy indicators that can provide guidance for improving process efficiency. In this work, updated global and specific energy performance indicators (EnPIs) related to the production process and to the auxiliary and general services are defined and evaluated. At the same time, targeted actions and interventions to improve the energy efficiency of the sector are analysed, showing the role of different intervention areas and their cost-effectiveness. The analysis is based on 100 Italian mandatory energy audits of the sector collected according to Art.8 EU Directive 27/2012. The applied methodology made it possible to calculate specific energy performance indicators by considering the overall and sub-process energy consumption of different production sites. Based on a detailed database containing real data from recent energy audits, this study provides an up-to-date and reliable benchmark for the rubber industry sector. In addition, the analysis of energy audits allows the identification of the most effective energy efficiency interventions for the rubber industry in terms of cost-effectiveness and payback time.
Energy Transformation Development Strategies: Evaluation of Asset Conversion in the Regions
Authors: Svazas Mantas , Navickas Valentinas
Published: March 2024

Manifestations of energy transformation are visible throughout the developed world. As the threat to the survival of humanity arises, the countries of the world are starting to take faster and more specific actions to transform the energy sector. One of the energy transformation strategies is the decentralized development of the energy system in the regions. This concept is especially relevant at this time, when centralized sources of energy production and supply are becoming the target of physical and cyber attacks. The purpose of this article is to form theoretical assumptions for the smooth implementation of the decentralization of the energy system. This article aims to remove obstacles to short-term energy transformation. The novelty of this article is related to emphasizing the role of biomass cogeneration in achieving energy system efficiency and greening. Mathematical modeling based on RSM is used in the article. The established factors of the market structure revealed that the efficiency of energy production is based on the use of cogeneration and the markets for raw materials and energy can be attributed to different types. The results of this study showed that the optimal combination of biomass cogeneration can ensure competitive energy production. This article is relevant because it offers transitional solutions until adequate hydrogen utilization and energy storage solutions are developed.
Making Room for Silicon: Including SiOx in a Graphite-Based Anode Formulation and Harmonization in 1 Ah Cells
Authors: Landa-Medrano Imanol , Urdampilleta Idoia , Castrillo Iker , Grande Hans-Jürgen , de Meatza Iratxe , Eguia-Barrio Aitor
Published: March 2024

Transitioning to more ambitious electrode formulations facilitates developing high-energy density cells, potentially fulfilling the demands of electric car manufacturers. In this context, the partial replacement of the prevailing anode active material in lithium-ion cells, graphite, with silicon-based materials enhances its capacity. Nevertheless, this requires adapting the rest of the components and harmonizing the electrode integration in the cell to enhance the performance of the resulting high-capacity anodes. Herein, starting from a replacement in the standard graphite anode recipe with 22% silicon suboxide at laboratory scale, the weight fraction of the electrochemically inactive materials was optimized to 2% carbon black/1% dispersant/3% binder combination before deriving an advantage from including single-wall carbon nanotubes in the formulation. In the second part, the recipe was upscaled to a semi-industrial electrode coating and cell assembly line. Then, 1 Ah lithium-ion pouch cells were filled and tested with different commercial electrolytes, aiming at studying the dependency of the Si-based electrodes on the additives included in the composition. Among all the electrolytes employed, the EL2 excelled in terms of capacity retention, obtaining a 48% increase in the number of cycles compared to the baseline electrolyte formulation above the threshold capacity retention value (80% state of health).
Photovoltaic Solar Power Prediction Using iPSO-Based Data Clustering and AdaLSTM Network
Authors: Liu Jincun , Li Kangji , Xue Wenping
Published: March 2024

Due to the increasing integration of photovoltaic (PV) solar power into power systems, the prediction of PV solar power output plays an important role in power system planning and management. This study combines an optimized data clustering method with a serially integrated AdaLSTM network to improve the accuracy and robustness of PV solar power prediction. During the data clustering process, the Euclidean distance-based clustering centroids are optimized by an improved particle swarm optimization (iPSO) algorithm. For each obtained data cluster, the AdaLSTM network is utilized for model training, in which multiple LSTMs are serially combined together through the AdaBoost algorithm. For PV power prediction tasks, the inputs of the testing set are classified into the nearest data cluster by the K-nearest neighbor (KNN) method, and then the corresponding AdaLSTM network of this cluster is used to perform the prediction. Case studies from two real PV stations are used for prediction performance evaluation. Results based on three prediction horizons (10, 30 and 60 min) demonstrate that the proposed model combining the optimized data clustering and AdaLSTM has higher prediction accuracy and robustness than other comparison models. The root mean square error (RMSE) of the proposed model is reduced, respectively, by 75.22%, 73.80%, 67.60%, 66.30%, and 64.85% compared with persistence, BPNN, CNN, LSTM, and AdaLSTM without clustering (Case A, 30 min prediction). Even compared with the model combining the K-means clustering and AdaLSTM, the RMSE can be reduced by 10.75%.
Data-Driven Techniques for Short-Term Electricity Price Forecasting through Novel Deep Learning Approaches with Attention Mechanisms
Authors: Laitsos Vasileios , Vontzos Georgios , Bargiotas Dimitrios , Daskalopulu Aspassia , Tsoukalas Lefteri H.
Published: March 2024

The electricity market is constantly evolving, being driven by factors such as market liberalization, the increasing use of renewable energy sources (RESs), and various economic and political influences. These dynamics make it challenging to predict wholesale electricity prices. Accurate short-term forecasting is crucial to maintaining system balance and addressing anomalies such as negative prices and deviations from predictions. This paper investigates short-term electricity price forecasting using historical time series data and employs advanced deep learning algorithms. First, four deep learning models are implemented and proposed, which are a convolutional neural network (CNN) with an integrated attention mechanism, a hybrid CNN followed by a gated recurrent unit model (CNN-GRU) with an attention mechanism, and two ensemble learning models, which are a soft voting ensemble and a stacking ensemble model. Also, the optimized version of a transformer model, the Multi-Head Attention model, is introduced. Finally, the perceptron model is used as a benchmark for comparison. Our results show excellent prediction accuracy, particularly in the hybrid CNN-GRU model with attention, thereby achieving a mean absolute percentage error (MAPE) of 6.333%. The soft voting ensemble model and the Multi-Head Attention model also performed well, with MAPEs of 6.125% and 6.889%, respectively. These findings are significant, as previous studies have not shown high performance with transformer models and attention mechanisms. The presented results offer promising insights for future research in this field.
A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant
Authors: Famoso Fabio , Oliveri Ludovica Maria , Brusca Sebastian , Chiacchio Ferdinando
Published: March 2024

This paper presents a novel approach to estimating short-term production of wind farms, which are made up of numerous turbine generators. It harnesses the power of big data through a blend of data-driven and model-based methods. Specifically, it combines an Artificial Neural Network (ANN) for immediate future predictions of wind turbine power output with a stochastic model for dependability, using Hybrid Reliability Block Diagrams. A thorough state-of-the-art review has been conducted in order to demonstrate the applicability of an ANN for non-linear stochastic problems of energy or power forecast estimation. The study leverages an innovative cluster analysis to group wind turbines and reduce the computational effort of the ANN, with a dependability model that improves the accuracy of the data-driven output estimation. Therefore, the main novelty is the employment of a hybrid model that combines an ANN with a dependability stochastic model that accounts for the realistic operational scenarios of wind turbines, including their susceptibility to random shutdowns This approach marks a significant advancement in the field, introducing a methodology which can aid the design and the power production forecast. The research has been applied to a case study of a 24 MW wind farm located in the south of Italy, characterized by 28 turbines. The findings demonstrate that the integrated model significantly enhances short-term wind-energy production estimation, achieving a 480% improvement in accuracy over the solo-clustering approach.
A Direct Chemical Approach to Mitigate Environment Lead Contamination in Perovskite Solar Cells
Authors: Liu Benjamin , Jia Zihan , Chen Zhiliang
Published: March 2024

Current guidelines indicate that the lead levels in perovskite solar cells are sufficiently low, putting them on par with the safety of other lead-containing electronics. Yet, there remains ambiguity regarding the exact environmental impact of lead derived from perovskite. When this lead enters the soil, it has the potential to permeate plants and, subsequently, our food supply, at a rate that is a staggering ten times more than other lead contaminants from human-induced activities. Given this, it becomes vital to ensure that lead does not pollute our environment as we further adopt these technologies. In this study, we propose a novel method using polymer net bones to anchor the lead, which effectively reduces the risk of lead leaching due to rainfall. Perovskite Solar Cells (PSCs) integrated with this polymer net bone show improved operational efficiency and hold significant promise in curtailing lead leakage, reinforcing the ecological integrity of perovskite solutions. When enhanced with Polyvinyl Alcohol (PVA), these PSCs register a notable increase in Power Conversion Efficiency (PCE), scoring 24.7% as opposed to the 22.3% in PSCs devoid of PVA. Additionally, PVA-augmented PSCs outperform in stability when compared to their traditional counterparts.
Modelling Distribution of an Endangered Longhorn Beetle, Callipogon relictus (Coleoptera: Cerambycidae), in Northeast Asia
Authors: Kuprin Alexander , Shevchenko Nicolaj , Baklanova Vladislava
Published: March 2024

Based on our own field data and using modeling of modern potential ranges (in the MaxEnt program), an assessment of the spatial distribution of the rare representative of the longhorn beetle family, Callipogon (Eoxenus) relictus Semenov 1899, was conducted in northeast Asia (220 geographic locations). The generated maps of the potential range of C. relictus demonstrate a high likelihood of the species’ presence in the upland areas of southern far east Russia, the provinces of northeastern China, and the Korean Peninsula. Field data also indicate the extensive association of the species with undisturbed broadleaf and coniferous–broadleaf forests in northeast Asia. Maps of the potential distribution of C. relictus in northeast Asia have been compiled based on four climate change scenarios from the present time to 2070. Under all of the climate scenarios used, it is shown that suitable habitats for the species will persist in certain areas of Primorsky Krai, as well as neighboring provinces of the People’s Republic of China and a small enclave on the Korean Peninsula in Gangwon-do province. Significant reduction in suitable conditions for the rare longhorn beetle will occur in the rest of its distribution range.