bluuebunny/crossref_metadata_2025 huggingface dataset:
Acid Ceramidase Inhibitor LCL-805 Antagonizes Akt Signaling and Promotes Iron-Dependent Cell Death in Acute Myeloid Leukemia
Authors: Ung Johnson , Tan Su-Fern , Fox Todd E. , Shaw Jeremy J. P. , Taori Maansi , Horton Bethany J. , Golla Upendarrao , Sharma Arati , Szulc Zdzislaw M. , Wang Hong-Gang , Chalfant Charles E. , Cabot Myles C. , Claxton David F. , Loughran Thomas P. , Feith David J.
Published: December 2023

Acute myeloid leukemia (AML) is an aggressive hematologic malignancy requiring urgent treatment advancements. Ceramide is a cell-death-promoting signaling lipid that plays a central role in therapy-induced cell death. We previously determined that acid ceramidase (AC), a ceramide-depleting enzyme, is overexpressed in AML and promotes leukemic survival and drug resistance. The ceramidase inhibitor B-13 and next-generation lysosomal-localizing derivatives termed dimethylglycine (DMG)-B-13 prodrugs have been developed but remain untested in AML. Here, we report the in vitro anti-leukemic efficacy and mechanism of DMG-B-13 prodrug LCL-805 across AML cell lines and primary patient samples. LCL-805 inhibited AC enzymatic activity, increased total ceramides, and reduced sphingosine levels. A median EC50 value of 11.7 μM was achieved for LCL-805 in cell viability assays across 32 human AML cell lines. As a single agent tested across a panel of 71 primary AML patient samples, a median EC50 value of 15.8 μM was achieved. Exogenous ceramide supplementation with C6-ceramide nanoliposomes, which is entering phase I/II clinical trial for relapsed/refractory AML, significantly enhanced LCL-805 killing. Mechanistically, LCL-805 antagonized Akt signaling and led to iron-dependent cell death distinct from canonical ferroptosis. These findings elucidated key factors involved in LCL-805 cytotoxicity and demonstrated the potency of combining AC inhibition with exogenous ceramide.
IGF1 and Insulin Receptor Single Nucleotide Variants Associated with Response in HER2-Negative Breast Cancer Patients Treated with Neoadjuvant Chemotherapy with or without a Fasting Mimicking Diet (BOOG 2013-04 DIRECT Trial)
Authors: de Gruil Nadia , Böhringer Stefan , de Groot Stefanie , Pijl Hanno , Kroep Judith R. , Swen Jesse J.
Published: December 2023

Aim: We aimed to investigate associations between IGF1R and INSR single nucleotide variants (SNVs) and clinical response in patients with breast cancer treated with neoadjuvant chemotherapy with or without a fasting mimicking diet (FMD) from the DIRECT trial (NCT02126449), since insulin-like growth factor 1 (IGF1) and the insulin pathway are heavily involved in tumor growth and progression. Methods: Germline DNA from 113 patients was tested for 17 systematically selected candidate SNVs in IGF1R and INSR with pathological and radiological response. Results: IGF1R variants A > G (rs3743259) and G > A (rs3743258) are associated with worse pathological response compared to reference alleles p = 0.002, OR = 0.42 (95%CI: 0.24; 0.73); p = 0.0016; OR = 0.40 (95%CI: 0.23; 0.70). INSR T > C (rs1051690) may be associated with worse radiological response p = 0.02, OR = 2.92 (95%CI: 1.16; 7.36), although not significant after Bonferroni correction. Exploratory interaction analysis suggests that IGF1R SNVs rs2684787 and rs2654980 interact negatively with the FMD group regarding radiological response p = 0.036, OR = 5.13 (95%CI: 1.12; 23.63); p = 0.024, OR = 5.71 (95%CI: 1.26; 25.85). Conclusions: The IGF1R variants rs3743259 and rs3743258 are negatively associated with pathological response in this cohort, suggesting potential relevance as a predictive biomarker. Further research is needed to validate these findings and elucidate the underlying mechanisms and interaction with FMD.
Different Outcomes According to Needling Point Location Used in Sham Acupuncture for Cancer-Related Pain: A Systematic Review and Network Meta-Analysis
Authors: Lee Boram , Kwon Chan-Young , Lee Hye Won , Nielsen Arya , Wieland L. Susan , Kim Tae-Hun , Birch Stephen , Alraek Terje , Lee Myeong Soo
Published: December 2023

Numerous acupuncture studies have been conducted on cancer-related pain; however, its efficacy compared to sham acupuncture remains controversial. We confirmed whether the outcome of acupuncture differs according to the needling points of sham acupuncture for cancer-related pain. We searched 10 databases on 23 May 2023 to screen acupuncture trials using sham acupuncture or waiting list as controls for cancer-related pain. Sham acupuncture was classified into two types, depending on whether the needling was applied at the same locations as verum acupuncture (SATV) or not (SATS). A network meta-analysis (NMA) was performed on the basis of a frequentist approach to assess pain severity. Eight studies (n = 574 participants) were included in the review, seven of which (n = 527 participants) were included in the NMA. The pain severity was not significantly different between SATV and verum acupuncture, but verum acupuncture significantly improved pain severity compared to SATS. The risk of bias affecting the comparisons between the verum and sham acupuncture was generally low. Previous acupuncture trials for cancer-related pain showed differing outcomes of sham and verum acupuncture, depending on the needling points of sham acupuncture. The application of SATV cannot be considered a true placebo, which leads to an underestimation of the efficacy of verum acupuncture.
Influencing Factors on Intersegmental Identification Adequacy in Segmentectomy with Intraoperative Indocyanine Green (ICG) Intravenous Administration
Authors: Ueno Harushi , Setogawa Tomohiro , Makita Ayaka , Ohara Yuko , Imamura Yoshito , Okado Shoji , Watanabe Hiroki , Kawasumi Yuta , Kadomatsu Yuka , Kato Taketo , Nakamura Shota , Mizuno Tetsuya , Chen-Yoshikawa Toyofumi Fengshi
Published: December 2023

Accurate identification of the intersegmental plane is essential in segmentectomy, and Indocyanine Green (ICG) assists in visualizing lung segments. Various factors, including patient-related, intraoperative, and technical issues, can influence boundary delineation. This study aims to assess the rate of unsuccessful intersegmental identification and identify the contributing factors. We analyzed cases of lung segmentectomy from April 2020 to March 2023, where intraoperative ICG was intravenously administered during robot-assisted or video-assisted thoracoscopic surgery. Cases where fluorescence extended beyond expected boundaries within 30 s were classified as the “unclear boundary group”. This group was then compared to the “clear boundary group”. The study encompassed 111 cases, 104 (94%) of which were classified under the “clear boundary group” and 7 (6%) under the “unclear boundary group”. The “unclear boundary group” had a significantly lower DLCO (15.7 vs. 11.8, p = 0.03) and DLCO/VA (4.3 vs. 3.0, p = 0.01) compared to the “clear boundary group”. All cases in the “unclear boundary group” underwent lower lobe segmentectomy. ICG administration effectively outlines pulmonary segments. Challenges in segment demarcation may occur in cases with low DLCO and DLCO/VA values, particularly during lower lobe segmentectomy.
Developing Cardio-Oncology Programs in the New Era: Beyond Ventricular Dysfunction Due to Cancer Treatments
Authors: Pons-Riverola Alexandra , Morillas Herminio , Berdejo Javier , Pernas Sonia , Pomares Helena , Asiain Leyre , Garay Alberto , Fernandez-Ortega Adela , Oliveira Ana Carla , Martínez Evelyn , Jiménez-Marrero Santiago , Pina Elena , Fort Eduard , Ramos Raúl , Alcoberro Lídia , Hidalgo Encarnación , Antonio-Rebollo Maite , Alcober Laia , Enjuanes Grau Cristina , Comín-Colet Josep , Moliner Pedro
Published: December 2023

Cardiovascular disease is a common problem in cancer patients that is becoming more widely recognized. This may be a consequence of prior cardiovascular risk factors but could also be secondary to the anticancer treatments. With the goal of offering a multidisciplinary approach to guaranteeing optimal cancer therapy and the early detection of related cardiac diseases, and in light of the recent ESC Cardio-Oncology Guideline recommendations, we developed a Cardio-Oncology unit devoted to the prevention and management of these specific complications. This document brings together important aspects to consider for the development and organization of a Cardio-Oncology program through our own experience and the current evidence.
ImmunoPET Targeting Receptor Tyrosine Kinase: Clinical Applications
Authors: Linguanti Flavia , Abenavoli Elisabetta Maria , Calabretta Raffaella , Berti Valentina , Lopci Egesta
Published: December 2023

Receptor tyrosine kinases, or RTKs, are one large family of cell surface receptors involved in signal transduction, which represent an integral part of the signaling pathways. They play a crucial role in most important cellular processes, starting with the cell cycle, proliferation and differentiation, as well as cell migration, metabolism and survival. The introduction of ImmunoPET evaluating the expression of RTKs by specific monoclonal antibodies (mAbs) or antibody fragments is regarded as a promising tool for imaging treatment efficacy and developing anticancer therapeutics. Our review focuses mainly on the current clinical research regarding ImmunoPET targeting RTKs, with particular interest in the epidermal growth factor family, or HER family, and vascular endothelial-derived growth factor/receptor.
Radiomics for Differentiation of Pediatric Posterior Fossa Tumors: A Meta-Analysis and Systematic Review of the Literature
Authors: Garaba Alexandru , Ponzio Francesco , Grasso Eleonora Agata , Brinjikji Waleed , Fontanella Marco Maria , De Maria Lucio
Published: December 2023

Purpose: To better define the overall performance of the current radiomics-based models for the discrimination of pediatric posterior fossa tumors. Methods: A comprehensive literature search of the databases PubMed, Ovid MEDLINE, Ovid EMBASE, Web of Science, and Scopus was designed and conducted by an experienced librarian. We estimated overall sensitivity (SEN) and specificity (SPE). Event rates were pooled across studies using a random-effects meta-analysis, and the χ2 test was performed to assess the heterogeneity. Results: Overall SEN and SPE for differentiation between MB, PA, and EP were found to be promising, with SEN values of 93% (95% CI = 0.88–0.96), 83% (95% CI = 0.66–0.93), and 85% (95% CI = 0.71–0.93), and corresponding SPE values of 87% (95% CI = 0.82–0.90), 95% (95% CI = 0.90–0.98) and 90% (95% CI = 0.84–0.94), respectively. For MB, there is a better trend for LR classifiers, while textural features are the most used and the best performing (ACC 96%). As for PA and EP, a synergistic employment of LR and NN classifiers, accompanied by geometrical or morphological features, demonstrated superior performance (ACC 94% and 96%, respectively). Conclusions: The diagnostic performance is high, making radiomics a helpful method to discriminate these tumor types. In the forthcoming years, we expect even more precise models.
Oxygen-Bonding State and Oxygen-Reduction Reaction Mechanism of Pr0.7Ca0.3Mn1−xCoxO3−d (x = 0, 0.1, 0.2, 0.3)
Authors: Jo Kanghee , Lee Seungjae , Lee Heesoo
Published: December 2023

We investigated the effects of Co doping on Pr0.7Ca0.3MnO3−d in the perspective of an oxygen-bonding state change. In all compositions, Pr0.7Ca0.3Mn1−xCoxO3−d (PCMCx, x = 0, 0.1, 0.2, 0.3) showed an orthorhombic structure, and the lattice gradually contracted with increasing Co content. The doped Co was mostly present as 2+ and 3+, which decreased the average oxidation value of the B site and created oxygen vacancies for charge compensation. However, as the Co content increased, the proportion of Co3+ increased, and the content of oxygen vacancies gradually decreased. In addition, the ratio of adsorbed oxygen in PCMC0.1 was the highest, and the B-O covalency was enhanced. Accordingly, the electrochemical reaction of oxygen with the cathode material in PCMC0.1 could occur most easily, showing the smallest polarization resistance among the Co-doped Pr0.7Ca0.3MnO3−d. We can confirm the formation of oxygen vacancies via Co doping and the effect of B-O covalency on the oxygen-reduction reaction of Pr0.7Ca0.3MnO3−d.
The Impact of 131I-Metaiodobenzylguanidine as a Conditioning Regimen of Tandem High-Dose Chemotherapy and Autologous Stem Cell Transplantation for High-Risk Neuroblastoma
Authors: Park Hyun Jin , Choi Jung Yoon , Kim Bo Kyung , Hong Kyung Taek , Kim Hyun-Young , Kim Il Han , Cheon Gi Jeong , Cheon Jung-Eun , Park Sung-Hye , Kang Hyoung Jin
Published: December 2023

Background: The optimal conditioning regimen of tandem high-dose chemotherapy (HDC) and autologous stem cell transplantation (ASCT) for high-risk neuroblastoma (HR-NBL) has not been established. The efficacy of 131I-MIBG therapy is under exploration in newly diagnosed HR-NBL patients. Here, we compared the outcomes of tandem HDC/ASCT between the 131I-MIBG combination and non-MIBG groups. Methods: We retrospectively analyzed the clinical data of 33 HR-NBL patients who underwent tandem HDC/ASCT between 2007 and 2021 at the Seoul National University Children’s Hospital. Results: The median age at diagnosis was 3.6 years. 131I-MIBG was administered to 13 (39.4%) of the patients. Thirty patients (90.9%) received maintenance therapy after tandem HDC/ASCT, twenty-two were treated with isotretinoin ± interleukin-2, and eight received salvage chemotherapy. The five-year overall survival (OS) and event-free survival (EFS) rates of all patients were 80.4% and 69.4%, respectively. Comparing the 131I-MIBG combined group and other groups, the five-year OS rates were 82.1% and 79.7% (p = 0.655), and the five-year EFS rates were 69.2% and 69.6% (p = 0.922), respectively. Among the adverse effects of grade 3 or 4, the incidence of liver enzyme elevation was significantly higher in the non-131I-MIBG group. Conclusions: Although tandem HDC/ASCT showed promising outcomes, the 131I-MIBG combination did not improve survival rates.
The Impact of PSR™ (Plant Small RNA Technology), Tea Extract, and Its Principal Components on Mitochondrial Function and Antioxidant Properties in Skin Cells
Authors: Moreau Marielle , Naiken Tanesha , Bru Gérard , Marteau Clarisse , Canaple Laurence , Gourguillon Lorène , Leblanc Emmanuelle , Oger Elodie , Le Mestr Audrey , Mantelin Joel , Imbert Isabelle , Nizard Carine , Bulteau Anne-Laure
Published: December 2023

Objective: This study explored the impact of a black tea extract obtained through (plant small RNA) PSRTM technology, characterized by its abundance of small molecules, particularly citric acid—an antioxidant and tricarboxylic acid (TCA) cycle contributor—on mitochondrial health. The primary focus was to assess whether this extract could counteract reactive oxygen species (ROS)-induced mitochondrial alterations associated with aging, which lead to impaired mitochondrial function, reduced ATP production, and increased ROS generation. Methods: The PSRTM extraction method was employed to obtain a high content of polyphenols and small molecules, particularly citric acid. Results: In comparison with a conventional extract, the PSRTM extract demonstrated significant enhancements in aconitase activity, an ROS-sensitive enzyme in the TCA cycle, as well as basal respiration and ATP synthesis in fibroblast cells and skin biopsies. Moreover, the PSRTM extract effectively reduced ROS production by safeguarding this critical enzyme within the Krebs cycle and displayed superior capabilities in scavenging free radicals when exposed to UV-induced stress. When administered post-UV exposure, the PSRTM extract protected nuclear DNA by reducing the formation of cyclobutane pyrimidine dimers (CPDs) and promoting DNA repair mechanisms. Furthermore, the extract exhibited beneficial effects on the extracellular matrix, characterized by a reduction in matrix metalloprotease 1 (MMP1) and an increase in fibrillin 1 expression. Conclusions: These findings collectively suggest that the PSRTM extract holds promising antiaging potential, potentially functioning as a mitochondrial nutrient/protector due to its multifaceted benefits on mitochondrial function, nuclear DNA integrity, and the extracellular matrix.
Shorter Durations of Anti-HER2 Therapy for Patients with Early-Stage, HER2-Positive Breast Cancer: The Physician Perspective
Authors: Bradbury Michelle , Savard Marie-France , Vandermeer Lisa , Clemons Lucas , Pond Gregory , Hilton John , Clemons Mark , McGee Sharon
Published: December 2023

Despite evidence from clinical trials showing the efficacy of shorter durations of therapy, most HER2-positive early breast cancer (EBC) patients receive a year of anti-HER2 therapy. A survey of Canadian oncologists was conducted online, with electronic data collection, and the analysis is reported descriptively. Measures collected included current practices with respect to the duration of adjuvant anti-HER2 therapy, perspectives on data regarding shorter durations of treatment, and interest in further trials on this subject. Responses were received from 42 providers across Canada. Half (50%, 21/42) reported having never recommended 6 months of anti-HER2 therapy. The primary reason physicians consider a shorter duration is in response to treatment-related toxicities (76%, 31/41). Most participants (79%, 33/42) expressed the need for more data to determine which patients can be safely and effectively treated with shorter durations. Patient factors such as young age, initial stage, hormone receptor status, and type of neoadjuvant chemotherapy were attributed to reluctance to offer shorter durations of treatment. Many respondents (83%, 35/42) expressed interest in participating in the proposed clinical trial of 6 months of anti-HER2 therapy. In contemporary Canadian practice, 12 months of anti-HER2 therapy remains the primary practice. Future trials are required to better define the role of shorter treatment durations.
Ibrutinib Plus RCHOP versus RCHOP Only in Young Patients with Activated B-Cell-like Diffuse Large B-Cell Lymphoma (ABC-DLBCL): A Cost-Effectiveness Analysis
Authors: Rho Hayeong , Jeong Irene Joo-Hyun , Prica Anca
Published: December 2023

The standard treatment for Diffuse Large B-Cell Lymphoma (DLBCL) is rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (RCHOP). However, many patients require subsequent treatment after relapsed disease. The ABC subtype of DLBCL (ABC-DLBCL) has a worse prognosis, and the PHOENIX trial explored adding ibrutinib to RCHOP for this patient population. The trial showed favorable outcomes for younger patients, and our study aimed to inform clinical decision-making via a cost-effectiveness model to compare RCHOP with and without ibrutinib (I-RCHOP). A Markov decision analysis model was designed to compare the treatments for patients younger than 60 years with ABC-DLBCL. The model considered treatment pathways, adverse events, relapses, and death, incorporating data on salvage treatments and novel therapies. The results indicated that I-RCHOP was more cost-effective, with greater quality-adjusted life years (QALY, 15.48 years vs. 14.25 years) and an incremental cost-effectiveness ratio (ICER) of CAD 34,111.45/QALY compared to RCHOP only. Sensitivity analyses confirmed the model’s robustness. Considering the high market price for ibrutinib, I-RCHOP may be more costly. However, it is suggested as the preferred cost-effective strategy for younger patients due to its benefits in adverse events, overall survival, and quality of life. The decision analytic model provided relevant and robust results to inform clinical decision-making.
Treatment Response of Gingival Squamous-Cell Carcinoma to Palliative Intent Immunotherapy
Authors: Trehan Natalia , Debbas Angelina , Sternick Mykaihla , Johnson Jennifer , Gates James C.
Published: December 2023

The use of PD-1 immune checkpoint inhibitor medications has become a common practice in the treatment of recurrent and metastatic head and neck squamous-cell carcinomas. Success in this setting has led to the investigation of their efficacy in locally advanced cases as a part of first-line therapy. In this report, we detail the treatment response to palliative intent immunotherapy of three geriatric patients with mandibular gingival squamous-cell carcinoma who decided against surgical intervention. Patient #1 was treated with pembrolizumab, a PD-1 inhibitor, and displayed complete clinical and radiologic response of the gingival mass after three months of treatment, which is ongoing at 19 months from initiation. Patients #2 and 3 are each on treatment with single-agent pembrolizumab, with partial response of their tumors, minimal side effects, and ongoing response at 9 and 5 months of treatment, respectively. Durable clinical treatment response to palliative immunotherapy, as is evident in this report, warrants further consideration and investigation in the geriatric population. With appropriate patient selection, surgery may be avoided and allow patients to prioritize quality of life over curative intent surgery.
Dynamic Feature Extraction-Based Quadratic Discriminant Analysis for Industrial Process Fault Classification and Diagnosis
Authors: Li Hanqi , Jia Mingxing , Mao Zhizhong
Published: December 2023

This paper introduces a novel method for enhancing fault classification and diagnosis in dynamic nonlinear processes. The method focuses on dynamic feature extraction within multivariate time series data and utilizes dynamic reconstruction errors to augment the feature set. A fault classification procedure is then developed, using the weighted maximum scatter difference (WMSD) dimensionality reduction criterion and quadratic discriminant analysis (QDA) classifier. This method addresses the challenge of high-dimensional, sample-limited fault classification, offering early diagnosis capabilities for online samples with smaller amplitudes than the training set. Validation is conducted using a cold rolling mill simulation model, with performance compared to classical methods like linear discriminant analysis (LDA) and kernel Fisher discriminant analysis (KFD). The results demonstrate the superiority of the proposed method for reliable industrial process monitoring and fault diagnosis.
Ising Ladder with Four-Spin Plaquette Interaction in a Transverse Magnetic Field
Authors: Nunes Maria Eugenia S. , Lima Francisco Welington S. , Plascak Joao A.
Published: December 2023

The spin-1/2 quantum transverse Ising model, defined on a ladder structure, with nearest-neighbor and four-spin interaction on a plaquette, was studied by using exact diagonalization on finite ladders together with finite-size-scaling procedures. The quantum phase transition between the ferromagnetic and paramagnetic phases has then been obtained by extrapolating the data to the thermodynamic limit. The critical transverse field decreases as the antiferromagnetic four-spin interaction increases and reaches a multicritical point. However, the exact diagonalization approach was not able to capture the essence of the dimer phase beyond the multicritical transition.
Ideal Agent System with Triplet States: Model Parameter Identification of Agent–Field Interaction
Authors: Börner Christoph J. , Hoffmann Ingo , Stiebel John H.
Published: December 2023

On the capital market, price movements of stock corporations can be observed independent of overall market developments as a result of company-specific news, which suggests the occurrence of a sudden risk event. In recent years, numerous concepts from statistical physics have been transferred to econometrics to model these effects and other issues, e.g., in socioeconomics. Like other studies, we extend the approaches based on the “buy” and “sell” positions of agents (investors’ stance) with a third “hold” position. We develop the corresponding theory within the framework of the microcanonical and canonical ensembles for an ideal agent system and apply it to a capital market example. We thereby design a procedure to estimate the required model parameters from time series on the capital market. The aim is the appropriate modeling and the one-step-ahead assessment of the effect of a sudden risk event. From a one-step-ahead performance comparison with selected benchmark approaches, we infer that the model is well-specified and the model parameters are well determined.
Vector Approximate Message Passing Based OFDM-IM Detection for Underwater Acoustic Communications
Authors: Feng Xiao , Tian Feng , Zhou Mingzhang , Sun Haixin , Qasem Zeyad A. H.
Published: December 2023

Orthogonal frequency division multiplexing with index modulation (OFDM-IM) has great potential for the implementation of high spectral-efficiency underwater acoustic (UWA) communications. However, general receivers consisting of the optimal maximum likelihood detection suffer from high computational load, which prohibits real-time data transmissions in underwater scenarios. In this paper, we propose a detection based on a vector approximate message passing (VAMP) algorithm for UWA OFDM-IM communications. Firstly, a VAMP framework with a non-loopy factor graph for index detection is formulated. Secondly, by utilizing the sparsity inherently existing in OFDM-IM symbols, a novel shrinkage function is derived based on the minimum mean square error criterion, which guarantees better posterior estimation. To reduce the errors from estimated non-existing indices, one trick is utilized to search the elements from the look-up table with the minimal Euclidean distance for the replacement of erroneously estimated indices. Experiments verify the advantages of the proposed detector in terms of low complexity, robustness and effectiveness compared with the state-of-art benchmarks.
Scaling Exponents of Time Series Data: A Machine Learning Approach
Authors: Raubitzek Sebastian , Corpaci Luiza , Hofer Rebecca , Mallinger Kevin
Published: December 2023

In this study, we present a novel approach to estimating the Hurst exponent of time series data using a variety of machine learning algorithms. The Hurst exponent is a crucial parameter in characterizing long-range dependence in time series, and traditional methods such as Rescaled Range (R/S) analysis and Detrended Fluctuation Analysis (DFA) have been widely used for its estimation. However, these methods have certain limitations, which we sought to address by modifying the R/S approach to distinguish between fractional Lévy and fractional Brownian motion, and by demonstrating the inadequacy of DFA and similar methods for data that resembles fractional Lévy motion. This inspired us to utilize machine learning techniques to improve the estimation process. In an unprecedented step, we train various machine learning models, including LightGBM, MLP, and AdaBoost, on synthetic data generated from random walks, namely fractional Brownian motion and fractional Lévy motion, where the ground truth Hurst exponent is known. This means that we can initialize and create these stochastic processes with a scaling Hurst/scaling exponent, which is then used as the ground truth for training. Furthermore, we perform the continuous estimation of the scaling exponent directly from the time series, without resorting to the calculation of the power spectrum or other sophisticated preprocessing steps, as done in past approaches. Our experiments reveal that the machine learning-based estimators outperform traditional R/S analysis and DFA methods in estimating the Hurst exponent, particularly for data akin to fractional Lévy motion. Validating our approach on real-world financial data, we observe a divergence between the estimated Hurst/scaling exponents and results reported in the literature. Nevertheless, the confirmation provided by known ground truths reinforces the superiority of our approach in terms of accuracy. This work highlights the potential of machine learning algorithms for accurately estimating the Hurst exponent, paving new paths for time series analysis. By marrying traditional finance methods with the capabilities of machine learning, our study provides a novel contribution towards the future of time series data analysis.
Addressing Potential Conflict among University Students during Collaborative Tasks
Authors: Omodan Bunmi Isaiah , Skosana Chiggo
Published: December 2023

This study addresses potential conflicts during collaborative tasks among university undergraduate students and presents strategies to mitigate such conflicts at a university in South Africa. Drawing on the unique context of South Africa, characterised by its rich cultural diversity and historical challenges, Social Identity Theory (SIT) was used to theorise the study within a transformative paradigm, qualitative approach and participatory research design. Ten undergraduate students were sampled and interviewed. Thematic analysis was employed to make sense of the data. The study found that a lack of a culture of open dialogue and clear guidelines and expectations leads to conflict, and the study also found that promoting a culture of open dialogue and establishing clear guidelines and expectations during collaborative projects can help prevent potential conflicts among university undergraduate students during collaborative engagement. The study concludes that promoting a culture of open dialogue and fostering establishing clear guidelines and expectations provide undergraduate students with the tools to manage group conflicts.
The Role of Emotional Intelligence, Meta-Comprehension Knowledge and Oral Communication on Reading Self-Concept and Reading Comprehension
Authors: Ondé Daniel , Cabellos Beatriz , Gràcia Marta , Jiménez Virginia , Alvarado Jesús M.
Published: December 2023

Emotional Intelligence (EI) is considered a fundamental variable for a person’s adequate psychosocial adjustment. In education, its importance transcends the level of interpersonal relationships, and has been proposed as a variable that somehow influences academic performance, although there is controversy about whether its effect is direct or, rather, an intermediate variable. The present research analyses, from a sample of 327 students (52.6% female and mean age = 14.5), the relationship of EI with respect to the knowledge and management of oral communication and reading meta-comprehension strategies, which should directly affect different educational outcomes. In order to assess both the direct and indirect effects of these variables, a Partial Least Squares Structural Equation Modelling (PLS-SEM) approach has been proposed, due to its versatility and the possibility of jointly analysing reflective and formative measures. The results show that EI indirectly affects reading self-concept and reading comprehension, as it is involved in the management and handling of both effective oral communication and reading meta-comprehension strategies.
Modeling of Cross-Coupled AC–DC Charge Pump Operating in Subthreshold Region
Authors: Kotsubo Ryoma , Tanzawa Toru
Published: December 2023

This paper proposes a circuit model of a cross-coupled CMOS AC–DC charge pump (XC–CP) operating in the subthreshold region. The aim is to improve the efficiency of designing XC–CPs with a variety of specifications, e.g., input and output voltages and AC input frequency. First, it is shown that the output resistance (Ro) of XC–CP is much higher than those of CPs with single diodes (SD–CP) and ultra-low-power diodes (ULPD–CP) as charge transfer switches (CTSs). Second, the reason behind the above feature of XC–CP, identified by a simple model, is that the gate-to-source voltages of CTS MOSFETs are independent of the output voltage of the CP. Third, the high but finite Ro of XC–CP is explainable with a more accurate model that includes the dependence of the saturation current of MOSFETs operating in the subthreshold region on the drain-to-source voltage, which is a function of the output voltage of CP. The model is in good agreement with measured and simulated results of XC–, SD–, and ULPD–CPs fabricated in a 250 nm CMOS.
A Service Recommendation System Based on Dynamic User Groups and Reinforcement Learning
Authors: Zhang En , Ma Wenming , Zhang Jinkai , Xia Xuchen
Published: December 2023

Recently, advancements in machine-learning technology have enabled platforms such as short video applications and e-commerce websites to accurately predict user behavior and cater to their interests. However, the limited nature of user data may compromise the accuracy of these recommendation systems. To address personalized recommendation challenges and adapt to changes in user preferences, reinforcement-learning algorithms have been developed. These algorithms strike a balance between exploring new items and exploiting existing ones, thereby enhancing recommendation accuracy. Nevertheless, the cold-start problem and data sparsity continue to impede the development of these recommendation systems. Hence, we proposed a joint-training algorithm that combined deep reinforcement learning with dynamic user groups. The goal was to capture user preferences for precise recommendations while addressing the challenges of data sparsity and cold-start. We used embedding layers to capture representations and make decisions before the reinforcement-learning process, executing this approach cyclically. Through this method, we dynamically obtained more accurate user and item representations and provide precise recommendations. Additionally, to address data sparsity, we introduced a dynamic user grouping algorithm that collectively enhanced the recommendations using group parameters. We evaluated our model using movie-rating and e-commerce datasets. As compared to other baseline algorithms, our algorithm not only improved recommendation accuracy but also enhanced diversity by uncovering recommendations across more categories.
Bit-Weight Adjustment for Bridging Uniform and Non-Uniform Quantization to Build Efficient Image Classifiers
Authors: Zhou Xichuan , Duan Yunmo , Ding Rui , Wang Qianchuan , Wang Qi , Qin Jian , Liu Haijun
Published: December 2023

Network quantization, which strives to reduce the precision of model parameters and/or features, is one of the most efficient ways to accelerate model inference and reduce memory consumption, particularly for deep models when performing a variety of real-time vision tasks on edge platforms with constrained resources. Existing quantization approaches function well when using relatively high bit widths but suffer from a decline in accuracy at ultra-low precision. In this paper, we propose a bit-weight adjustment (BWA) module to bridge uniform and non-uniform quantization, successfully quantizing the model to ultra-low bit widths without bringing about noticeable performance degradation. Given uniformly quantized data, the BWA module adaptively transforms these data into non-uniformly quantized data by simply introducing trainable scaling factors. With the BWA module, we combine uniform and non-uniform quantization in a single network, allowing low-precision networks to benefit from both the hardware friendliness of uniform quantization and the high performance of non-uniform quantization. We optimize the proposed BWA module by directly minimizing the classification loss through end-to-end training. Numerous experiments on the ImageNet and CIFAR-10 datasets reveal that the proposed approach outperforms state-of-the-art approaches across various bit-width settings and can even produce low-precision quantized models that are competitive with their full-precision counterparts.
Imitation Learning-Based Energy Management Algorithm: Lille Catholic University Smart Grid Demonstrator Case Study
Authors: Swibki Taheni , Ben Salem Ines , Kraiem Youssef , Abbes Dhaker , El Amraoui Lilia
Published: December 2023

This paper proposes a novel energy management approach (imitation-Q-learning) based on imitation learning (IL) and reinforcement learning (RL). The proposed approach reinforces a decision-making agent based on a modified Q-learning algorithm to mimic an expert demonstration to solve a microgrid (MG) energy management problem. Those demonstrations are derived from solving a set of linear programming (LP) problems. Consequently, the imitation-Q-learning algorithm learns by interacting with the MG simulator and imitating the LP demonstrations to make decisions in real time that minimize the MG energy costs without prior knowledge of uncertainties related to photovoltaic (PV) production, load consumption, and electricity prices. A real-scale MG at the Lille Catholic University in France was used as a case study to conduct experiments. The proposed approach was compared to the expert performances, which are the LP algorithm and the conventional Q-learning algorithm in different test scenarios. It was approximately 80 times faster than conventional Q-learning and achieved the same performance as LP. In order to test the robustness of the proposed approach, a PV inverter crush and load shedding were also simulated. Preliminary results show the effectiveness of the proposed method.
Optimization of Electric Vehicles Charging Scheduling Based on Deep Reinforcement Learning: A Decentralized Approach
Authors: Azzouz Imen , Fekih Hassen Wiem
Published: December 2023

The worldwide adoption of Electric Vehicles (EVs) has embraced promising advancements toward a sustainable transportation system. However, the effective charging scheduling of EVs is not a trivial task due to the increase in the load demand in the Charging Stations (CSs) and the fluctuation of electricity prices. Moreover, other issues that raise concern among EV drivers are the long waiting time and the inability to charge the battery to the desired State of Charge (SOC). In order to alleviate the range of anxiety of users, we perform a Deep Reinforcement Learning (DRL) approach that provides the optimal charging time slots for EV based on the Photovoltaic power prices, the current EV SOC, the charging connector type, and the history of load demand profiles collected in different locations. Our implemented approach maximizes the EV profit while giving a margin of liberty to the EV drivers to select the preferred CS and the best charging time (i.e., morning, afternoon, evening, or night). The results analysis proves the effectiveness of the DRL model in minimizing the charging costs of the EV up to 60%, providing a full charging experience to the EV with a lower waiting time of less than or equal to 30 min.
Thermal Diffusivity in the Subsoil: A Case Study in the Asturias (Northern Spain)
Authors: Marcos-Robredo Germán , Castro-García María Pilar , Rey-Ronco Miguel Ángel , Alonso-Sánchez Teresa
Published: December 2023

This study presents a novel methodology for determining the apparent thermal diffusivity of subsoil in situ, employing two heat transfer models within the subsurface: one method is based on heat conduction caused by air temperature oscillations, while the other considers heat transmission via both conduction and convection due to groundwater flow. Differential equations were solved, and non-linear regression analysis was employed. This method has direct applications in various engineering and environmental domains, such as underground transmission lines, oil and gas pipelines, radioactive waste management, and geothermal systems, especially in the context of implementing horizontal geothermal collectors (HGC). The apparent thermal diffusivity value of 1.514 × 10−6 m2 s−1, within a 95% confidence interval spanning 1.512 × 10−6 m2 s−1 and 1.516 × 10−6 m2 s−1, was obtained from the section between 1.67 and 3.86 m depth in a research borehole located in Asturias, Northern Spain, using twenty-one temperature sensors. The method allowed for the calculation of the subsoil’s apparent thermal diffusivity up to a depth of 14.55 m.
Increasing the Level of Autonomy of Control of the Electric Arc Furnace by Weakening Interphase Interactions
Authors: Kozyra Jacek , Lozynskyy Andriy , Łukasik Zbigniew , Kuśmińska-Fijałkowska Aldona , Kutsyk Andriy , Kasha Lidiia
Published: December 2023

Steelmaking is one of the most energy-intensive industries, so improving control efficiency helps to reduce the energy used to produce a tonne of steel. Mutual influences between the phases of an electric arc furnace in available electrode movement control systems cause unproductive electrode movements as a reaction to the redistribution of currents among the phases of a three-phase power supply system due to changes in arc length in one of the phases. The nonlinearity of the characteristics of an electric arc furnace significantly complicates the ability to provide autonomous electrode movement control. The approach proposed in this paper, based on the formation of a matrix of mutual influences with variable coefficients, significantly improves the per-phase autonomy of the electrode movement control system. Nonlinear dependences of the mutual influence coefficients as a function of the current increment in the phase in which the disturbance occurred are obtained. Thus, it is possible to practically eliminate unproductive electrode movements in existing control systems by avoiding the traditional use of a dead zone, which reduces the control quality in the zone of small disturbances. The complex of experiments performed using the mathematical model demonstrate that the mutual influence improves the dynamic properties of the electrode movement system in certain operating modes.
Application and Challenges of Coalitional Game Theory in Power Systems for Sustainable Energy Trading Communities
Authors: Bandeiras Filipe , Gomes Álvaro , Gomes Mário , Coelho Paulo
Published: December 2023

The role of prosumers is changing as they become active and empowered members of the grid by exchanging energy. This introduces bidirectional power flow and other challenges into the existing power systems, which require new approaches capable of dealing with the increased decentralization and complexity. Such approaches rely on game-theoretic models and mechanisms to analyze strategic decisions in competitive settings. More specifically, a coalitional game can encourage participants to trade energy with one another and obtain fair and sustainable outcomes. Therefore, the contents of this work address the coalitional game for sustainable energy trading, as well as the challenges associated with its application in power systems. This is achieved by identifying literature works that successfully implemented coalitional games in energy trading and management applications while providing an overview of solution concepts and discussing their properties and contributions to sustainability. Moreover, this work also proposes conditions that peer-to-peer energy trading should satisfy to be considered sustainable. Finally, a case study is presented to demonstrate how a coalitional game and various solution concepts can be successfully implemented to ensure the benefits and stability of cooperation in power systems. The weighted Shapley value is proposed to allocate profits among communities according to their level of sustainability.
Multi-Criteria Optimization of Energy and Water Consumption in Fruit- and Vegetable-Processing Plants in Poland
Authors: Trajer Jędrzej , Winiczenko Radosław , Dróżdż Bogdan , Wojdalski Janusz , Sałat Robert
Published: December 2023

Fruit and vegetable processing comes 6th in terms of energy consumption in the agri-food industry. At the same time, 88.4% of the industry’s final energy consumption structure is thermal energy, which depends heavily on electricity consumption. In addition, fruit and vegetable processing has a significant impact on the environment due to consumption of significant amounts of water. Reducing these three indicators simultaneously would increase the efficiency of the process while improving environmental protection. This paper proposes neural models of thermal energy, electricity and water consumption for selected major fruit- and vegetable-processing plants in Poland. These models were the basis for formulating a multi-criteria optimization task. Optimization of thermal energy, electricity and water consumption was carried out using genetic algorithms. The optimization results in the sense of Pareto can be the basis for the use of sustainable technology in selected fruit- and vegetable-processing plants.
A Dynamic Incentive Mechanism for Smart Grid Data Sharing Based on Evolutionary Game Theory
Authors: Zhang Lihua , Lu Qingyu , Huang Rui , Chen Shihong , Yang Qianqian , Gu Jinguang
Published: December 2023

With the increasing popularization and application of the smart grid, the harm of the data silo issue in the smart grid is more and more prominent. Therefore, it is especially critical to promote data interoperability and sharing in the smart grid. Existing data-sharing schemes generally lack effective incentive mechanisms, and data holders are reluctant to share data due to privacy and security issues. Because of the above issues, a dynamic incentive mechanism for smart grid data sharing based on evolutionary game theory is proposed. Firstly, several basic assumptions about the evolutionary game model are given, and the evolutionary game payoff matrix is established. Then, we analyze the stabilization strategy of the evolutionary game based on the payoff matrix, and propose a dynamic incentive mechanism for smart grid data sharing based on evolutionary game theory according to the analysis results, aiming to encourage user participation in data sharing. We further write the above evolutionary game model into a smart contract that can be invoked by the two parties involved in data sharing. Finally, several factors affecting the sharing of data between two users are simulated, and the impact of different factors on the evolutionary stabilization strategy is discussed. The simulation results verify the positive or negative incentives of these parameters in the data-sharing game process, and several factors influencing the users’ data sharing are specifically analyzed. This dynamic incentive mechanism scheme for smart grid data sharing based on evolutionary game theory provides new insights into effective incentives for current smart grid data sharing.
Estimation of Forest Stock Volume Combining Airborne LiDAR Sampling Approaches with Multi-Sensor Imagery
Authors: Liu Jianyang , Quan Ying , Wang Bin , Shi Jinan , Ming Lang , Li Mingze
Published: December 2023

Timely and reliable estimation of forest stock volume is essential for sustainable forest management and conservation. Light detection and ranging (LiDAR) data can provide an effective depiction of the three-dimensional structure information of forests, but its large-scale application is hampered by spatial continuity. This study aims to construct a LiDAR sampling framework, combined with multi-sensor imagery, to estimate the regional forest stock volume of natural secondary forests in Northeast China. Two sampling approaches were compared, including systematic sampling and classification-based sampling. First, the forest stock volume was mapped using a combination of field measurement data and full-coverage LiDAR data. Then, the forest stock volume obtained in the first step of estimation was used as a reference value, and optical images and topographic features were combined for secondary modeling to compare the effectiveness and accuracy of different sampling methods, including 12 systematic sampling and classification-based sampling methods. Our results show that the root mean square error (RMSE) of the 12 systematic sampling approaches ranged from 55.81 to 57.42 m3/ha, and the BIAS ranged from 21.55 to 24.89 m3/ha. The classification-based LiDAR sampling approach outperformed systematic sampling, with an RMSE of 55.56 (<55.81 m3/ha) and a BIAS of 20.68 (<21.55 m3/ha). This study compares different LiDAR sampling approaches and explores an effective LiDAR sample collection scheme for estimating forest stock, while balancing cost and accuracy. The classification-based LiDAR sampling approach described in this study is easy to apply and portable and can provide a reference for future LiDAR sample collection.