bluuebunny/crossref_metadata_2025 huggingface dataset:
Construction of ZnCdS Quantum-Dot-Modified CeO2 (0D–2D) Heterojunction for Enhancing Photocatalytic CO2 Reduction and Mechanism Insight
Authors: Yan Junzhi , Sun Yuming , Cai Junxi , Cai Ming , Hu Bo , Yan Yan , Zhang Yue , Tang Xu
Published: September 2024

It is important to improve the separation ability of photogenerated electrons and the adsorption capacity of carbon dioxide (CO2) for efficient photoreduction of CO2. Here, we synthesized ZnCdS quantum dots (ZCS-QDs) and cerium dioxide nanosheets (CeO2) using the solvothermal method and calcination method. We combined CeO2 and ZCS-QDs to effectively enhance the charge separation efficiency, and the lifetime of photogenerated electrons was increased 4.5 times. The CO evolution rate of the optimized composite (ZCS-QDs/CeO2) was up to 495.8 μmol g−1 h−1, and it had 100% product selectivity. In addition, the stability remained high after five cycles. The CO2 adsorption capacity of the catalyst surface was observed by in situ FTIR. The test results showed that improving CO2 capture ability and promoting photogenic electron separation had positive effects on enhancing photoreduction of CO2. This study provides a reference for constructing a zero-dimensional–two-dimensional (0D–2D) heterojunction and explores potential CO2 reduction reaction mechanisms.
Reduction of p-Nitrophenol with Modified Coal Fly Ash Supported by Palladium Catalysts
Authors: Zhang Hao , Zhou Kaicheng , Ye Tao , Xu Huajun , Xie Man , Sun Pengfei , Dong Xiaoping
Published: September 2024

The compound p-Nitrophenol (p-NP) is widely recognized as a highly toxic nitro-aromatic substance that urgently requires emission control. Reducing p-NP to p-aminophenol (p-AP) not only decreases its toxicity and mineralization properties in nature but also provides a key raw material for the chemical and pharmaceutical industries. The study used coal fly ash (CFA) as a catalyst carrier for synthesizing the p-NP reduction catalyst. Using CFA as an alternative option not only reduces costs but also achieves the objective of treating waste with waste compared to utilizing commercial solid materials for synthesizing catalysts. By employing hydrochloric acid and sodium hydroxide pretreatment methods, the physicochemical properties of CFA are significantly improved, enhancing the dispersion of palladium (Pd) nanoparticles. The structural features of the prepared samples were characterized using various surface analysis techniques, and both intermittent and continuous modes were experimentally tested for the model catalytic reaction involving the sodium borohydride (NaBH4)-mediated reduction of p-NP. The results demonstrate that CFA has potential in wastewater treatment.
Iodine-Mediated One-Pot Synthesis of Imidazo[1,5-a]Pyridines
Authors: Huang Lang , Liu Yarong , Guan Zhipeng , Dong Zhibing
Published: September 2024

In this report, we developed an efficient one-pot method for the synthesis of 3-phenyl-1-(phenylthio)imidazo[1,5-a]pyridine analogs starting from 2-aminomethylpyridines, benzaldehydes, and sodium benzenesulfinates, which constructed C-N and C-S bonds simultaneously. The method features mild reaction conditions, a wide range of substrates, high atom utilization, and convenient and easily accessible starting materials.
The Proteasome and Cul3-Dependent Protein Ubiquitination Is Required for Gli Protein-Mediated Activation of Gene Expression in the Hedgehog Pathway
Authors: Uśpieński Tomasz , Niewiadomski Paweł
Published: September 2024

Many cellular processes are regulated by proteasome-mediated protein degradation, including regulation of signaling pathways and gene expression. Among the pathways regulated by the ubiquitin–proteasome system is the Hedgehog pathway and its downstream effectors, the Gli transcription factors. Here we provide evidence that proteasomal activity is necessary for maintaining the activation of the Hedgehog pathway, and this crucial event takes place at the level of Gli proteins. We undertook extensive work to demonstrate the specificity of the observed phenomenon by ruling out the involvement of primary cilium, impaired nuclear import, failed dissociation from Sufu, microtubule stabilization, and stabilization of Gli repressor forms. Moreover, we showed that proteasomal-inhibition-mediated Hedgehog pathway downregulation is not restricted to the NIH-3T3 cell line. We demonstrated, using CRISPR/Ca9 mutagenesis, that neither Gli1, Gli2, nor Gli3 are solely responsible for the Hedgehog pathway downregulation upon proteasome inhibitor treatment, and that Cul3 KO renders the same phenotype. Finally, we report two novel E3 ubiquitin ligases, Btbd9 and Kctd3, known Cul3 interactors, as positive Hedgehog pathway regulators. Our data pave the way for a better understanding of the regulation of gene expression and the Hedgehog signaling pathway.
D4Z4 Hypomethylation in Human Germ Cells
Authors: Potabattula Ramya , Durackova Jana , Kießling Sarah , Michler Alina , Hahn Thomas , Schorsch Martin , Trapphoff Tom , Dieterle Stefan , Haaf Thomas
Published: September 2024

Expression of the double homeobox 4 (DUX4) transcription factor is highly regulated in early embryogenesis and is subsequently epigenetically silenced. Ectopic expression of DUX4 due to hypomethylation of the D4Z4 repeat array on permissive chromosome 4q35 alleles is associated with facioscapulohumeral muscular dystrophy (FSHD). In peripheral blood samples from 188 healthy individuals, D4Z4 methylation was highly variable, ranging from 19% to 76%, and was not affected by age. In 48 FSHD2 patients, D4Z4 methylation varied from 3% to 30%. Given that DUX4 is one of the earliest transcribed genes after fertilization, the D4Z4 array is expected to be unmethylated in mature germ cells. Deep bisulfite sequencing of 188 mainly normozoospermic sperm samples revealed an average methylation of 2.5% (range 0.3–22%). Overall, the vast majority (78%) of individual sperm cells displayed no methylation at all. In contrast, only 19 (17.5%) of 109 individual germinal vesicle (GV) oocytes displayed D4Z4 methylation <2.5%. However, it is not unexpected that immature GV oocytes which are not usable for assisted reproduction are endowed with D4Z4 (up to 74%) hypermethylation and/or abnormal (PEG3 and GTL2) imprints. Although not significant, it is interesting to note that the pregnancy rate after assisted reproduction was higher for donors of sperm samples and oocytes with <2.5% methylation.
Correction: Nally et al. The Effectiveness of School-Based Interventions on Obesity-Related Behaviours in Primary School Children: A Systematic Review and Meta-Analysis of Randomised Controlled Trials. Children 2021, 8, 489
Authors: Nally Sarah , Carlin Angela , Blackburn Nicole E. , Baird Judith S. , Salmon Jo , Murphy Marie H. , Gallagher Alison M.
Published: September 2024

We acknowledge an error in the BMI data in our publication [...]
Weight Status Determines the Impact of a School-Based Nutrition Education Intervention on Lifestyle Behaviors in Children
Authors: Miguel-Berges María L. , Larruy-García Alicia , De Miguel-Etayo Pilar , Jimeno-Martinez Andrea , Torres Antonio , Moreno Luis A.
Published: September 2024

Background/Objectives: This study investigated the impact of the FLUYE school-based intervention on children’s lifestyle behaviors, Mediterranean diet adherence, and emotional well-being in Spain. The objective was to promote healthy habits through nutrition education, physical activity, and emotional support within the school environment, with a focus on addressing the needs of both normal-weight and overweight/obese children. Methods: A total of 552 children aged 3 to 12 years participated in the study, with data collected at baseline (T0) and follow-up (T1). The intervention was designed to integrate health education into the school curriculum, emphasizing the development of personal competencies in diet, physical activity, and emotional well-being. The analysis included changes in dietary habits, screen time, physical activity, and psychosocial outcomes, with comparisons made between the normal-weight and overweight/obese groups. Results: Significant improvements were observed in water consumption and reductions in sugar-sweetened beverage intake across both weight groups. However, an increase in screen time, particularly among normal-weight children, highlighted ongoing challenges in reducing sedentary behavior. Adherence to the Mediterranean diet improved more significantly in the overweight/obese group, suggesting the program’s effectiveness in promoting healthier eating patterns among at-risk children. Emotional well-being and self-esteem also saw significant enhancements, with children reporting increased feelings of pride and positivity post-intervention. Conclusions: The FLUYE program effectively improved various aspects of children’s lifestyle behaviors, particularly in dietary habits and emotional well-being. These findings underscore the importance of comprehensive, school-based interventions that address both physical and psychosocial aspects of health, especially for children at higher risk for obesity.
Assessment of Influential Operational Parameters in the Mitigation of CO2 Emissions in a Power Plant: Case Study in Portugal
Authors: Balanuta Vítor , Baptista Patrícia , Carreira Fernando , Duarte Gonçalo O. , Casaca Cláudia S.S.L.
Published: September 2024

The European decarbonization goals and requirement for energy independence are mostly relying on intermittent renewable energy sources for electrification. A numerical model was developed to simulate the operation of a steam generator, allowing a study of the potential impacts of retrofitting existing coal-fired power plants to operate with biomass or coal–biomass mixtures on combustion parameters and CO2 emissions. The results obtained using the operational parameters of the Sines power plant indicate that a mixture of 25% coal and 75% pine sawdust allow operation at λ = 1.8, demonstrating that a small amount of coal allows operation near the coal combustion parameters (λ = 1.9). These conditions have the drawback of a reduction of 8.7% in adiabatic flame temperature but a significant reduction of 57.5% in CO2 emissions, considering the biomass as carbon-neutral.
Assessing the Vulnerability of Farming Households on the Caribbean Island of Hispaniola to Climate Change
Authors: Duvil Jacky , Feuillet Thierry , Emmanuel Evens , Paul Bénédique
Published: September 2024

This article assesses the individual vulnerability of 550 farming households, 430 in Haiti and 120 in the Dominican Republic, on the Caribbean island of Hispaniola to the impacts of climate change. This assessment is based on an integrated approach, using socio-economic and biophysical variables. The variables collected for each farm household were grouped into three categories: adaptive capacity, sensitivity, and exposure. Multiple correspondence analysis (MCA) was used to develop a vulnerability index for each farm household, enabling them to be classified according to their level of vulnerability to the impacts of climate change. A logistic regression model was then used to identify the main factors influencing their vulnerability. The results revealed that on the island of Hispaniola, 33.91%, 32.09%, and 34% of farming households were classified as very vulnerable, vulnerable, and less vulnerable. In Haiti, these proportions were 36.74%, 36.51%, and 26.75%, while in the Dominican Republic, they were 20%, 20%, and 60%. Agricultural households with highly accessible credit (OR = 0.16, p < 0.001) and university education (OR = 0.05, p < 0.001) were relatively less vulnerable to climate change impacts compared to their counterparts.
Sol-Gel SiO2 Coatings with Curcumin and Thymol on 3D Printouts Manufactured from Ti6Al4V ELI
Authors: Słabęcka Natalia , Czajkowska Karolina , Łataś Paulina , Śniarowski Patryk , Ciosek Laura , Jastrzębski Krzysztof , Pietrzyk Bożena
Published: September 2024

Bacterial biofilm on implants may cause inflammation, which disturbs the process of the implant’s integration with the surrounding tissues. Such problems are becoming critical for patients’ health, especially in connection with the presence of antibiotic-resistant bacterial strains. Among the existing alternatives for drug treatments are natural-based substances. This study focused on the examination of silica coatings with curcumin and thymol, which were deposited using the sol-gel method on 3D printouts made of Ti6Al4V ELI. This substrate material is commonly used in medicine. The selective laser melting technique used for the manufacturing of samples was in line with the existing procedures applied for individual orthopedic implants. The examination involved the assessment of the coatings’ morphology, chemical composition, and biological effect. The antibacterial properties were tested using a flow cytometer using Escherichia coli, and the cytotoxicity on Saos-2 cells was assessed using the LIVE/DEAD test. The obtained results showed that it is possible to produce silica sol-gel coatings with the addition of specific natural substances in concentrations assuring a bacteriostatic effect. The produced coatings did not show any cytotoxic effect, which confirms the possibility of using both curcumin and thymol as additives to coatings used in medicine, e.g., for orthopedic implants.
A Survey of Blockchain Applicability, Challenges, and Key Threats
Authors: Morar Catalin Daniel , Popescu Daniela Elena
Published: September 2024

With its decentralized, immutable, and consensus-based validation features, blockchain technology has grown from early financial applications to a variety of different sectors. This paper aims to outline various applications of the blockchain, and systematically identify general challenges and key threats regarding its adoption. The challenges are organized into even broader groups, to allow a clear overview and identification of interconnected issues. Potential solutions are introduced into the discussion, addressing their possible ways of mitigating these challenges and their forward-looking effects in fostering the adoption of blockchain technology. The paper also highlights some potential directions for future research that may overcome these challenges to unlock further applications. More generally, the article attempts to describe the potential transformational implications of blockchain technology, through the manner in which it may contribute to the advancement of a diversity of industries.
Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions
Authors: Pham Tuan D. , Teh Muy-Teck , Chatzopoulou Domniki , Holmes Simon , Coulthard Paul
Published: September 2024

Artificial intelligence (AI) is revolutionizing head and neck cancer (HNC) care by providing innovative tools that enhance diagnostic accuracy and personalize treatment strategies. This review highlights the advancements in AI technologies, including deep learning and natural language processing, and their applications in HNC. The integration of AI with imaging techniques, genomics, and electronic health records is explored, emphasizing its role in early detection, biomarker discovery, and treatment planning. Despite noticeable progress, challenges such as data quality, algorithmic bias, and the need for interdisciplinary collaboration remain. Emerging innovations like explainable AI, AI-powered robotics, and real-time monitoring systems are poised to further advance the field. Addressing these challenges and fostering collaboration among AI experts, clinicians, and researchers is crucial for developing equitable and effective AI applications. The future of AI in HNC holds significant promise, offering potential breakthroughs in diagnostics, personalized therapies, and improved patient outcomes.
Internal Consistency and Floor/Ceiling Effects of the Gross Motor Function Measure for Use with Children Affected by Cancer: A Cross-Sectional Study
Authors: Rossi Francesca , Valle Monica , Galeoto Giovanni , Tofani Marco , Berchialla Paola , Sciannameo Veronica , Bertin Daniele , Calcagno Annalisa , Casalaz Roberto , Cerboneschi Margherita , Cervo Marta , Cornelli Annalisa , Di Pede Chiara , Esposito Maria , Ferrarese Miriana , Imazio Paola , Lorenzon Maria , Longo Lucia , Martinuzzi Andrea , Naretto Gabriella , Orsini Nicoletta , Panzeri Daniele , Pellegrini Chiara , Peranzoni Michela , Picone Fabiola , Rabusin Marco , Ricci Federica , Zigrino Claudia , Zucchetti Giulia , Fagioli Franca
Published: September 2024

Children/adolescents with cancer can develop adverse effects impacting gross motor function. There is a lack of gross motor function assessment tools that have been validated for this population. The aim of this multicenter cross-sectional study was to preliminary validate the 88-item Gross Motor Function Measure (GMFM-88) for use in children/adolescents with cancer, exploring internal consistency and floor/ceiling effect. Inclusion criteria regarded children/adolescents diagnosed with cancer on treatment or <1 year off therapy. The internal consistency was assessed using Cronbach’s α, and the floor–ceiling effects were calculated through percentage. This study involved 217 participants with heterogeneous neoplasm conditions. Internal consistency was good, with a Cronbach’s α of 0.989. Floor–ceiling effect analysis reveals that several items obtained a dichotomous scoring distribution in each of the five sub-scales of the GMFM-88. This can be explained by the heterogeneous clinical characteristics of the target population. The preliminary validation of GMFM-88 in a group of children/adolescents affected by cancer suggests that some items are not able to discriminate between different gross motor function levels, and therefore it does not represent an informative tool to measure gross motor function in children with cancer. Future research is needed to define which ones could be more useful for clinical practice.
Revealing the Causes of Dyslexia through a Differential Diagnosis, a Short-Term Effective Treatment and an Appropriate Conceptual Framework
Authors: Werth Reinhard
Published: September 2024

Various different impairments and their interactions can cause reading problems referred to as “dyslexia”. Since reading requires the interaction of many abilities, the impairment of each of these abilities can result in dyslexia. Therefore, the diagnosis must differentiate various kinds of dyslexia. The diagnosis of a certain kind of dyslexia cannot be delimited to the investigation and description of symptoms but must also include the investigation of the causes of each kind of dyslexia. For this purpose, a scientifically unequivocal concept of causation and appropriate methods are needed to distinguish them from co-existing impairments that have no causal influence on reading performance. The results of applying these methods cannot be adequately accounted for by a non-scientific, intuitive understanding of necessary and sufficient conditions and causation. The methods suitable for revealing the causes of dyslexia are described in detail, and the results of applying these methods in experiments, in which 356 children with developmental dyslexia participated, are reviewed. Since the concepts of “necessary” and “sufficient” conditions and “causation” proposed in the philosophy of science are not suitable for describing causes of dyslexia and their interaction, they are replaced by a more detailed, experimentally based conceptual framework that provides an accurate description of the conditions required for correct reading and the causes of dyslexia.
White Light Diffraction Phase Microscopy in Imaging of Breast and Colon Tissues
Authors: Smarandache Adriana , Pirvulescu Ruxandra A. , Andrei Ionut-Relu , Dinache Andra , Romanitan Mihaela Oana , Branisteanu Daniel Constantin , Zemba Mihail , Anton Nicoleta , Pascu Mihail-Lucian , Nastasa Viorel
Published: September 2024

This paper reports results obtained using white light diffraction phase microscopy (wDPM) on captured images of breast and colon tissue samples, marking a contribution to the advancement in biomedical imaging. Unlike conventional brightfield microscopy, wDPM offers the capability to capture intricate details of biological specimens with enhanced clarity and precision. It combines high resolution, enhanced contrast, and quantitative capabilities with non-invasive, label-free imaging. These features make it a useful tool for tissue imaging, providing detailed and accurate insights into tissue structure and dynamics without compromising the integrity of the samples. Our findings underscore the potential of quantitative phase imaging in histopathology, in the context of automating the process of tissue analysis and diagnosis. Of particular note are the insights gained from the reconstructed phase images, which provide physical data regarding peripheral glandular cell membranes. These observations serve to focus attention on pathologies involving the basal membrane, such as early invasive carcinoma. Through our analysis, we aim to contribute to catalyzing further advancements in tissue (breast and colon) imaging.
Lower Late Development Rate of Acute Respiratory Distress Syndrome in Patients with Lower Mechanical Power or Driving Pressure
Authors: Lee Ya-Chi , Liu Pi-Hua , Lin Shih-Wei , Yu Chung-Chieh , Chu Chien-Ming , Wu Huang-Pin
Published: September 2024

For patients on ventilation without acute respiratory distress syndrome (ARDS), there are, as yet, limited data on ventilation strategies. We hypothesized that driving pressure (DP) and mechanical power (MP) may play key roles for the late development of ARDS in patients without initial ARDS. A post hoc analysis of a database from our previous cohort was performed. The mean DP/MP was computed from the data before ARDS development or until ventilator support was discontinued within 28 days. The association between DP/MP and late development of ARDS within 28 days was determined. One hundred and twelve patients were enrolled, among whom seven developed ARDS. Univariate Cox regression showed that congestive heart failure (CHF) history and higher levels of mean MP and DP were associated with ARDS development. Multivariate models revealed that the mean MP and mean DP were still factors independently associated with ARDS development at hazard ratios of 1.177 and 1.226 after adjusting for the CHF effect. Areas under the receiver operating characteristic curves for mean DP/MP in predicting ARDS development were 0.813 and 0.759, respectively. In conclusion, high mean DP and MP values may be key factors associated with late ARDS development. The mean DP had a better predicted value for the development of ARDS than the mean MP.
A Narrative Review of Burnout Syndrome in Medical Personnel
Authors: Ungur Andreea-Petra , Bârsan Maria , Socaciu Andreea-Iulia , Râjnoveanu Armand Gabriel , Ionuț Răzvan , Goia Letiția , Procopciuc Lucia Maria
Published: September 2024

Burnout among healthcare workers has been extensively studied since its initial recognition in 1960, with its defining characteristics established by Maslach in 1982. The syndrome, characterized by emotional exhaustion, depersonalization, and low personal accomplishment, is exacerbated by work-related stress and has profound implications for individual and societal well-being. Methods: A review of the literature, including PubMed searches and analyses of risk factors and protective measures, was conducted to assess the prevalence, impacts, and biomarkers associated with burnout among healthcare workers. Various instruments for evaluating burnout were examined, including the widely used Maslach Burnout Inventory, alongside specific tools tailored to different occupational populations. Results: Healthcare workers, particularly physicians, exhibit significantly higher rates of burnout compared to the general population. Factors such as night shifts, workload, and exposure to biohazards contribute to elevated burnout risk. Biomarkers like cortisol, melatonin, and thyroid hormones have been linked to burnout, highlighting physiological implications. Conclusions: Burnout poses significant challenges to healthcare systems globally, impacting patient care, worker retention, and overall well-being. Identifying and addressing risk factors while promoting protective factors such as resilience and social support are crucial in mitigating burnout. Further research into prevention strategies and biomarker monitoring is warranted to support the mental and physical health of healthcare workers.
Assessment of Periodontitis Risk Factors in Endodontically Treated Teeth: A Cross-Sectional Study
Authors: Sălceanu Mihaela , Dascălu Cristina , Melian Anca , Giuroiu Cristian , Antohi Cristina , Concita Corina , Hamburda Tudor , Topoliceanu Claudiu , Mârţu Maria-Alexandra
Published: September 2024

The aim of the study was to collect data about the prevalence and risk factors of apical periodontitis in a population of endodontically treated patients. The study group included 151 patients (52 males, 99 females; mean age 48.36 ± 15.708 yrs.) with 391 endodontically treated teeth (mean follow-up of 5.25 ± 1.759 yrs.). According to the initial tooth diagnosis, root-filled teeth were divided into Group A, root-filled teeth treated for pulpitis or for the purpose of prosthetic pulpectomies (vital pulp group), and Group B, root-filled teeth with non-vital pulp (necrotic pulp). Clinical and radiographic evaluation of the root and its periapical area were performed to establish the success/failure of endodontic therapy, the quality of the root canal fillings (length, density, taper), and coronal restoration. The presence of recurrent caries, periodontal pathology, or endo-periodontal lesions were also recorded. Univariate and multivariate analyses were used to determine the risk factors for apical periodontitis and calculate their odds ratios (ORs). For the root-filled vital pulp tooth group, the highest risks for apical periodontitis are associated with inadequate homogeneity (OR 30.938), periodontitis (OR 9.226), and over-filling (OR 8.800). For the root-filled non-vital pulp tooth group, the highest risks are associated with periodontitis (OR 4.235) and age over 60 yrs. (OR 4.875). For the necrotic pulp tooth group, multivariate analysis identified an age > 60 yrs., filled molars, intracanal posts, poor coronal restoration quality, under-filling, and periodontitis as significant combined risk factors. Inadequate root canal filling and periodontitis in both groups were risk factors associated with most cases of apical periodontitis. Other risk factors include age > 60 yrs., poor coronal restoration quality, and the presence of intracanal posts in root-filled teeth with necrotic pulp.
Interpretable Machine Learning Models for Predicting Critical Outcomes in Patients with Suspected Urinary Tract Infection with Positive Urine Culture
Authors: Yen Chieh-Ching , Ma Cheng-Yu , Tsai Yi-Chun
Published: September 2024

(1) Background: Urinary tract infection (UTI) is a leading cause of emergency department visits and hospital admissions. Despite many studies identifying UTI-related risk factors for bacteremia or sepsis, a significant gap remains in developing predictive models for in-hospital mortality or the necessity for emergent intensive care unit admission in the emergency department. This study aimed to construct interpretable machine learning models capable of identifying patients at high risk for critical outcomes. (2) Methods: This was a retrospective study of adult patients with urinary tract infection (UTI), extracted from the Medical Information Mart for Intensive Care IV Emergency Department (MIMIC-IV-ED) database. The critical outcome is defined as either in-hospital mortality or transfer to an intensive care unit within 12 h. ED visits were randomly partitioned into a 70%/30% split for training and validation. The extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM) algorithms were constructed using variables selected from the stepwise logistic regression model. The XGBoost model was then compared to the traditional model and clinical decision rules (CDRs) on the validation data using the area under the curve (AUC). (3) Results: There were 3622 visits among 3235 unique patients diagnosed with UTI. Of the 2535 patients in the training group, 836 (33%) experienced critical outcomes, and of the 1087 patients in the validation group, 358 (32.9%) did. The AUCs for different machine learning models were as follows: XGBoost, 0.833; RF, 0.814; and SVM, 0.799. The XGBoost model performed better than others. (4) Conclusions: Machine learning models outperformed existing traditional CDRs for predicting critical outcomes of ED patients with UTI. Future research should prospectively evaluate the effectiveness of this approach and integrate it into clinical practice.
Long-Term Outcomes of Anterior Cruciate Ligament Reconstruction Based on Gait Analysis
Authors: Skvortsov Dmitry , Altukhova Alyona , Kaurkin Sergey , Akhpashev Alexander
Published: September 2024

Background: Currently available studies on the long-term functional outcomes of anterior cruciate ligament (ACL) reconstruction have yielded conflicting results. The purpose of this study was to evaluate the biomechanical characteristics of walking in the long term after ACL reconstruction. Methods: The study included a test group of 18 patients (3.4 years from the date of ACL reconstruction on average) and a control group of 20 healthy subjects. Their gaits were assessed using functional tests at voluntary walking and fast-walking speeds. The biomechanical assessments utilized included spatiotemporal and kinematic parameters of walking, as well as surface electromyography (EMG) amplitudes of the main flexor-extensor muscles of the lower extremities. Results: Fast-walking speeds and the clearances of the operated-upon limbs in the patient group exceeded those in the control group. The gait cycle in the patient group was significantly longer when walking at a voluntary speed compared to the control group. In the patient group, most of the movements were symmetrical at both speeds, and the parameters did not differ from the control group. The only exception was the hip joint amplitude and the main amplitude of the knee joint flexion, which significantly and simultaneously increased when walking at a fast speed. Conclusions: In the postoperative period, at voluntary speeds, the patient group was characterized by increased amplitudes in the hip and knee joints and higher EMG amplitudes, which almost disappeared at higher speeds.
Nested-PCR vs. RT-qPCR: A Sensitivity Comparison in the Detection of Genetic Alterations in Patients with Acute Leukemias
Authors: Pessoa Flávia Melo Cunha de Pinho , de Oliveira Marcelo Braga , Barreto Igor Valentim , Machado Anna Karolyna da Costa , Oliveira Deivide Sousa de , Ribeiro Rodrigo Monteiro , Medeiros Jaira Costa , Maciel Aurélia da Rocha , Silva Fabiana Aguiar Carneiro , Gurgel Lívia Andrade , de Albuquerque Kaira Mara Cordeiro , Lopes Germison Silva , Vieira Ricardo Parente Garcia , Arraes Jussara Alencar , Alencar Filho Meton Soares de , Khayat André Salim , Moraes Maria Elisabete Amaral de , de Moraes Filho Manoel Odorico , Moreira-Nunes Caroline Aquino
Published: September 2024

The detection of genetic alterations in patients with acute leukemias is essential for the targeting of more specific and effective therapies. Therefore, the aim of this study was to compare the sensitivity of Nested-PCR and RT-qPCR techniques in the detection of genetic alterations in patients with acute leukemias. This study included samples from 117 patients treated at the Fortaleza General Hospital. All samples were submitted to analysis using the Nested-PCR and the RT-qPCR techniques. Acute Myeloid Leukemia (AML) patients’ samples were submitted to the analysis of the following alterations: FLT3-ITD, RUNX1::RUNX1T1, CBFB::MYH11 and PML::RARA; meanwhile, BCR::ABL1, TCF3::PBX1, KMT2A::AFF1, ETV6::RUNX1, and STIL::TAL1 fusions were investigated in the Acute Lymphoblastic Leukemia (ALL) patients’ samples. Throughout the study, 77 patients were diagnosed with AML and 40 with ALL. Among the 77 AML patients, FLT3-ITD, RUNX1::RUNX1T1, PML::RARA, and CBFB::MYH11 were detected in 4, 7, 10 and 8 patients, respectively. Among the 40 ALL patients, the presence of 23 patients with BCR::ABL1 translocation and 9 patients with TCF3::PBX1 translocation was observed through the RT-qPCR methodology. Overall, the present study demonstrated that the RT-qPCR technique presented a higher sensitivity when compared to the Nested-PCR technique at the time of diagnosis of the acute leukemia samples studied.
Optimization of Urban Target Area Accessibility for Multi-UAV Data Gathering Based on Deep Reinforcement Learning
Authors: Jin Zhengmiao , Chen Renxiang , Wu Ke , Yu Tengwei , Fu Linghua
Published: September 2024

Unmanned aerial vehicles (UAVs) are increasingly deployed to enhance the operational efficiency of city services. However, finding optimal solutions for the gather–return task pattern under dynamic environments and the energy constraints of UAVs remains a challenge, particularly in dense high-rise building areas. This paper investigates the multi-UAV path planning problem, aiming to optimize solutions and enhance data gathering rates by refining exploration strategies. Initially, for the path planning problem, a reinforcement learning (RL) technique equipped with an environment reset strategy is adopted, and the data gathering problem is modeled as a maximization problem. Subsequently, to address the limitations of stationary distribution in indicating the short-term behavioral patterns of agents, a Time-Adaptive Distribution is proposed, which evaluates and optimizes the policy by combining the behavioral characteristics of agents across different time scales. This approach is particularly suitable for the early stages of learning. Furthermore, the paper describes and defines the “Narrow-Elongated Path” Problem (NEP-Problem), a special spatial configuration in RL environments that hinders agents from finding optimal solutions through random exploration. To address this, a Robust-Optimization Exploration Strategy is introduced, leveraging expert knowledge and robust optimization to ensure UAVs can deterministically reach and thoroughly explore any target areas. Finally, extensive simulation experiments validate the effectiveness of the proposed path planning algorithms and comprehensively analyze the impact of different exploration strategies on data gathering efficiency.
Deep Reinforcement Learning-Driven Collaborative Rounding-Up for Multiple Unmanned Aerial Vehicles in Obstacle Environments
Authors: Zhao Zipeng , Wan Yu , Chen Yong
Published: September 2024

With the rapid advancement of UAV technology, the utilization of multi-UAV cooperative operations has become increasingly prevalent in various domains, including military and civilian applications. However, achieving efficient coordinated rounding-up of multiple UAVs remains a challenging problem. This paper addresses the issue of collaborative drone hunting by proposing a decision-making control model based on deep reinforcement learning. Additionally, a shared experience data pool is established to facilitate communication between drones. Each drone possesses independent decision-making and control capabilities while also considering the presence of other drones in the environment to collaboratively accomplish obstacle avoidance and rounding-up tasks. Furthermore, we redefine and design the reward function of reinforcement learning to achieve precise control of drone swarms in diverse environments. Simulation experiments demonstrate the feasibility of the proposed method, showcasing its successful completion of obstacle avoidance, tracking, and rounding-up tasks in an obstacle environment.
The Learning Assessment Process in Higher Education: A Grounded Theory Approach
Authors: Aguayo-Hernández Claudia H. , Sánchez Guerrero Alejandro , Vázquez-Villegas Patricia
Published: September 2024

This study aims to understand university professors’ perspectives on the learning assessment process, including its importance during teaching and learning, their conceptualization, and their considerations in their practices. The research used a grounded theory approach to recognize evaluation as a dialogical and intersubjective space. The methodology consisted of an open survey and a semi-structured interview with faculty professors from a university in northern Mexico. The findings highlighted the importance of educational institutions, emphasizing that faculties prioritize evaluating quality based on relevance, alignment with learning objectives, continuity throughout the process, and feedback. These aspects align with recent approaches that consider evaluation as a process that promotes learning, as evidenced by the high saturation rate in the theoretical sampling. Furthermore, the study revealed that the institution’s educational model, curricular design, and evaluation policies significantly influence the faculty members’ perspective. As a result, educational institutions must consider these factors when formulating an evaluation model, thereby making the research directly applicable to the work of educational policymakers and university professors.
Application of Variable Universe Fuzzy PID Controller Based on ISSA in Bridge Crane Control
Authors: Zhang Youyuan , Liu Lisang , He Dongwei
Published: September 2024

Bridge crane control systems are complex, multivariable, and nonlinear. However, traditional fuzzy PID control methods rely heavily on expert experience for initial parameter tuning and lack adaptive adjustment for the fuzzy universe. To address these issues, we propose a variable universe fuzzy PID controller based on the improved sparrow search algorithm (ISSA-VUFPID). First, tent chaotic mapping is introduced to initialize the sparrow population, enhancing the algorithm’s global search capability. Second, the positioning strategy of the northern goshawk exploration phase is integrated to improve the search thoroughness of sparrow discoverers within the solution space and to accelerate the optimization process. Last, an adaptive t-distribution perturbation strategy is employed to adjust the positions of sparrow followers, enhancing the algorithm’s optimization ability in the early search phase and focusing on local exploitation in the later phase to improve solution accuracy. The improved algorithm is applied to tune the initial parameters of the PID controller. Additionally, system error and its rate of change are introduced as dynamic parameters into the scaling factor, which is used to achieve adaptive adjustment of the fuzzy universe, thereby enhancing the safety and reliability of the control system. Simulation results demonstrate that the proposed ISSA-VUFPID control method outperforms ISSA-FPID and ISSA-PID control methods. It reduces the trolley’s positioning time and minimizes the load’s maximum swing angle, demonstrating strong adaptability and robustness. This approach greatly enhances the robustness and safety of bridge crane operations.
AFTEA Framework for Supporting Dynamic Autonomous Driving Situation
Authors: Kim Subi , Kang Jieun , Yoon Yongik
Published: September 2024

The accelerated development of AI technology has brought about revolutionary changes in various fields of society. Recently, it has been emphasized that fairness, accountability, transparency, and explainability (FATE) should be considered to support the reliability and validity of AI-based decision-making. However, in the case of autonomous driving technology, which is directly related to human life and requires real-time adaptation and response to various changes and risks in the real world, environmental adaptability must be considered in a more comprehensive and converged manner. In order to derive definitive evidence for each object in a convergent autonomous driving environment, it is necessary to transparently collect and provide various types of road environment information for driving objects and driving assistance and to construct driving technology that is adaptable to various situations by considering all uncertainties in the real-time changing driving environment. This allows for unbiased and fair results based on flexible contextual understanding, even in situations that do not conform to rules and patterns, by considering the convergent interactions and dynamic situations of various objects that are possible in a real-time road environment. The transparent, environmentally adaptive, and fairness-based outcomes provide the basis for the decision-making process and support clear interpretation and explainability of decisions. All of these processes enable autonomous vehicles to draw reliable conclusions and take responsibility for their decisions in autonomous driving situations. Therefore, this paper proposes an adaptability, fairness, transparency, explainability, and accountability (AFTEA) framework to build a stable and reliable autonomous driving environment in dynamic situations. This paper explains the definition, role, and necessity of AFTEA in artificial intelligence technology and highlights its value when applied and integrated into autonomous driving technology. The AFTEA framework with environmental adaptability will support the establishment of a sustainable autonomous driving environment in dynamic environments and aims to provide a direction for establishing a stable and reliable AI system that adapts to various real-world scenarios.
A Short-Term Power Prediction Method for Photovoltaics Based on Similar Day Clustering and Spatio-Temporal Feature Extraction
Authors: Huang Xu , Wang Leying , Ge Leijiao , Hou Luyang , Du Tianshuo , Zheng Yiwen , Chen Yanbo
Published: September 2024

Accurate PV power prediction is crucial for enhancing grid planning, optimizing dispatch operations, and advancing management strategies. In pursuit of this objective, this study proposes a short-term distributed PV power prediction method that incorporates temporal and spatial feature extraction as well as similar day analysis. Firstly, to address the poor adaptability of traditional clustering methods to time-series data, the K-shape clustering algorithm is employed to categorize the time series into different weather types. Secondly, to overcome the challenges posed by varying time resolutions in similar day analysis, a novel method based on Dynamic Time Warping (DTW) is proposed. This method calculates the similarity between the target days and the days to be collected, considering both the time of day and the day of the week. Subsequently, a PV power generation prediction model based on a convolutional long short-term memory (CNN-LSTM) network is developed to enhance prediction accuracy. To tackle the difficulty of manual hyperparameter tuning, the chaos reverse sparrow search algorithm (CRSSA) is introduced. Finally, a case study is conducted on the measured data of a distributed photovoltaic power station in a certain region of China. By comparing RMSE and MAPE, compared with other prediction models, the proposed prediction model and solving algorithm effectively reduced the relative error by more than 1%, verifying the effectiveness of the proposed method.
A Personalized Federated Learning Method Based on Knowledge Distillation and Differential Privacy
Authors: Jiang Yingrui , Zhao Xuejian , Li Hao , Xue Yu
Published: September 2024

Federated learning allows data to remain decentralized, and various devices work together to train a common machine learning model. This method keeps sensitive data local on devices, protecting privacy. However, privacy protection and non-independent and identically distributed data are significant challenges for many FL techniques currently in use. This paper proposes a personalized federated learning method (FedKADP) that integrates knowledge distillation and differential privacy to address the issues of privacy protection and non-independent and identically distributed data in federated learning. The introduction of a bidirectional feedback mechanism enables the establishment of an interactive tuning loop between knowledge distillation and differential privacy, allowing dynamic tuning and continuous performance optimization while protecting user privacy. By closely monitoring privacy overhead through Rényi differential privacy theory, this approach effectively balances model performance and privacy protection. Experimental results using the MNIST and CIFAR-10 datasets demonstrate that FedKADP performs better than conventional federated learning techniques, particularly when handling non-independent and identically distributed data. It successfully lowers the heterogeneity of the model, accelerates global model convergence, and improves validation accuracy, making it a new approach to federated learning.
Edge Detection in Colored Images Using Parallel CNNs and Social Spider Optimization
Authors: Zhang Jiahao , Wang Wei , Wang Jianfei
Published: September 2024

Edge detection is a crucial issue in computer vision, with convolutional neural networks (CNNs) being a key component in various systems for detecting edges within images, offering numerous practical implementations. This paper introduces a hybrid approach for edge detection in color images using an enhanced holistically led edge detection (HED) structure. The method consists of two primary phases: edge approximation based on parallel convolutional neural networks (PCNNs) and edge enhancement based on social spider optimization (SSO). The first phase uses two parallel CNN models to preliminarily approximate image edges. The first model uses edge-detected images from the Otsu-Canny operator, while the second model accepts RGB color images as input. The output of the proposed PCNN model is compared with pairwise combination of color layers in the input image. In the second phase, the SSO algorithm is used to optimize the edge detection result, modifying edges in the approximate image to minimize differences with the resulting color layer combinations. The experimental results demonstrate that our proposed method achieved a precision of 0.95. Furthermore, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) values stand at 20.39 and 0.83, respectively. The high PSNR value of our method signifies superior output quality, showing reduced contrast and noise compared to the ground truth image. Similarly, the SSIM value indicates that the method’s edge structure surpasses that of the ground truth image, further affirming its superiority over other methods.
Towards Sustainability and Energy Efficiency Using Data Analytics for HPC Data Center
Authors: Chinnici Andrea , Ahmadzada Eyvaz , Kor Ah-Lian , De Chiara Davide , Domínguez-Díaz Adrián , de Marcos Ortega Luis , Chinnici Marta
Published: September 2024

High-performance computing (HPC) in data centers increases energy use and operational costs. Therefore, it is necessary to efficiently manage resources for the sustainability of and reduction in the carbon footprint. This research analyzes and optimizes ENEA HPC data centers, particularly the CRESCO6 cluster. The study starts by gathering and cleaning extensive datasets consisting of job schedules, environmental conditions, cooling systems, and sensors. Descriptive statistics accompanied with visualizations provide deep insight into collated data. Inferential statistics are then used to investigate relationships between various operational variables. Finally, machine learning models predict the average hot-aisle temperature based on cooling parameters, which can be used to determine optimal cooling settings. Furthermore, idle periods for computing nodes are analyzed to estimate wasted energy, as well as for evaluating the effect that idle node shutdown will have on the thermal characteristics of the data center under consideration. It closes with a discussion on how statistical and machine learning techniques can improve operations in a data center by focusing on important variables that determine consumption patterns.