bluuebunny/crossref_metadata_2025 huggingface dataset:
CRISPR/Cas9-Mediated Gene Therapy for Glioblastoma: A Scoping Review
Authors: Begagić Emir , Bečulić Hakija , Đuzić Nermin , Džidić-Krivić Amina , Pugonja Ragib , Muharemović Asja , Jaganjac Belma , Salković Naida , Sefo Haso , Pojskić Mirza
Published: January 2024

This scoping review examines the use of CRISPR/Cas9 gene editing in glioblastoma (GBM), a predominant and aggressive brain tumor. Categorizing gene targets into distinct groups, this review explores their roles in cell cycle regulation, microenvironmental dynamics, interphase processes, and therapy resistance reduction. The complexity of CRISPR-Cas9 applications in GBM research is highlighted, providing unique insights into apoptosis, cell proliferation, and immune responses within the tumor microenvironment. The studies challenge conventional perspectives on specific genes, emphasizing the potential therapeutic implications of manipulating key molecular players in cell cycle dynamics. Exploring CRISPR/Cas9 gene therapy in GBMs yields significant insights into the regulation of cellular processes, spanning cell interphase, renewal, and migration. Researchers, by precisely targeting specific genes, uncover the molecular orchestration governing cell proliferation, growth, and differentiation during critical phases of the cell cycle. The findings underscore the potential of CRISPR/Cas9 technology in unraveling the complex dynamics of the GBM microenvironment, offering promising avenues for targeted therapies to curb GBM growth. This review also outlines studies addressing therapy resistance in GBM, employing CRISPR/Cas9 to target genes associated with chemotherapy resistance, showcasing its transformative potential in effective GBM treatments.
Inflammatory Profile Assessment in a Highly Selected Athyreotic Population Undergoing Controlled and Standardized Hypothyroidism
Authors: Piticchio Tommaso , Savarino Francesco , Volpe Salvatore , Prinzi Antonio , Costanzo Gabriele , Gamarra Elena , Frasca Francesco , Trimboli Pierpaolo
Published: January 2024

Background: Hypothyroidism (hT) presents heterogeneous symptoms and findings. Evidence on this topic comes mainly from heterogeneous populations in terms of disease duration, residual thyroid function, and comorbidities. Therefore, it would be useful to assess systemic inflammation in a homogeneous hT population. The aim of this study was to investigate inflammation in a population that underwent standardized controlled hT. Methods: We recruited thyroidectomized patients diagnosed with thyroid cancer who were otherwise fit and healthy, showing hypothyroidism before I131 treatment using a standard protocol of LT4 withdrawal. The blood inflammatory indexes (BIIXs) (i.e., NLR, PLR, MLR, SII, SIRI, and AISI) were calculated using the blood tests collected just before I131 administration. Patients were divided according to sex, BMI, and thyroglobulin. The relationships between the BIIXs, age, and thyroid hormones were also investigated. Results: We included 143 patients. The median age of the sample was 43 years. The BIIX median values showed significant differences based on sex, BMI, and thyroglobulin levels (p < 0.05). No significant correlations were found between the BIIXs and age, TSH, FT4, and FT3. Conclusions: This study shows the BIIX median values of a population which underwent standardized hT. It suggests a role for some BIIXs in the evaluation of hypothyroidism in obese people and as hypothetical prognostic markers for thyroid cancer.
Properties of Nanohybrid Dental Composites—A Comparative In Vitro Study
Authors: Păstrav Mihaela , Păstrav Ovidiu , Chisnoiu Andrea Maria , Chisnoiu Radu Marcel , Cuc Stanca , Petean Ioan , Saroși Codruța , Feștilă Dana
Published: January 2024

(1) Background: the current study investigated three nanohybrid composites: two commercial products ClearfilMajestyTM (CM) and HarmonizeTM (HU), compared with an experimental product PS2. (2) Methods: Two sample types were molded using Teflon dies. The first sample type was represented by standard discs (20 mm diameter and 2 mm thickness) (n = 60, 20/each material), used for surface conditioning investigation, specifically roughness monitoring and color stability analysis using AFM and the CIELab test, respectively. The second sample type was a standard cylindrical specimen (4 mm diameter and 6 mm height) for compression testing (n = 60, 20/each material). After complete polymerization, the samples were ground with sandpaper and further polished. The filler size and distribution in the polymer matrix were investigated with SEM. Data were statistically analyzed using the Anova Test followed by Tukey’s post hoc test on the Origin Lab 2019 software produced by OriginLab Corporation, Northampton, MA, USA. (3) Results: A mono-disperse system was identified in HU samples, while CM and PS2 revealed both nano- and microfiller particles. The samples’ observation after immersion in coffee and tea indicated that a lower roughness combined with optimal filler lamination within the polymer matrix assured the best color preservation. The compression strength was lower for the HU sample, while higher values were obtained for the complex filler systems within CM and PS2. (4) Conclusions: the behavior of the investigated nanohybrid composites strongly depends on the microstructural features.
Porcine Models of Neurotrauma and Neurological Disorders
Authors: O’Donnell John C. , Petrov Dmitriy
Published: January 2024

The translation of therapeutics from lab to clinic has a dismal record in the fields of neurotrauma and neurological disorders [...]
CAR NK92 Cells Targeting BCMA Can Effectively Kill Multiple Myeloma Cells Both In Vitro and In Vivo
Authors: Park Eunhee , Mun Hui-jin , Seo Eunju , Hwang Seojin , Lee Jae Hee , Song Sukgil , Sung Hyeran , Kim Hoi-Yul , Kwon Mi-Jin
Published: January 2024

Multiple myeloma (MM) is a hematological malignancy caused by malignant proliferation of plasma cells in bone marrow. Over the last decade, the survival outcome of patients with multiple myeloma (MM) has been substantially improved with the emergence of novel therapeutic agents. However, MM remains an incurable neoplastic plasma cell disorder. In addition, almost all MM patients inevitably relapse due to drug resistance. Chimeric antigen receptor (CAR)-modified NK cells represent a promising immunotherapeutic modality for cancer treatment. In this study, NK92 cells were engineered to express the third generation of BCMA CAR. In vitro, BCMA CAR-engineered NK92 cells displayed higher cytotoxicity and produced more cytokines such as IFN-γ and granzyme B than NK92 cells when they were co-cultured with MM cell lines. Furthermore, BCMA CAR-engineered NK92 cells released significantly higher amounts of cytokines and showed higher cytotoxicity when they were exposed to primary cells isolated from MM patients. The cytotoxicity of BCMA CAR NK92 cells was enhanced after MM cells were treated with bortezomib. Additionally, BCMA CAR NK92 cells exhibited potent antitumor activities in subcutaneous tumor models of MM. These results demonstrate that regional administration of BCMA CAR NK92 cells is a potentially promising strategy for treating MM.
Customized Subperiosteal Implants for the Rehabilitation of Atrophic Jaws: A Consensus Report and Literature Review
Authors: Herce-López Javier , Pingarrón Mariano del Canto , Tofé-Povedano Álvaro , García-Arana Laura , Espino-Segura-Illa Marc , Sieira-Gil Ramón , Rodado-Alonso Carlos , Sánchez-Torres Alba , Figueiredo Rui
Published: January 2024

(1) Background: The aim was to perform a literature review on customized subperiosteal implants (CSIs) and provide clinical guidelines based on the results of an expert consensus meeting held in 2023. (2) Methods: A literature search was performed in Pubmed (MEDLINE) in July 2023, including case series and cohort studies with a minimum follow-up of 6 months that analyzed totally or partially edentulous patients treated with CSIs. Previously, an expert consensus meeting had been held in May 2023 to establish the most relevant clinical guidelines. (3) Results: Six papers (four case series and two retrospective cohort studies) were finally included in the review. Biological and mechanical complication rates ranged from 5.7% to 43.8% and from 6.3% to 20%, respectively. Thorough digital planning to ensure the passive fit of the CSI is mandatory to avoid implant failure. (4) Conclusions: CSIs are a promising treatment option for rehabilitating edentulous patients with atrophic jaws; they seem to have an excellent short-term survival rate, a low incidence of major complications, and less morbidity in comparison with complex bone grafting procedures. As the available data on the use of CSIs are very scarce, it is not possible to establish clinical recommendations based on scientific evidence.
A Semi-Autonomous Hierarchical Control Framework for Prosthetic Hands Inspired by Dual Streams of Human
Authors: Zhou Xuanyi , Zhang Jianhua , Yang Bangchu , Ma Xiaolong , Fu Hao , Cai Shibo , Bao Guanjun
Published: January 2024

The routine use of prosthetic hands significantly enhances amputees’ daily lives, yet it often introduces cognitive load and reduces reaction speed. To address this issue, we introduce a wearable semi-autonomous hierarchical control framework tailored for amputees. Drawing inspiration from the visual processing stream in humans, a fully autonomous bionic controller is integrated into the prosthetic hand control system to offload cognitive burden, complemented by a Human-in-the-Loop (HIL) control method. In the ventral-stream phase, the controller integrates multi-modal information from the user’s hand–eye coordination and biological instincts to analyze the user’s movement intention and manipulate primitive switches in the variable domain of view. Transitioning to the dorsal-stream phase, precise force control is attained through the HIL control strategy, combining feedback from the prosthetic hand’s sensors and the user’s electromyographic (EMG) signals. The effectiveness of the proposed interface is demonstrated by the experimental results. Our approach presents a more effective method of interaction between a robotic control system and the human.
An Effective and Playful Way of Practicing Online Motor Proficiency in Preschool Children
Authors: Adamopoulou Eleanna , Karatrantou Konstantina , Kaloudis Ioannis , Krommidas Charalampos , Gerodimos Vassilis
Published: January 2024

The children’s fairytale is a playful educational tool that can be modified in such a way to enhance motor proficiency. This study investigated the effect of an online exercise program with modified fairytales on children’s motor proficiency during the kindergarten curriculum. Forty preschool children (20 girls and 20 boys; 5.13 ± 0.24 years old) were divided into two equal groups: an intervention (IG) group and a control group (CG). The IG followed a 3-month (3 times/week) online exercise intervention program (supervised by the class’s kindergarten teacher) with modified children’s fairytales, during the COVID-19 pandemic, with an aim to improve their motor proficiency. The CG did not attend any exercise intervention program. The Democritos Movement Screening Tool for Preschool Children (DEMOST-PRE), consisting of ten tests, was used to assess the children’s motor proficiency. Τhe IG, after the end of the intervention program, significantly improved in the DEMOST-PRE total score (p < 0.001; mean change: 40.7%), while the CG did not significantly improve in the total score (p > 0.05). Furthermore, in the IG, a significant negative correlation between the pre-training DEMOST-PRE total score and the percentage change following the intervention (r = −0.64; p = 0.002) was observed. A modified exercise program using children’s fairytales may be incorporated into the kindergarten curriculum as an effective educational tool for the improvement of motor proficiency.
Experiences and Challenges of Health Professionals in Implementing Family-Centred Planning: A Qualitative Study
Authors: Cuenca-Sánchez Lorena , Sánchez-Teruel David , Robles-Bello Maria Auxiliadora
Published: January 2024

Early childhood intervention is crucial for the development of minors with disabilities or at risk. Family-centred planning (FCP), which involves families in care, stands out in this context. Despite its importance, little is known about professionals’ experiences of its implementation. FCP aims to tailor services to the needs of the family and the child within the Spanish health system. This study highlights the importance of assessing professionals’ perceptions of FCP. Professionals rooted in traditional approaches may resist change. To assess the implementation of FCPs, the study explores the perspectives of 25 healthcare professionals using qualitative methods to assess their experiences. The qualitative descriptive phenomenological design, following Giorgi’s modified Husserlian approach, seeks to understand the essence of the phenomenon from the participants’ perspective. Two main themes emerged: (1) a social and work organization that perpetuates rehabilitation or early stimulation practices and (2) a socio-family and work organization that promotes FCP adherence, along with subthemes and units of meaning. The evaluation reveals common challenges, such as the need for solid training and institutional support. Evaluating the experience of professionals is essential to overcome barriers and ensure the successful implementation of FCPs. Administrators have an important role to play in providing social, health, and educational alternatives.
Long-Term Hepatitis B Vaccine Immunity in Ethiopian Children That Received a Pentavalent Vaccine Series: A Retrospective Cohort Study
Authors: Alemayehu Tinsae , Daba Million Dechassa , Buonsenso Danilo
Published: January 2024

Background: Chronic hepatitis B affects close to 300 million people globally with 1.5 million new infections per year. It causes the highest numbers of cirrhosis and liver cancer diagnoses each year. In children, perinatal transmission and contact with infected blood or body fluids remain the main methods of transmission. There are increasing reports of breakthrough hepatitis B infections in fully vaccinated children born to hepatitis B-negative mothers, especially in low- and middle-income countries. Our study aimed to measure the adequacy of hepatitis B surface antibody levels among children and adolescents who received three rounds of hepatitis B vaccination during infancy and delivered to hepatitis B-negative mothers in Addis Ababa, Ethiopia. Method: This was a retrospective cohort study analyzing results of paired serology tests for hepatitis B surface antibody and antigen tests performed for children aged 1–18 years from July 2022 to June 2023. All recorded data were transferred to SPSS version 29.0. The prevalence of adequate hepatitis B surface antibody levels was determined and sub-group analysis conducted using descriptive statistics, frequencies and tables. The magnitude of association between different variables and vaccine-induced hepatitis B immunity was assessed using logistic regression. Statistically significant differences were taken at p < 0.05. Results: A total of 256 children were included in the study (mean age: 7.53 years). Six children (2.3%) had breakthrough hepatitis B infections. Overall, 37 children (14.4%) were categorized as having optimal hepatitis B surface antibody levels (vaccine-induced antibody titers of >10 IU/mL), while 219 (85.6%) had low titers of <10 IU/mL. Nearly all (97.4%) of the sub-group aged 10 years and above had below-par antibody levels, with adolescents (11–18 years) being ten times more likely to have low seroprotection than those aged less than 5 years. Conclusions: Our study showed markedly low vaccine-induced hepatitis B surface antibody levels among the study population, especially adolescents. The presence of breakthrough infections may suggest a genuine lack of response and not just a mere drop in antibody titers and thus could highlight a significant public health problem in Ethiopia. Further immunologic studies and a thorough analysis of vaccine storage and administration should be conducted to inform prevention programs.
The Effects of a Physical Activity Intervention on Adiposity, Physical Fitness and Motor Competence: A School-Based, Non-Randomized Controlled Trial
Authors: Godoy-Cumillaf Andrés , Fuentes-Merino Paola , Giakoni-Ramírez Frano , Duclos-Bastías Daniel , Bruneau-Chávez José , Merellano-Navarro Eugenio
Published: January 2024

Evidence suggests that early physical activity interventions are a means of preventing childhood obesity and are more effective when delivered in a school setting and based on the ecological model. Therefore, the present study aims to determine the effect of a multicomponent intervention based on the ecological model on adiposity, physical fitness and motor competence in children aged 4 to 5 years. Methods: This study is a non-randomized controlled trial involving 173 children from Chile. The intervention was based on an ecological model and consisted of a physical activity program with three simultaneous parts, affecting intra- and interpersonal dimensions. The adiposity index, body mass index and waist circumference were measured. For physical fitness, muscle strength in the lower part, speed/agility and cardiorespiratory fitness were measured. Motor competence was assessed using catching, aiming and dynamic and static balance tests. Results: After the intervention, there was no reduction in adiposity indices; in the intervention group, body mass index increased significantly with a high effect size. The intervention group showed significant differences in physical fitness in the components of muscle strength in the lower part (p = 0.000) and speed/agility (p = 0.002). For motor competence, the intervention group showed significant improvements in most components. Conclusions: The multicomponent intervention did not reduce adiposity indices; however, it caused significant improvements in the physical fitness and motor competence components, so it seems prudent to continue implementing it, given the benefits that adequate levels of motor competence and physical fitness bring to children’s health, both in the short and long term.
Development of Inherent Vulnerability Index within Jammu Municipal Limits, India
Authors: Bharti Simran , Haq Adyan Ul , Guite L. T. Sasang , Kanga Shruti , Mushtaq Fayma , Farooq Majid , Singh Suraj Kumar , Kumar Pankaj , Meraj Gowhar
Published: January 2024

Evaluating inherent vulnerability, an intrinsic characteristic becomes imperative for the formulation of adaptation strategies, particularly in highly complex and vulnerable regions of Himalayas. Jammu City, situated in the north-western Himalayas within a transitional zone between the Himalayan range and the plains, is not only susceptible to intense seismic activities but also faces multiple hazards, including floods, earthquakes, avalanches, and landslides. In recent years, the region has experienced growth in population with rapid progress in infrastructure development, encompassing the construction of highways, dams, and tunnels as integral components of urban development initiatives. Therefore, this study has been conducted to assess the inherent vulnerability index (VI) in Jammu City at ward level as a function of sensitivity, adaptive capacity, and exposure, using ecological and social indicators in GIS environment. The primary objective was to identify the most vulnerable area and ascertain the corresponding municipal ward, aiming to formulate a comprehensive ranking. The 22 indicators analysed were from four major components, namely social, infrastructure, technological, and ecological. The ecological indicators like Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), and Land use/Land cover were derived from Landsat 8 OLI satellite data. The results show that the majority of the area of the city falls into the moderate (20%), high (25.49%), and very high (25.17%) vulnerability categories, respectively, clustered in north-western and south-western transects with densely populated residential areas. The results can assist policymakers in identification of components of inherent vulnerability for focused resource management and formulating adaptation strategies to address the current stressors in the region.
Influence of Gyrotactic Microorganisms on Bioconvection in Electromagnetohydrodynamic Hybrid Nanofluid through a Permeable Sheet
Authors: Rashed Ahmed S. , Nasr Ehsan H. , Mabrouk Samah M.
Published: January 2024

Many biotechnology sectors that depend on fluids and their physical characteristics, including the phenomenon of bioconvection, have generated a great deal of discussion. The term “bioconvection” describes the organized movement of microorganisms, such as bacteria or algae. Microorganisms that participate in bioconvection display directed movement, frequently in the form of upward or downward streaming, which can lead to the production of distinctive patterns. The interaction between the microbes’ swimming behavior and the physical forces acting on them, such as buoyancy and fluid flow, is what drives these patterns. This work considers the laminar-mixed convection incompressible flow at the stagnation point with viscous and gyrotactic microorganisms in an unsteady electrically conducting hybrid nanofluid (Fe3O4-Cu/water). In addition, hybrid nanofluid flow over a horizontal porous stretched sheet, as well as external and induced magnetic field effects, can be used in biological domains, including drug delivery and microcirculatory system flow dynamics. The governing system has been reduced to a set of ordinary differential equations (ODEs) through the use of the group technique. The current research was inspired by an examination of the impacts of multiple parameters, including Prandtl number, Pr, magnetic diffusivity, η0, shape factor, n, microorganism diffusion coefficient, Dn, Brownian motion coefficient, DB, thermophoresis diffusion coefficient,  DT, bioconvection Peclet number, Pe, temperature difference,  δt, and concentration difference,  δc. The results show that as Pr rises, temperature, heat flux, and nanoparticles all decrease. In contrast, when the η0 value increases, the magnetic field and velocity decrease. Heat flow, bacterial density, and temperature decrease as the DB value rises, yet the number of nanoparticles increases. As the DT value increases, the temperature, heat flow, and concentration of nanoparticles all rise while the density of bacteria decreases. Even though temperature, heat flux, nanoparticles, and bacterial density all decrease as δc values climb, bacterial density rises as Dn values do although bacterial density falls with increasing,  δt and Pe values; on the other hand, when n values increase, temperature and heat flow increase but the density of bacteria and nanoparticle decrease. The physical importance and behavior of the present parameters were illustrated graphically.
Computer Aided Structure-Based Drug Design of Novel SARS-CoV-2 Main Protease Inhibitors: Molecular Docking and Molecular Dynamics Study
Authors: Kolybalov Dmitry S. , Kadtsyn Evgenii D. , Arkhipov Sergey G.
Published: January 2024

Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) virus syndrome caused the recent outbreak of COVID-19 disease, the most significant challenge to public health for decades. Despite the successful development of vaccines and promising therapies, the development of novel drugs is still in the interests of scientific society. SARS-CoV-2 main protease Mpro is one of the key proteins for the lifecycle of the virus and is considered an intriguing target. We used a structure-based drug design approach as a part of the search of new inhibitors for SARS-CoV-2 Mpro and hence new potential drugs for treating COVID-19. Four structures of potential inhibitors of (4S)-2-(2-(1H-imidazol-5-yl)ethyl)-4-amino-2-(1,3-dihydroxypropyl)-3-hydroxy-5-(1H-imidazol-5-yl)pentanal (L1), (2R,4S)-2-((1H-imidazol-4-yl)methyl)-4-chloro-8-hydroxy-7-(hydroxymethyl)octanoic acid (L2), 1,9-dihydroxy-6-(hydroxymethyl)-6-(((1S)-1,7,7-trimethylbicyclo [2.2.1]heptan-2-yl)amino)nonan-4-one (L3), and 2,4,6-tris((4H-1,2,4-triazol-3-yl)amino)benzonitrile (L4) were modeled. Three-dimensional structures of ligand–protein complexes were modeled and their potential binding efficiency proved. Docking and molecular dynamic simulations were performed for these compounds. Detailed trajectory analysis of the ligands’ binding conformation was carried out. Binding free energies were estimated by the MM/PBSA approach. Results suggest a high potential efficiency of the studied inhibitors.
Improved Deep Learning Model for Workpieces of Rectangular Pipeline Surface Defect Detection
Authors: Chen Changxing , Azman Afizan
Published: January 2024

This study introduces a novel approach to address challenges in workpiece surface defect identification. It presents an enhanced Single Shot MultiBox Detector model, incorporating attention mechanisms and multi-feature fusion. The research methodology involves carefully curating a dataset from authentic on-site factory production, enabling the training of a model with robust real-world generalization. Leveraging the Single Shot MultiBox Detector model lead to improvements integrating channel and spatial attention mechanisms in the feature extraction network. Diverse feature extraction methods enhance the network’s focus on crucial information, improving its defect detection efficacy. The proposed model achieves a significant Mean Average Precision (mAP) improvement, reaching 99.98% precision, a substantial 3% advancement over existing methodologies. Notably, the proposed model exhibits a tendency for the values of the P-R curves in object detection for each category to approach 1, which allows a better balance between the requirements of real-time detection and precision. Within the threshold range of 0.2 to 1, the model maintains a stable level of precision, consistently remaining between 0.99 and 1. In addition, the average running speed is 2 fps lower compared to other models, and the reduction in detection speed after the model improvement is kept within 1%. The experimental results indicate that the model excels in pixel-level defect identification, which is crucial for precise defect localization. Empirical experiments validate the algorithm’s superior performance. This research represents a pivotal advancement in workpiece surface defect identification, combining technological innovation with practical efficacy.
Impact of Lifestyle on Differences in Skin Hydration of Selected Body Areas in Young Women
Authors: Załęcki Piotr , Rogowska Karolina , Wąs Paulina , Łuczak Kamila , Wysocka Marta , Nowicka Danuta
Published: January 2024

Measuring skin hydration is important for dermatology research, cosmetic practice, and daily skincare; it provides valuable insight into skin assessment and predicts treatment outcomes. This study investigated the level of skin hydration on various parts of the body in female university students, utilizing corneometry for measurement. Demographic, water intake, skincare, and exercise habit data were collected. The highest hydration levels were on the face (n = 40), followed by cleavage skin (n = 42), neck skin (n = 48), and leg skin (n = 42). Differences were observed between the chin and forehead (106.06 ± 21.06 vs. 91.20 ± 26.68; p = 0.001) and the chin and cheek (106.06 ± 21.06 vs. 92.00 ± 27.63; p > 0.001). The differences in the hydration measurement results on cleavage were not significant (right clavicle 55.1 ± 12.93; left clavicle 51.84 ± 10.00; sternum 53.55 ± 16.3; p = 0.379). On the neck, the middle point exhibited the highest hydration (left 41.7 ± 11.3; middle 49.2 ± 12; right 47.2 ± 8.2; p > 0.001). The skin on the thigh was significantly drier than that on the shank (31.09 ± 6.89 vs. 33.54 ± 6.28; p = 0.008). Water consumption was positively associated with skin hydration and the amount of physical activity. We conclude that every skin area is characterized by different levels of hydration. Water intake and physical activity contribute to improved skin hydration.
Approach Distances of Scottish Golden Eagles Aquila chrysaetos to Wind Turbines According to Blade Motion Status, Wind Speed, and Preferred Habitat
Authors: Fielding Alan H. , Anderson David , Benn Stuart , Taylor John , Tingay Ruth , Weston Ewan D. , Whitfield D. Philip
Published: January 2024

Understanding drivers underlying birds’ responses to operational wind turbines is essential for robust wind farm proposal assessments, especially for large raptors with life history traits engendering sensitivity to impacts from two potential adverse effects: fatality through collision with rotating turbine blades and functional habitat loss through avoidance of turbines. The balance between these two potential effects represents opposing extremes on a continuum and is influenced by several factors. Collisions have an obvious impact on survival, but the impacts of avoidance may be more insidious and potentially more significant for a population. It is reasonable to conclude that collisions are less likely when blades are motionless. Consequently, turbine shutdown systems (TSSs, “shutdown on demand” or “curtailment”), instigated as raptors approach operational turbines, may provide mitigation against collisions. By contrast, if avoidance is most likely, this could be independent of blade motion, and TSSs/curtailment would provide no mitigation against habitat loss. For birds tending to wariness of turbines, therefore, it is important to understand if it is conditional on blade motion. Scottish golden eagles show a strong propensity to avoid (be wary of) turbines, subject largely to the suitability of habitat at and surrounding turbine locations. A previous Scottish study found that approach distances to turbines by non-territorial eagles were unaffected by blade motion but were closer at higher wind speed. Here, we analyse movement data from a GPS-tagged territorial eagle and non-territorial eagles responding to the motion status (and wind speed) of turbines at another Scottish wind farm. Eagles’ approach distances to turbines were only weakly affected by blade motion but were closer at higher wind speed. We again found that habitat suitability in and around turbine locations was strongly influential on eagles’ approach distance to turbines. Our confirmation that blade motion had little effect on Scottish golden eagles’ wariness of turbines suggests that for eagles that are prone to avoid turbines, their wariness is a response to turbines per se, and not blades’ movement. In our study system, and others where avoidance is the predominant response, curtailment of turbines’ operation on birds’ close approaches, or making turbine blades more obvious, should, therefore, have little material influence on functional habitat loss impacts. If true, this has important implications for wind farm designs and any proposed mitigation.
Incorporating a Load-Shifting Algorithm for Optimal Energy Storage Capacity Design in Smart Homes
Authors: Khwanrit Ruengwit , Lim Yuto , Javaid Saher , Charoenlarpnopparut Chalie , Tan Yasuo
Published: January 2024

In today’s power system landscape, renewable energy (RE) resources play a pivotal role, particularly within the residential sector. Despite the significance of these resources, the intermittent nature of RE resources, influenced by variable weather conditions, poses challenges to their reliability as energy resources. Addressing this challenge, the integration of an energy storage system (ESS) emerges as a viable solution, enabling the storage of surplus energy during peak-generation periods and subsequent release during shortages. One of the great challenges of ESSs is how to design ESSs efficiently. This paper focuses on a distributed power-flow system within a smart home environment, comprising uncontrollable power generators, uncontrollable loads, and multiple energy storage units. To address the challenge of minimizing energy loss in ESSs, this paper proposes a novel approach, called energy-efficient storage capacity with loss reduction (SCALE) scheme, that combines multiple-load power-flow assignment with a load-shifting algorithm to minimize energy loss and determine the optimal energy storage capacity. The optimization problem for optimal energy storage capacity is formalized using linear programming techniques. To validate the proposed scheme, real experimental data from a smart home environment during winter and summer seasons are employed. The results demonstrate the efficacy of the proposed algorithm in significantly reducing energy loss, particularly under winter conditions, and determining optimal energy storage capacity, with reductions of up to 11.4% in energy loss and up to 62.1% in optimal energy storage capacity.
Rule-Based Verification of Autonomous Unmanned Aerial Vehicles
Authors: Sieber Christoph , Vieira da Silva Luis Miguel , Grünhagen Kilian , Fay Alexander
Published: January 2024

Automation enhances the capabilities of unmanned aerial vehicles (UAVs) by enabling self-determined behavior, while reducing the need for extensive human involvement. Future concepts envision a single human operator commanding multiple autonomous UAVs with minimal supervision. Despite advances in automation, there remains a demand for a “human in command” to assume overall responsibility, driven by concerns about UAV safety and regulatory compliance. In response to these challenges, a method for runtime verification of UAVs using a knowledge-based system is introduced. This method empowers human operators to identify unsafe behavior without assuming full control of the UAV. Aspects of automated formalization, updating and processing of knowledge elements at runtime, coupled with an automatic reasoning process, are considered. The result is an ontology-based approach for runtime verification, addressing the growing complexity of UAVs and the need to ensure safety in the context of evolving aviation regulations.
Truck-Drone Delivery Optimization Based on Multi-Agent Reinforcement Learning
Authors: Bi Zhiliang , Guo Xiwang , Wang Jiacun , Qin Shujin , Liu Guanjun
Published: January 2024

In recent years, the adoption of truck–drone collaborative delivery has emerged as an innovative approach to enhance transportation efficiency and minimize the depletion of human resources. Such a model simultaneously addresses the endurance limitations of drones and the time wastage incurred during the “last-mile” deliveries by trucks. Trucks serve not only as a carrier platform for drones but also as storage hubs and energy sources for these unmanned aerial vehicles. Drawing from the distinctive attributes of truck–drone collaborative delivery, this research has created a multi-drone delivery environment utilizing the MPE library. Furthermore, a spectrum of optimization techniques has been employed to enhance the algorithm’s efficacy within the truck–drone distribution system. Finally, a comparative analysis is conducted with other multi-agent reinforcement learning algorithms within the same environment, thus affirming the rationality of the problem formulation and highlighting the algorithm’s superior performance.
Information Transmission in a Drone Swarm: A Temporal Network Analysis
Authors: Grosfils Patrick
Published: January 2024

We consider an ensemble of drones moving in a two-dimensional domain, each one of them carrying a communication device, and we investigate the problem of information transfer in the swarm when the transmission capabilities are short range. The problem is discussed under the framework of temporal networks, and special attention is paid to the analysis of the transmission time of messages transported within the swarm. Traditional theoretical methods of graph theory are extended to tackle the problem of time-varying networks and a numerical analysis of the detection time statistics is performed in order to evaluate the efficiency of the communication network as a function of the parameters characterizing the swarm dynamics.
Geometric Phase of a Transmon in a Dissipative Quantum Circuit
Authors: Viotti Ludmila , Lombardo Fernando C. , Villar Paula I.
Published: January 2024

Superconducting circuits reveal themselves as promising physical devices with multiple uses. Within those uses, the fundamental concept of the geometric phase accumulated by the state of a system shows up recurrently, as, for example, in the construction of geometric gates. Given this framework, we study the geometric phases acquired by a paradigmatic setup: a transmon coupled to a superconductor resonating cavity. We do so both for the case in which the evolution is unitary and when it is subjected to dissipative effects. These models offer a comprehensive quantum description of an anharmonic system interacting with a single mode of the electromagnetic field within a perfect or dissipative cavity, respectively. In the dissipative model, the non-unitary effects arise from dephasing, relaxation, and decay of the transmon coupled to its environment. Our approach enables a comparison of the geometric phases obtained in these models, leading to a thorough understanding of the corrections introduced by the presence of the environment.
Equity, Diversity, and Inclusion Strategies in Engineering and Computer Science
Authors: Amer Adan , Sidhu Gaganpreet , Alvarez Maria Isabel Ramirez , Ramos Juan Antonio Lopez , Srinivasan Seshasai
Published: January 2024

This article delves into the issues of equity, diversity, and inclusiveness (EDI) in the engineering disciplines in Canada and Spain and presents the challenges faced by underrepresented individuals and ways to promote an inclusive and diverse environment. Two strategic lines are identified: (a) facilitating university education access to underrepresented and minority groups and (b) guiding such students during university training to set them up for successful future careers. Accordingly, this article shows how the strategies mentioned above are implemented in some selected Canadian and Spanish universities, clearly distinguishing the approach taken in the two countries. In Canada, there is a more decentralized approach to addressing EDI issues, wherein the universities devise their agendas independently. In Spain, on the other hand, there is a stronger and more direct involvement of the government to ensure a comprehensive, system-wide approach to tackling EDI issues in academia. This article helps education policymakers to devise and implement pragmatic strategies for achieving EDI and the relevant UN-defined sustainable development goals.
Three-Dimensional-Consistent Scene Inpainting via Uncertainty-Aware Neural Radiance Field
Authors: Wang Meng , Yu Qinkang , Liu  Haipeng
Published: January 2024

3D (Three-Dimensional) scene inpainting aims to remove objects from scenes and generate visually plausible regions to fill the hollows. Leveraging the foundation of NeRF (Neural Radiance Field), considerable advancements have been achieved in the realm of 3D scene inpainting. However, prevalent issues persist: primarily, the presence of inconsistent 3D details across different viewpoints and occlusion losses of real background details in inpainted regions. This paper presents a NeRF-based inpainting approach using uncertainty estimation that formulates mask and uncertainty branches for consistency enhancement. In the initial training, the mask branch learns a 3D-consistent representation from inaccurate input masks, and after background rendering, the background regions can be fully exposed to the views. The uncertainty branch learns the visibility of spatial points by modeling them as Gaussian distributions, generating variances to identify regions to be inpainted. During the inpainting training phase, the uncertainty branch measures 3D consistency in the inpainted views and calculates the confidence from the variance as dynamic weights, which are used to balance the color and adversarial losses to achieve 3D-consistent inpainting with both the structure and texture. The results were evaluated on datasets such as Spin-NeRF and NeRF-Object-Removal. The proposed approach outperformed the baselines in inpainting metrics of LPIPS and FID, and preserved more spatial details from real backgrounds in multi-scene settings, thus achieving 3D-consistent restoration.
Landmark-Based Domain Adaptation and Selective Pseudo-Labeling for Heterogeneous Defect Prediction
Authors: Chen Yidan , Chen Haowen
Published: January 2024

Cross -project defect prediction (CPDP) is a promising technical means to solve the problem of insufficient training data in software defect prediction. As a special case of CPDP, heterogeneous defect prediction (HDP) has received increasing attention in recent years due to its ability to cope with different metric sets in projects. Existing studies have proven that using mixed-project data is a potential way to improve HDP performance, but there remain several challenges, including the negative impact of noise modules and the insufficient utilization of unlabeled modules. To this end, we propose a landmark-based domain adaptation and selective pseudo-labeling (LDASP) approach for mixed-project HDP. Specifically, we propose a novel landmark-based domain adaptation algorithm considering marginal and conditional distribution alignment and a class-wise locality structure to reduce the heterogeneity between both projects while reweighting modules to alleviate the negative impact brought by noise ones. Moreover, we design a progressive pseudo-label selection strategy exploring the underlying discriminative information of unlabeled target data to further improve the prediction effect. Extensive experiments are conducted based on 530 heterogeneous prediction combinations that are built from 27 projects using four datasets. The experimental results show that (1) our approach improves the F1-score and AUC over the baselines by 9.8–20.2% and 4.8–14.4%, respectively and (2) each component of LDASP (i.e., the landmark weights and selective pseudo-labeling strategy) can promote the HDP performance effectively.
Predictive Maintenance of Machinery with Rotating Parts Using Convolutional Neural Networks
Authors: Apeiranthitis Stamatis , Zacharia Paraskevi , Chatzopoulos Avraam , Papoutsidakis Michail
Published: January 2024

All kinds of vessels consist of dozens of complex machineries with rotating parts and electric motors that operate continuously in harsh environments with excess temperature, humidity, vibration, fatigue, and load. A breakdown or malfunction in one of these machineries can significantly impact a vessel’s operation and safety and, consequently, the safety of the crew and the environment. To maintain operational efficiency and seaworthiness, the shipping industry invests substantial resources in preventive maintenance and repairs. This study presents the economic and technical benefits of predictive maintenance over traditional preventive maintenance and repair by replacement approaches in the maritime domain. By leveraging modern technology and artificial intelligence, we can analyze the operating conditions of machinery by obtaining measurements either from sensors permanently installed on the machinery or by utilizing portable measuring instruments. This facilitates the early identification of potential damage, thereby enabling efficient strategizing for future maintenance and repair endeavors. In this paper, we propose and develop a convolutional neural network that is fed with raw vibration measurements acquired in a laboratory environment from the ball bearings of a motor. Then, we investigate whether the proposed network can accurately detect the functional state of ball bearings and categorize any possible failures present, contributing to improved maintenance practices in the shipping industry.
A Circularly Polarized Millimeter Wave Radar for Wind Turbine Sensing
Authors: Chen Jiayi , Guo Bin , Jin Yitong , Bao Zhijian , Wang Lijun , Wang Siye , Yang Guangli , Wang Rui , Luo Yong
Published: January 2024

Wind power is a crucial direction for new energy transition technology in response to the challenges of global warming. However, the potential for collisions between the blades and the tower barrel remains a significant concern. To address this issue, a large number of sensors, such as lasers and cameras, are attached to the structure, but they struggle to operate in complex weather and at night. This paper presents a method of employing a 79 GHz FMCW (frequency-modulated continuous wave) mmWave (millimeter-wave) radar with circularly polarization on the top of the tower. During the design, two main considerations are raised: (1) Since the small-RCS (radar cross-section) blade experiences an oblique incidence from more than 70 m away, the channel SNR (signal-to-noise ratio) is low, so high-gain antennas and SIMO (single-input multiple-output) radar configurations are designed to increase the Pt (transmitting power). (2) Wind turbines are often located in offshore or mountainous areas with a high level of weather interference, so a pair of circularly polarized antenna is used to reduce the interference of meteorological particles to the radar. Finally, test results from a practical wind turbine in different weather conditions prove its practicality. During tests, the wind turbine operates at a rotor speed of 6 to 12 rounds per minute, and the clearance range has an obvious inverse relationship with it, ranging from 6 to 12 m. This technology enhances safety, maximizes efficiency, and enables optimal length and weight determination during design for improved power generation.
A Hybrid Control-Oriented PEMFC Model Based on Echo State Networks and Gaussian Radial Basis Functions
Authors: Aguilar José Agustín , Chanal Damien , Chamagne Didier , Yousfi Steiner Nadia , Péra Marie-Cécile , Husar Attila , Andrade-Cetto Juan
Published: January 2024

The goal of increasing efficiency and durability of fuel cells can be achieved through optimal control of their operating conditions. In order to implement such controllers, accurate and computationally efficient fuel cell models must be developed. This work presents a hybrid (physics-based and data-driven), control-oriented model for approximating the output voltage of proton exchange membrane fuel cells (PEMFCs) while operating under dynamical conditions. First, a physics-based model, built from simplified electrochemical, membrane dynamics and mass conservation equations, is developed and validated through experimental data. Second, a data-driven, neural network (echo state network) is trained, fitted and tested with the same dataset. Then, the hybrid model is formed as a parallel structure, where the simplified physics-based model and the trained data-driven model are merged through an algorithm based on Gaussian radial basis functions. The merging algorithm compares the output of both single models and assigns weights for computing the prediction of the hybrid result. The proposed hybrid model structure is successfully trained, validated and tested with an experimental dataset originating from fuel cells within an automotive PEMFC stack. The hybrid model is assessed through the mean square error index, with the result of a low tracking error.