bluuebunny/crossref_metadata_2025 huggingface dataset:
Aerodatabase Development and Integration and Mission Analysis of a Mach 2 Supersonic Civil Aircraft
Authors: Roncioni Pietro , Marini Marco , Gori Oscar , Fusaro Roberta , Viola Nicole
Published: January 2024

The request for faster and greener civil aviation is urging the worldwide scientific community and aerospace industry to develop a new generation of supersonic aircraft, which are expected to be environmentally sustainable and to guarantee a high-level protection of citizens. A key aspect to monitor the potential environmental impact of new configurations is the aerodynamic efficiency and its impact onto the real mission. To pursue this goal, this paper discloses increasing-fidelity aerodynamic modeling approaches to improve the conceptual design of high-speed vehicles. The disclosed methodology foresees the development of aerodynamic aerodatabases by means of incremental steps starting from simplified methods (panels methods and/or low-fidelity CFD simulations) up to very reliable data based on high-fidelity CFD simulations and experimental measurements with associated confidence levels. This multifidelity approach enables the possibility of supporting the aircraft design process at different stages of its design cycle, from the estimation of preliminary aerodynamic coefficients at the beginning of the conceptual design, up to the development of tailored aerodatabases at advanced design phases. For each design stage, a build-up approach is adopted, starting from the investigation of the clean external configuration up to the complete one, including control surfaces’ effects and, if any, the effects of the integration of the propulsive effects. In addition, the applicability of the approach is guaranteed for a wide range of supersonic and hypersonic aircraft, and the developed methodology is here applied to the characterization of Mach 2 aircraft configuration, a relevant case study of the H2020 MORE&LESS project.
Some Geospatial Insights on Orange Grove Site Selection in a Portion of the Northern Citrus Belt of Mexico
Authors: Díaz-Rivera Juan Carlos , Aguirre-Salado Carlos Arturo , Miranda-Aragón Liliana , Aguirre-Salado Alejandro Ivan
Published: January 2024

This study aimed to delineate the most suitable areas for sustainable citrus production by integrating multi-criteria decision analysis, time-series remote sensing, and principal component analysis in a portion of the northern citrus belt of Mexico, particularly in the Rioverde Valley. Fourteen specific factors were grouped into four main factors, i.e., topography, soil, climate, and proximity to water sources, to carry out a multi-criteria decision analysis for classifying production areas according to suitability levels. To explore the effect of precipitation on land suitability for citrus production, we analyzed the historical record of annual precipitation estimated by processing 20-year NDVI daily data. The multi-criteria model was run for every precipitation year. The final map of land suitability was obtained by using the first component after principal component analysis on annual land suitability maps. The results indicate that approximately 30% of the study area is suitable for growing orange groves, with specific areas designated as suitable based on both mean annual precipitation (MAP) and principal component analysis (PCA) criteria, resulting in 84,415.7 ha and 95,485.5 ha of suitable land, respectively. The study highlighted the importance of remotely sensed data-based time-series precipitation in predicting potential land suitability for growing orange groves in semiarid lands. Our results may support decision-making processes for the effective land management of orange groves in the Mexico’s Rioverde region.
Modelling the Temperature Inside a Greenhouse Tunnel
Authors: Hull Keegan , van Schalkwyk Pieter Daniel , Mabitsela Mosima , Phiri Ethel Emmarantia , Booysen Marthinus Johannes
Published: January 2024

Climate-change-induced unpredictable weather patterns are adversely affecting global agricultural productivity, posing a significant threat to sustainability and food security, particularly in developing regions. Wealthier nations can invest substantially in measures to mitigate climate change’s impact on food production, but economically disadvantaged countries face challenges due to limited resources and heightened susceptibility to climate change. To enhance climate resilience in agriculture, technological solutions such as the Internet of Things (IoT) are being explored. This paper introduces a digital twin as a technological solution for monitoring and controlling temperatures in a greenhouse tunnel situated in Stellenbosch, South Africa. The study incorporates an aeroponics trial within the tunnel, analysing temperature variations caused by the fan and wet wall temperature regulatory systems. The research develops an analytical model and employs a support vector regression algorithm as an empirical model, successfully achieving accurate predictions. The analytical model demonstrated a root mean square error (RMSE) of 2.93 °C and an R2 value of 0.8, while the empirical model outperformed it with an RMSE of 1.76 °C and an R2 value of 0.9 for a one-hour-ahead simulation. Potential applications and future work using these modelling techniques are then discussed.
The Influence of Reverse Yielding on the Plastic Conditioning of Interference Fits in Power Transmission Engineering
Authors: Schierz Mario , Hasse Alexander
Published: January 2024

Interference fits are very common shaft–hub connections due to their low manufacturing costs and excellent technical properties. The Plastic Conditioning of this machine element is a new and not very well-known method. During the development of this method, it was discovered that Reverse Yielding occurs in certain applications and has a negative impact on the result. This paper examines the effects of Reverse Yielding on the technology of Plastic Conditioning of interference fits in Power Transmission Engineering. Based on the Shear Stress Hypothesis (SH), the Plane Stress State (PSS), and the ideal plastic behavior of materials, established stress–mechanical relationships are used to find the influencing parameters of Reverse Yielding on the technology of Plastic Conditioning and their limits. As a result, a new computational concept is developed that allows the user to maximize Plastic Conditioning while avoiding Reverse Yielding. Analytical calculation suggestions and diagrams for practical application are provided. Furthermore, the deviations in the obtained results, taking into account other material models such as the Von Mises Yield Criterion (VMYC) and material hardening, as well as the Bauschinger effect, are examined in comparison with our own numerical results from the development of Plastic Conditioning, and the resulting need for further research is defined. In addition, the method of Plastic Conditioning of interference fits is introduced and its basic principles are briefly explained.
Value Addition and Coconut-Based Beverages: Current Perspectives
Authors: Parisi Salvatore , Parisi Carmelo , Varghese Suni Mary
Published: January 2024

(1) Background: The definition of value addition is based on the process or processes which are used to transform, physically, the initial raw material into the final food or non-food article. Diversification can enhance the possibility of increased gains. The aim of this work is to give a reliable description of value addition when speaking of coconut-based beverages among all possible derivatives. (2) Methods: A systematic review in which the main papers on the argument have been critically examined and discussed. (3) Results: Processing degree is a consequence of consumers’ requests. Three different drivers for value addition have been considered: packaging, durability, and size options; sensorial features; and sustainability. The results of this investigation have highlighted the added value of several products because of recyclable packaging materials, intermediate- or long-durability expectations, different available sizes, and good or excellent sensorial performances. (4) Conclusions: There are different value-added coconut-based beverages with interesting perspectives. On the other hand, sustainability and eco-friendly policies may be a problem for those products that are produced similarly to non-coconut-based beverages. The opportunity presented by certified organic and/or fair-trade products could help the coconuts industry in the near future. More research is still needed on this topic.
The Effect of Patient-Related Factors on the Primary Fixation of PEEK and Titanium Tibial Components: A Population-Based FE Study
Authors: Post Corine E. , Bitter Thom , Briscoe Adam , van Langen Inger , Fluit René , Verdonschot Nico , Janssen Dennis
Published: January 2024

Polyetheretherketone (PEEK) is of interest as implant material for cementless tibial total knee arthroplasty (TKA) components due to its potential advantages. One main advantage is that the stiffness of PEEK closely resembles the stiffness of bone, potentially avoiding peri-prosthetic stress-shielding. When introducing a new implant material for cementless TKA designs, it is essential to study its effect on the primary fixation. The primary fixation may be influenced by patient factors such as age, gender, and body mass index (BMI). Therefore, the research objectives of this finite element (FE) study were to investigate the effect of material (PEEK vs. titanium) and patient characteristics on the primary fixation (i.e., micromotions) of a cementless tibial tray component. A total of 296 FE models of 74 tibiae were created with either PEEK or titanium material properties, under gait and squat loading conditions. Overall, the PEEK models generated larger peak micromotions than the titanium models. Differences were seen in the micromotion distributions between the PEEK and titanium models for both the gait and squat models. The micromotions of all tibial models significantly increased with BMI, while gender and age did not influence micromotions.
Comparison of sEMG Onset Detection Methods for Occupational Exoskeletons on Extensive Close-to-Application Data
Authors: Kreipe Stefan , Helbig Thomas , Witte Hartmut , Schumann Nikolaus-Peter , Anders Christoph
Published: January 2024

The design of human-machine interfaces of occupational exoskeletons is essential for their successful application, but at the same time demanding. In terms of information gain, biosensoric methods such as surface electromyography (sEMG) can help to achieve intuitive control of the device, for example by reduction of the inherent time latencies of a conventional, non-biosensoric, control scheme. To assess the reliability of sEMG onset detection under close to real-life circumstances, shoulder sEMG of 55 healthy test subjects was recorded during seated free arm lifting movements based on assembly tasks. Known algorithms for sEMG onset detection are reviewed and evaluated regarding application demands. A constant false alarm rate (CFAR) double-threshold detection algorithm was implemented and tested with different features. Feature selection was done by evaluation of signal-to-noise-ratio (SNR), onset sensitivity and precision, as well as timing error and deviation. Results of visual signal inspection by sEMG experts and kinematic signals were used as references. Overall, a CFAR algorithm with Teager-Kaiser-Energy-Operator (TKEO) as feature showed the best results with feature SNR = 14.48 dB, 91% sensitivity, 93% precision. In average, sEMG analysis hinted towards impending movements 215 ms before measurable kinematic changes.
Characterization of Gelatin-Polycaprolactone Membranes by Electrospinning
Authors: Rodríguez-Martín Manuel , Aguilar José Manuel , Castro-Criado Daniel , Romero Alberto
Published: January 2024

New advances in materials science and medicine have enabled the development of new and increasingly sophisticated biomaterials. One of the most widely used biopolymers is polycaprolactone (PCL) because it has properties suitable for biomedical applications, tissue engineering scaffolds, or drug delivery systems. However, PCL scaffolds do not have adequate bioactivity, and therefore, alternatives have been studied, such as mixing PCL with bioactive polymers such as gelatin, to promote cell growth. Thus, this work will deal with the fabrication of nanofiber membranes by means of the electrospinning technique using PCL-based solutions (12 wt.% and 20 wt.%) and PCL with gelatin (12 wt.% and 8 wt.%, respectively). Formic acid and acetic acid, as well as mixtures of both in different proportions, have been used to prepare the preliminary solutions, thus supporting the electrospinning process by controlling the viscosity of the solutions and, therefore, the size and uniformity of the fibers. The physical properties of the solutions and the morphological, mechanical, and thermal properties of the membranes were evaluated. Results demonstrate that it is possible to achieve the determined properties of the samples with an appropriate selection of polymer concentrations as well as solvents.
A Novel Hybrid Deep Learning Method for Predicting the Flow Fields of Biomimetic Flapping Wings
Authors: Hu Fujia , Tay Weebeng , Zhou Yilun , Khoo Boocheong
Published: January 2024

The physics governing the fluid dynamics of bio-inspired flapping wings is effectively characterized by partial differential equations (PDEs). Nevertheless, the process of discretizing these equations at spatiotemporal scales is notably time consuming and resource intensive. Traditional PDE-based computations are constrained in their applicability, which is mainly due to the presence of numerous shape parameters and intricate flow patterns associated with bionic flapping wings. Consequently, there is a significant demand for a rapid and accurate solution to nonlinear PDEs, to facilitate the analysis of bionic flapping structures. Deep learning, especially physics-informed deep learning (PINN), offers an alternative due to its great nonlinear curve-fitting capability. In the present work, a hybrid coarse-data-driven physics-informed neural network model (HCDD-PINN) is proposed to improve the accuracy and reliability of predicting the time evolution of nonlinear PDEs solutions, by using an order-of-magnitude-coarser grid than traditional computational fluid dynamics (CFDs) require as internal training data. The architecture is devised to enforce the initial and boundary conditions, and incorporate the governing equations and the low-resolution spatiotemporal internal data into the loss function of the neural network, to drive the training. Compared to the original PINN with no internal data, the training and predicting dynamics of HCDD-PINN with different resolutions of coarse internal data are analyzed on the problem relevant to the two-dimensional unsteady flapping wing, which involves unsteady flow features and moving boundaries. Additionally, a hyper-parametrical study is conducted to obtain an optimal model for the problem under consideration, which is then utilized for investigating the effects of the snapshot and fraction of the coarse internal data on the HCDD-PINN’s performances. The results show that the proposed framework has a sufficient stability and accuracy for solving the considered biomimetic flapping-wing problem, and its great potential means that it can be considered as an alternative to accelerate or replace traditional CFD solvers in the future. The interested variables of the flow field at any instant can be rapidly obtained by the trained HCDD-PINN model, which is superior to the traditional CFD method that usually needs to be re-run. For the three-dimensional and optimization problems of flapping wings, the advantages of the proposed method are supposedly even more apparent.
Interplay of Ferroptosis and Cuproptosis in Cancer: Dissecting Metal-Driven Mechanisms for Therapeutic Potentials
Authors: Wang Jinjiang , Li Jiaxi , Liu Jiao , Chan Kit-Ying , Lee Ho-Sze , Lin Kenneth Nansheng , Wang Chi-Chiu , Lau Tat-San
Published: January 2024

Iron (Fe) and copper (Cu), essential transition metals, play pivotal roles in various cellular processes critical to cancer biology, including cell proliferation, mitochondrial respiration, distant metastases, and oxidative stress. The emergence of ferroptosis and cuproptosis as distinct forms of non-apoptotic cell death has heightened their significance, particularly in connection with these metal ions. While initially studied separately, recent evidence underscores the interdependence of ferroptosis and cuproptosis. Studies reveal a link between mitochondrial copper accumulation and ferroptosis induction. This interconnected relationship presents a promising strategy, especially for addressing refractory cancers marked by drug tolerance. Harnessing the toxicity of iron and copper in clinical settings becomes crucial. Simultaneous targeting of ferroptosis and cuproptosis, exemplified by the combination of sorafenib and elesclomol-Cu, represents an intriguing approach. Strategies targeting mitochondria further enhance the precision of these approaches, providing hope for improving treatment outcomes of drug-resistant cancers. Moreover, the combination of iron chelators and copper-lowering agents with established therapeutic modalities exhibits a synergy that holds promise for the augmentation of anti-tumor efficacy in various malignancies. This review elaborates on the complex interplay between ferroptosis and cuproptosis, including their underlying mechanisms, and explores their potential as druggable targets in both cancer research and clinical settings.
Revascularization of Non-Vital, Immature, Permanent Teeth with Two Bioceramic Cements: A Randomized Controlled Trial
Authors: Shaker Alaa , Rekab Mohamed Salem , Alharissy Mohammad , Kharouf Naji
Published: January 2024

The aim of this study was to clinically and radiographically assess the effects of two bioceramic cements as coronal plug materials for the revascularization of non-vital, immature, permanent teeth with apical periodontitis. Twenty non-vital, immature, permanent, anterior teeth with apical periodontitis were included in this study. Samples were randomly divided into two groups based on the coronal plug materials used; group I (GI): Well Root PT (Vericom, Gangwon-do, Korea) and group II (GII): MTA Biorep (Itena Clinical, Paris, France) (n = 10). Follow-up was conducted clinically and radiographically for up to 12 months to evaluate the changes in root dimensions and resolution of the periapical disease and investigate the degree of success. Data were statistically analyzed using ANOVA tests and Mann–Whitney U tests at a 0.05 significance level. At 12 months, none of the patients in either test group had clinical signs/symptoms. There was a slight increase in root length (4.4% in GI and 3.4% in GII) and a slight increase in dentin wall thickness (10.2% in GI and 9.9% in GII) with no statistically significant (p > 0.05) differences. Whilst there was a significant decrease in lesion dimension in all patients after 12 moths of treatment (93% in GI and 91% in GII), there were no statistically significant differences between the two study groups (p > 0.05). Regarding the degree of success, all cases (100%) achieved the primary goal (the elimination of symptoms and the evidence of bony healing), while 60% in GI and 40% in GII achieved the secondary goal (increased root wall thickness and/or increased root length). No statistically significant difference was found between groups (p > 0.05). Both bioceramic cements showed satisfactory clinical and radiographic findings. Premixed calcium aluminosilicate and tricalcium silicate cements could be used as coronal plug materials in revascularization procedures. Bioceramic cements can be considered promising coronal plug materials for the revascularization of immature, permanent teeth with necrotic pulps and apical periodontitis due to their physicochemical and biological properties.
Investigating Facilitators and Barriers for Active Breaks among Secondary School Students: Formative Evaluation of Teachers and Students
Authors: Masini Alice , Longo Giulia , Ricci Matteo , Scheier Lawrence M. , Sansavini Alessandra , Ceciliani Andrea , Dallolio Laura
Published: January 2024

Physical activity in the form of “active breaks” can be combined with academic instruction in primary school. However, few studies have examined the feasibility of conducting active breaks in secondary school. To address this gap, we conducted focus groups (FGs) regarding the implementation of an active breaks (ABs) protocol with 20 teachers and 10 secondary school students. Barriers/facilitators toward the implementation of ABs were classified using grounded theory inductive methods framed by the socio-ecological model. Individual-level factors were instrumental for both teachers and students. Teachers highlighted personal fears and concerns regarding using ABs, while students reported fears related to peer behaviour during the activity. Both teachers and students agreed that ABs can improve cognitive skills and time-on-task behaviour. Teachers articulated concerns related to student behaviour during ABs including possible social exclusion and injury. Students felt that ABs might affect classroom management and interfere with maintaining students’ academic focus. Teachers underscored that ABs required social support from the administration and colleagues. Students felt that ABs could support teachers’ instructional focus and provide them with an energy respite. Collectively, the FGs suggested that environmental limitations could hinder the implementation of ABs. Involving teacher and student feedback during the codesign phase can rationally inform the design of school-based ABs.
Molecular Analysis of HLA Genes in Romanian Patients with Chronic Hepatitis B Virus Infection
Authors: Tălăngescu Adriana , Calenic Bogdan , Mihăilescu Dan Florin , Tizu Maria , Marunțelu Ion , Constantinescu Alexandra E. , Constantinescu Ileana
Published: January 2024

Hepatitis B, a persistent inflammatory liver condition, stands as a significant global health issue. In Romania, the prevalence of chronic hepatitis B virus (CHB) infection ranks among the highest in the European Union. The HLA genotype significantly impacts hepatitis B virus infection progression, indicating that certain HLA variants can affect the infection’s outcome. The primary goal of the present work is to identify HLA alleles and specific amino acid residues linked to hepatitis B within the Romanian population. The study enrolled 247 patients with chronic hepatitis B; HLA typing was performed using next-generation sequencing. This study’s main findings include the identification of certain HLA alleles, such as DQB1*06:03:01, DRB1*13:01:01, DQB1*06:02:01, DQA1*01:03:01, DRB5*01:01:01, and DRB1*15:01:01, which exhibit a significant protective effect against HBV. Additionally, the amino acid residue alanine at DQB1_38 is associated with a protective role, while valine presence may signal an increased risk of hepatitis B. The present findings are important in addressing the urgent need for improved methods of diagnosing and managing CHB, particularly when considering the disease’s presence in diverse population groups and geographical regions.
Antidiabetic Effect of Urolithin A in Cultured L6 Myotubes and Type 2 Diabetic Model KK-Ay/Ta Mice with Glucose Intolerance
Authors: Kondo Shinji , Adachi Shin-ichi , Komatsu Wataru , Yoshizawa Fumiaki , Yagasaki Kazumi
Published: January 2024

Diabetes is caused by abnormal glucose metabolism, and muscle, the largest tissue in the human body, is largely involved. Urolithin A (UroA) is a major intestinal and microbial metabolite of ellagic acid and ellagitannins and is found in fruits such as strawberry and pomegranate. In this present study, we investigated the antidiabetic effects of UroA in L6 myotubes and in KK-Ay/Ta, a mouse model of type 2 diabetes (T2D). UroA treatment elevated the glucose uptake (GU) of L6 myotubes in the absence of insulin. This elevation in GU by UroA treatment was partially inhibited by the concurrent addition of LY294002, an inhibitor of phosphatidylinositol 3-kinase (PI3K) which activates Akt (PKB: protein kinase B) or Compound C, an inhibitor of 5′-adenosine monophosphate-activated protein kinase (AMPK). Moreover, UroA was found to activate both pathways of Akt and AMPK, and then to promote translocation of glucose transporter 4 (GLUT4) from the cytosol to the plasma membrane in L6 myotubes. Based on these in vitro findings, an intraperitoneal glucose tolerance test (IPGTT) was performed after the oral administration of UroA for 3 weeks to KK-Ay/Ta mice with glucose intolerance. UroA was demonstrated to alleviate glucose intolerance. These results suggest that UroA is a biofactor with antihyperglycemic effects in the T2D state.
Disruption of the Functional Activity of Neutrophil Granulocytes as a Risk Factor for the Development of Lung Damage in Pregnant Women with COVID-19
Authors: Andrievskaya Irina Anatolyevna , Ustinov Egor Mikhailovich , Lyazgian Karen Sargisovich , Ishutina Nataliya Alexandrovna , Dovzhikova Inna Victorovna
Published: January 2024

Currently, the assessment of immune status in patients with COVID-19 is limited to determining the count of polymorphonuclear leukocytes and the phagocytic function of neutrophils, which is insufficient to understand the regulatory role of innate immunity cells in the development of pneumonia. However, no such studies have been conducted in pregnant women with COVID-19. The aim of this study was to investigate the functional state of neutrophil granulocytes in order to identify predictors of pneumonia severity risk in pregnant women with COVID-19. A clinical characterization of pregnant women with COVID-19 in addition to minimal and average lung changes was provided. The composition and ratio of morphological forms of leukocyte cells were studied. Cytochemical studies of neutrophil granulocytes were carried out and calculations of the mean cytological index (MCI) for succinate dehydrogenase, myeloperoxidase, and cationic proteins were performed. The number of NETs in blood smears was counted. Independent predictors of pneumonia severity in pregnant women with COVID-19 were calculated using regression analysis. The quality of the model was assessed using ROC analysis. In pregnant women with COVID-19 and an average volume of lung changes, the number of polymorphonuclear leukocytes (p = 0.03) and band neutrophils (p = 0.002) in the blood was significantly higher than in pregnant women with minimal lung changes. The MCI indicators of succinate dehydrogenase, cationic proteins, and myeloperoxidase in pregnant women with COVID-19 were reduced in relation to the control group (p < 0.0001). In blood smears of pregnant women with COVID-19 and an average volume of lung changes, the number of NETs increased (p = 0.002). Regression analysis showed that succinate dehydrogenase and NETs are independent predictors of pneumonia severity in pregnant women with COVID-19. Our study confirms the prognostic significance of low levels of neutrophilic succinate dehydrogenase and high levels of NETs in the blood of pregnant women with COVID-19. The combination of these two biomarkers is a significant reflection of the severity of pneumonia development in pregnant women with COVID-19. However, further research is needed to identify the mechanisms underlying this association.
The Effects of a Collegiate Recovery Community Psychotherapy Program Incorporating Equine Interaction during the COVID-19 Pandemic on Young Adults with Substance Abuse Disorder
Authors: Holtcamp Katie , Nicodemus Molly C. , Phillips Tommy , Christiansen David , Rude Brian J. , Ryan Peter L. , Galarneau Karen
Published: January 2024

While psychotherapy incorporating equine interaction (PIE) has proven to be a viable therapeutic intervention, it is not a common mental health service found on college campuses. Nevertheless, with the rise of mental health challenges on campuses after the COVID-19 pandemic, a need for effective therapeutic solutions is warranted. Therefore, the objective of this study was to determine the effect of a collegiate recovery community (CRC) PIE program for substance abuse disorder (SUD) compared to that of traditional cognitive behavioral therapy (CBT) and to determine whether physiological synchronization occurs between the human and horse during the therapy process. College-aged adults were recruited during the COVID-19 pandemic for two types of short-term SUD therapeutic interventions, CRC-PIE and CBT. Both groups completed a self-reporting survey assessing emotional safety. Vital signs measurements for human and horse participants within the CRC-PIE were collected prior to and after the first and last therapeutic sessions. Results concluded that although emotional safety did not improve significantly for PIE participants by the last therapy session (p = 0.85), emotional safety scores were significantly different between therapy types, with lower post-therapy scores for PIE (p = 0.04). As for physiological measures for PIE participants, respiratory rates (Human: p = 0.01; Horse: p = 0.01) and pain rating scores (Human: p = 0.03; Horse: p = 0.01) significantly decreased post-therapy and a strong positive correlation (R = 0.73, R2 = 0.53) associated with vital signs was observed between humans and horses. This human–horse physiological synchronization during the therapeutic intervention suggests that the horse may be a viable tool within campus CRC programs for the development of therapeutic alliances within the therapy process.
Phytoplankton Community Dynamics in Ponds with Diverse Biomanipulation Approaches
Authors: Zhang Yantao , Yang Jie , Lin Xiaoman , Tian Biao , Zhang Tanglin , Ye Shaowen
Published: January 2024

The rising challenge of eutrophication in aquatic systems globally necessitates an understanding of phytoplankton community dynamics under diverse biomanipulation approaches. This study, conducted from June 2022 to July 2023 in the Yuqiao Reservoir’s ponds in China, explored phytoplankton dynamics across ponds under different biomanipulation strategies. The study included a pond (BL) without fish stocking, a pond (CH) stocked with carnivorous and herbivorous fish, and another pond (CFD) incorporating a mix of carnivorous, filter-feeding, and detritus-feeding fish. Substantial seasonal variations in phytoplankton density and biomass were observed. In the BL pond, phytoplankton density ranged from 0.23 × 107 to 3.21 × 107 ind/L and biomass from 0.71 to 7.10 mg/L, with cyanobacteria predominantly in warmer seasons and a shift to cryptophytes and chrysophytes in winter. The CH pond exhibited a density range from 0.61 × 107 to 8.04 × 107 ind/L and biomass of 1.11 to 7.58 mg/L. Remarkably, the CFD pond demonstrated a significant reduction in both density (0.11 × 107 to 2.36 × 107 ind/L) and biomass (0.27 to 5.95 mg/L), indicating the effective implementation of its biomanipulation strategy. Key environmental factors including total nitrogen, water temperature, pH, chlorophyll-a, and total phosphorus played a significant role in shaping phytoplankton communities. The study highlights the importance of tailored biomanipulation strategies in aquatic ecosystem management, emphasizing long-term monitoring for sustainable management of eutrophication.
MHAiR: A Dataset of Audio-Image Representations for Multimodal Human Actions
Authors: Shaikh Muhammad Bilal , Chai Douglas , Islam Syed Mohammed Shamsul , Akhtar Naveed
Published: January 2024

Audio-image representations for a multimodal human action (MHAiR) dataset contains six different image representations of the audio signals that capture the temporal dynamics of the actions in a very compact and informative way. The dataset was extracted from the audio recordings which were captured from an existing video dataset, i.e., UCF101. Each data sample captured a duration of approximately 10 s long, and the overall dataset was split into 4893 training samples and 1944 testing samples. The resulting feature sequences were then converted into images, which can be used for human action recognition and other related tasks. These images can be used as a benchmark dataset for evaluating the performance of machine learning models for human action recognition and related tasks. These audio-image representations could be suitable for a wide range of applications, such as surveillance, healthcare monitoring, and robotics. The dataset can also be used for transfer learning, where pre-trained models can be fine-tuned on a specific task using specific audio images. Thus, this dataset can facilitate the development of new techniques and approaches for improving the accuracy of human action-related tasks and also serve as a standard benchmark for testing the performance of different machine learning models and algorithms.
Overview of Identified Challenges in the Development Process of Superconducting Accelerator Magnets
Authors: Kaeske Jens , Fiscarelli Lucio , Albers Albert , Russenschuck Stephan
Published: January 2024

Development challenges in the domain of superconducting magnets are concentrated on technical problems in the current literature. Organizational, domain-specific challenges are often seen as secondary but must be considered with new holistic development approaches like Model-Based Systems Engineering (MBSE) becoming more popular. This work quantifies the domain challenges and gives the foundation to derive success criteria for design support in the future. A systematic literature review has been conducted to identify the overall domain challenges, and extensive interviews in the CERN technology department have been carried out to identify the development challenges on a practical level. Problems in knowledge management have been identified as a major challenge in the development process and the general literature. The paper concludes by picking up the most important challenges from the interviews and literature and puts them into the context of the authors’ knowledge of electrical magnet design.
Radiometric Improvement of Spectral Indices Using Multispectral Lightweight Sensors Onboard UAVs
Authors: Andrés-Anaya Paula , Molada-Tebar Adolfo , Hernández-López David , Moreno Miguel Ángel , González-Aguilera Diego , Herrero-Huerta Mónica
Published: January 2024

Close-range remote sensing techniques employing multispectral sensors on unoccupied aerial vehicles (UAVs) offer both advantages and drawbacks in comparison to traditional remote sensing using satellite-mounted sensors. Close-range remote sensing techniques have been increasingly used in the field of precision agriculture. Planning the flight, including optimal flight altitudes, can enhance both geometric and temporal resolution, facilitating on-demand flights and the selection of the most suitable time of day for various applications. However, the main drawbacks stem from the lower quality of the sensors being used compared to satellites. Close-range sensors can capture spectral responses of plants from multiple viewpoints, mitigating satellite remote sensing challenges, such as atmospheric interference, while intensifying issues such as bidirectional reflectance distribution function (BRDF) effects due to diverse observation angles and morphological variances associated with flight altitude. This paper introduces a methodology for achieving high-quality vegetation indices under varied observation conditions, enhancing reflectance by selectively utilizing well-geometry vegetation pixels, while considering factors such as hotspot, occultation, and BRDF effects. A non-parametric ANOVA analysis demonstrates significant statistical differences between the proposed methodology and the commercial photogrammetric software AgiSoft Metashape, in a case study of a vineyard in Fuente-Alamo (Albacete, Spain). The BRDF model is expected to substantially improve vegetation index calculations in comparison to the methodologies used in satellite remote sensing and those used in close-range remote sensing.
Particle Physics and Cosmology Intertwined
Authors: Nath Pran
Published: January 2024

While the standard model accurately describes data at the electroweak scale without the inclusion of gravity, beyond the standard model, physics is increasingly intertwined with gravitational phenomena and cosmology. Thus, the gravity-mediated breaking of supersymmetry in supergravity models leads to sparticle masses, which are gravitational in origin, observable at TeV scales and testable at the LHC, and supergravity also provides a candidate for dark matter, a possible framework for inflationary models and for models of dark energy. Further, extended supergravity models and string and D-brane models contain hidden sectors, some of which may be feebly coupled to the visible sector, resulting in heat exchange between the visible and hidden sectors. Because of the couplings between the sectors, both particle physics and cosmology are affected. The above implies that particle physics and cosmology are intrinsically intertwined in the resolution of essentially all of the cosmological phenomena, such as dark matter and dark energy, and in the resolution of cosmological puzzles, such as the Hubble tension and the EDGES anomaly. Here, we give a brief overview of the intertwining and its implications for the discovery of sparticles, as well as the resolution of cosmological anomalies and the identification of dark matter and dark energy as major challenges for the coming decades.
ICT Scalability and Replicability Analysis for Smart Grids: Methodology and Application
Authors: Rodríguez-Pérez Néstor , Matanza Domingo Javier , López López Gregorio
Published: January 2024

The essential role of Information and Communication Technologies (ICT) in modern electricity grids makes it necessary to consider them when evaluating the scalability and replicability capabilities of smart grid systems. This paper proposes a novel step-by-step methodology to quantitatively perform an ICT scalability and replicability analysis (SRA) in a smart grid context. The methodology is validated and exemplified by applying it to two real case studies that are demonstrated in the EU-funded RESPONSE project and comprise solutions relying on different communication technologies. The results of the proposed methodology are summarised through ICT scalability and replicability maps, which are introduced in this paper as a quick way of obtaining an overview of the scalability and replicability capabilities of an ICT system and as an efficient way of estimating the feasibility of scenarios not covered in the SRA.
Classification and Parametric Analysis of Solar Hybrid PVT System: A Review
Authors: Ahmed Md , Rashel Masud , Islam Mahmudul , Islam A. , Tlemcani Mouhaydine
Published: January 2024

A Hybrid Photovoltaic Thermal (PVT) system is one of the most emerging and energy-efficient technologies in the area of solar energy engineering. This review paper provides a comprehensive review of hybrid PVT systems in the context of the history of PVT, general classification, and parameter analysis. Several cell technologies with spectrum analysis are discussed to understand the application’s ability and energy efficiency. Hybrid PVT concept, characteristics, and structure analysis is also discussed in this study. An extensive analysis on the classifications of hybrid PVT systems from the recent literature is also presented here. These literatures are identified based on several criteria. In order to provide a complete and energy-efficient technology, an innovative classification of the hybrid PVT system is proposed in this paper. This proposed classification is a combination and upgrade of various existing classifications mentioned in recent research studies. Parameters have a significant and unavoidable impact on the performance and efficiency of the hybrid PVT system. A brief analysis of different parameters and the optimization of the system is conducted after reviewing recent research articles. This analysis provides insights into the impact of parameter variations on the system. A novel parameter model comprising parametric and optimistic analyses is also presented in this paper. It provides a detailed parametric description that significantly affects the performance and efficiency of the hybrid PVT system. Finally, the assessment focuses on a critical analysis of the main challenges in adopting PVT technology and suggests ways to overcome these barriers.
Development of Rock Classification Systems: A Comprehensive Review with Emphasis on Artificial Intelligence Techniques
Authors: Niu Gang , He Xuzhen , Xu Haoding , Dai Shaoheng
Published: January 2024

At the initial phases of tunnel design, information on rock properties is often limited. In such instances, the engineering classification of the rock is recommended as a primary assessment of its geotechnical condition. This paper reviews different rock mass classification methods in the tunnel industry. First, some important considerations for the classification of rock are discussed, such as rock quality designation (RQD), uniaxial compressive strength (UCS) and groundwater condition. Traditional rock classification methods are then assessed, including the rock structure rating (RSR), rock mass rating (RMR), rock mass index (RMI), geological strength index (GSI) and tunnelling quality index (Q system). As RMR and the Q system are two commonly used methods, the relationships between them are summarized and explored. Subsequently, we introduce the detailed application of artificial intelligence (AI) method on rock classification. The advantages and limitations of traditional methods and artificial intelligence (AI) methods are indicated, and their application scopes are clarified. Finally, we provide suggestions for the selection of rock classification methods and prospect the possible future research trends.
Recycling, Material Flow, and Recycled Content Demands of Polyethylene Terephthalate (PET) Bottles towards a Circular Economy in Korea
Authors: Lee Chonghee , Jang Yong-Chul , Choi Kyunghoon , Kim Byeonghwan , Song Hakyun , Kwon Youngsun
Published: January 2024

The recycling and proper management of PET bottles is needed to avoid plastic pollution, as well as to achieve a plastic circular economy. In this study, we examined the recycling system of PET bottles and their material flows by life cycle, from production to recycling and disposal under different scenarios with reduced consumption for PET bottles. In Korea, PET bottle recycling has been managed by the extended producer responsibility system since 2003. As of 2021, annual usage of PET bottles per capita in Korea was estimated to be approximately 6.5 kg. The recycling rate of PET bottles after collection was determined to be 79% in 2021. However, recycled PET (or r-PET) from mechanical recycling is mostly used in low-grade PET applications such as short fibers, bands, and sheets. More than 112 thousand tons of r-PET chips would be needed to meet the 30% recycled content requirement in PET bottles in 2030 by regulation. Thus, there is an urgent need for high-quality secondary raw materials (i.e., food-grade) by adopting advanced sorting and recycling technologies for PET bottles with recycled content. PET bottle-to-bottle recycling practices can ensure the production of high-quality recycled materials and move Korea toward a plastic circular economy.
A Holistic Analysis of Internet of Things (IoT) Security: Principles, Practices, and New Perspectives
Authors: Hossain Mahmud , Kayas Golam , Hasan Ragib , Skjellum Anthony , Noor Shahid , Islam S. M. Riazul
Published: January 2024

Driven by the rapid escalation of its utilization, as well as ramping commercialization, Internet of Things (IoT) devices increasingly face security threats. Apart from denial of service, privacy, and safety concerns, compromised devices can be used as enablers for committing a variety of crime and e-crime. Despite ongoing research and study, there remains a significant gap in the thorough analysis of security challenges, feasible solutions, and open secure problems for IoT. To bridge this gap, we provide a comprehensive overview of the state of the art in IoT security with a critical investigation-based approach. This includes a detailed analysis of vulnerabilities in IoT-based systems and potential attacks. We present a holistic review of the security properties required to be adopted by IoT devices, applications, and services to mitigate IoT vulnerabilities and, thus, successful attacks. Moreover, we identify challenges to the design of security protocols for IoT systems in which constituent devices vary markedly in capability (such as storage, computation speed, hardware architecture, and communication interfaces). Next, we review existing research and feasible solutions for IoT security. We highlight a set of open problems not yet addressed among existing security solutions. We provide a set of new perspectives for future research on such issues including secure service discovery, on-device credential security, and network anomaly detection. We also provide directions for designing a forensic investigation framework for IoT infrastructures to inspect relevant criminal cases, execute a cyber forensic process, and determine the facts about a given incident. This framework offers a means to better capture information on successful attacks as part of a feedback mechanism to thwart future vulnerabilities and threats. This systematic holistic review will both inform on current challenges in IoT security and ideally motivate their future resolution.
TinyML Algorithms for Big Data Management in Large-Scale IoT Systems
Authors: Karras Aristeidis , Giannaros Anastasios , Karras Christos , Theodorakopoulos Leonidas , Mammassis Constantinos S. , Krimpas George A. , Sioutas Spyros
Published: January 2024

In the context of the Internet of Things (IoT), Tiny Machine Learning (TinyML) and Big Data, enhanced by Edge Artificial Intelligence, are essential for effectively managing the extensive data produced by numerous connected devices. Our study introduces a set of TinyML algorithms designed and developed to improve Big Data management in large-scale IoT systems. These algorithms, named TinyCleanEDF, EdgeClusterML, CompressEdgeML, CacheEdgeML, and TinyHybridSenseQ, operate together to enhance data processing, storage, and quality control in IoT networks, utilizing the capabilities of Edge AI. In particular, TinyCleanEDF applies federated learning for Edge-based data cleaning and anomaly detection. EdgeClusterML combines reinforcement learning with self-organizing maps for effective data clustering. CompressEdgeML uses neural networks for adaptive data compression. CacheEdgeML employs predictive analytics for smart data caching, and TinyHybridSenseQ concentrates on data quality evaluation and hybrid storage strategies. Our experimental evaluation of the proposed techniques includes executing all the algorithms in various numbers of Raspberry Pi devices ranging from one to ten. The experimental results are promising as we outperform similar methods across various evaluation metrics. Ultimately, we anticipate that the proposed algorithms offer a comprehensive and efficient approach to managing the complexities of IoT, Big Data, and Edge AI.