bluuebunny/crossref_metadata_2025 huggingface dataset:
Variation in the nutrient content of different genotypes and varieties of millets, studied globally: a systematic review
Authors: Anitha Seetha , Rajendran Ananthan , Botha Rosemary , Baruah Chandrama , Mer Pooja , Sebastian Julia , Upadhyay Shweta , Kane-Potaka Joanna
Published: February 2024

This study was conducted to understand the variation in the nutrient contents of different types of millets by collecting data from published scientific journals and collating it by variety. The data is analyzed as a whole and as a subset, where it is clearly categorized into a released variety or genotype/accession. Calcium level was consistently high in finger millet and teff regardless of varieties at 331.29 ± 10 mg/100 g and 183.41 ± 29 mg/100 g, respectively. Iron content was highest for finger millet at 12.21 ± 13.69 mg/100 g followed by teff at 11.09 ± 8.35 mg/100 g. Pearl millet contained the highest zinc content of 8.73 ± 11.55 mg/100 g. Protein content was highest in job’s tears at 12.66 g/100 g followed by proso millet at 12.42 ± 1.99 g/100 g and barnyard millet with 12.05 ± 1.77 g/100 g. Some millets showed consistently low or consistently high levels of specific nutrients, while others had such wide variation that they could not be characterized as high or low for that particular nutrient. There is a huge variation in the nutrient content of each type of millet regardless of the released variety or genotype. In the interest of improving dietary nutrients, there is a need to have nutrition programs and product development based on selected high nutrient varieties of the millet, which requires attention from researchers and government and changes in research, policy, and awareness among the public and private sectors.
Impact of Potassium-Solubilizing Microorganisms with Potassium Sources on the Growth, Physiology, and Productivity of Wheat Crop under Salt-Affected Soil Conditions
Authors: El-Egami Hend Mostafa , Hegab Rehab H. , Montaser Heba , El-Hawary Mohammed Mohammed , Hasanuzzaman Mirza
Published: February 2024

Salinity adversely affects plant growth and productivity worldwide. To overcome salinity and other abiotic stresses, safe, ecofriendly biofertilizers that stimulate productivity have been experiencing rising demand, alongside decreasing use of mineral fertilizers. The purpose of this study was to examine changes in the growth, yield, physiological and biochemical parameters of wheat crop as a response to two potassium-solubilizing microorganisms (KSMs), Trichoderma asperellum and Bacillus circulans, with 50% or 75% of the recommended amount of K fertilizer (potassium sulphate), combined with no or 25% feldspar as well as 1.5% potassium sulphate (K-leaf) as foliar application, and all treatments were compared with a control treatment under salt-affected soil conditions, during two winter seasons in 2019–2020 and 2020–2021. The randomized complete block design (RCBD) was used to set up the experiment. Results showed that the vegetative growth, yield, physiological and biochemical parameters were affected under salt-affected soil conditions. Nevertheless, inoculation with T. asperellum and B. circulans with potassium application mitigated the deleterious effect of salt-affected soil conditions by improving growth parameters, photosynthetic pigment content, antioxidant enzymes (superoxide dismutase, ascorbate peroxidase, catalase and peroxidase) content, total soluble sugars, relative water content, potassium content in leaves, grains yield, and some biochemical constituents in the grains and straw. Meanwhile, these treatments decreased proline content, Na+ content in leaves, and the Na+/K+ ratio as compared to the control treatment. The most pronounced treatment, inoculation by B. circulans with 1.5% K-leaf as foliar application, significantly increased grain yield by 16.41% relative to the control treatment. It could be concluded that inoculating wheat with KSMs T. asperellum and B. circulans with 1.5% K-leaf as foliar application will increase wheat salinity tolerance and wheat productivity and decrease the detrimental effect of salinity on wheat growth and grain yield quantity and quality under salt-affected soils conditions.
Evaluation of Soil Total Nitrogen as an Indicator of Soil Bacterial Community Response to Biochar and Plant Growth-Promoting Rhizobacteria Applications
Authors: Ding Chenke , Lv Chengqun , Chen Hualin , Zhou Jiangmin , Ren Han
Published: February 2024

Biochar and plant growth-promoting rhizobacteria (PGPR) are widely used as an amendment for soil physicochemical properties and soil bacterial community diversity. In Guangxi, China, we carried out a study to determine how PGPR and biochar influence the soil’s environmental stability in an Eucalypt plantation. We applied biochar and PGPR in a contrasting application manner to an acidic red loam soil. Thus, three treatments were set up as 5 × 1010 CFU·mL−1 PGPR-only (MB0), 20 t·hm−2 biochar-only (B20), and co-application of 20 t·hm−2 biochar and 5 × 1010 CFU·mL−1 PGPR (MB20), as well as no biochar and no PGPR (M0B0). Our results indicated that MB20 significantly decreased the soil total nitrogen (TN) and increased the soil total phosphorus (Soil TP), soil ammonium nitrogen (NH4+), and soil water content (SWC) when compared with the control. The MB20 also significantly increased the Simpson, ACE, and Chao indices of the soil bacterial community’s diversity relative to the control. We also observed a significant effect of the Soil TN on both the bacterial community and the functional diversity in soil. These findings may indicate that assessing the soil N status is expected to be an essential indicator of the soil microenvironment’s response to biochar and PGPR applications.
Evaluation of the Potential of Chitosan Nanoparticles as a Delivery Vehicle for Gentamicin for the Treatment of Osteomyelitis
Authors: Simpson Elliot , Sarwar Humera , Jack Iain , Lowry Deborah
Published: February 2024

Chitosan nanoparticle delivery systems have the potential for enhancing bone healing and addressing osteomyelitis. The objective was to deliver antimicrobial agents capable of preventing or treating osteomyelitis. Each formulation was optimized to achieve desired characteristics in terms of size (ranging from 100 to 400 nm), PDI (less than 0.5), zeta potential (typically negative), and in vitro release profiles for gentamicin. Entrapment percentages varied with gentamicin ranging from 10% to 65%. The chitosan nanoparticles exhibited substantial antimicrobial efficacy, particularly against P. aeruginosa and MRSA, with zones of inhibition ranging from 13 to 24 mm and a complete reduction in colony forming units observed between 3 and 24 h. These chitosan nanoparticle formulations loaded with antimicrobials hold promise for addressing orthopedic complications.
Cysteine Is the Only Universally Affected and Disfavored Proteomic Amino Acid under Oxidative Conditions in Animals
Authors: Schindeldecker Mario , Moosmann Bernd
Published: February 2024

Oxidative modifications of amino acid side chains in proteins are a hallmark of oxidative stress, and they are usually regarded as structural damage. However, amino acid oxidation may also have a protective effect and may serve regulatory or structural purposes. Here, we have attempted to characterize the global redox role of the 20 proteinogenic amino acids in animals by analyzing their usage frequency in 5 plausible evolutionary paradigms of increased oxidative burden: (i) peroxisomal proteins versus all proteins, (ii) mitochondrial proteins versus all proteins, (iii) mitochondrially encoded respiratory chain proteins versus all mitochondrial proteins, (iv) proteins from long-lived animals versus those from short-lived animals, and (v) proteins from aerobic, free-living animals versus those from facultatively anaerobic animals. We have found that avoidance of cysteine in the oxidative condition was the most pronounced and significant variation in the majority of comparisons. Beyond this preeminent pattern, only local signals were observed, primarily increases in methionine and glutamine as well as decreases in serine and proline. Hence, certain types of cysteine oxidation appear to enforce its proteome-wide evolutionary avoidance despite its essential role in disulfide bond formation and metal ligation. The susceptibility to oxidation of all other amino acids appears to be generally unproblematic, and sometimes advantageous.
Robotic Stereotactic Body Radiotherapy for Spine Metastasis Pain Relief
Authors: Rivas Daniel , de la Torre-Luque Alejandro , Suárez Vladimir , García Rafael , Fernández Castalia , Gonsalves Daniela , Moreno-Olmedo Elena , Núñez María Isabel , López Escarlata
Published: February 2024

Spinal metastasis may occur in 40–70% of patients with cancer. Symptoms can vary from pain to spinal cord compression (SCC) and can affect their quality of life (QoL). Stereotactic body radiotherapy (SBRT) allows dose escalation of spinal tumor metastases, minimizing doses to organs at risk and improving pain control. The aim of this study is to retrospectively describe our institution’s experience with robotic SBRT (CyberKnife®, Accuray Incorporated, Sunnyvale, CA, USA) for spinal metastases, in terms of feasibility, oncological results, toxicities, and pain relief observed. In total, 25 patients with 43 lesions were assessed, most of them with dorsal metastases (48.8%). The median total dose was 27 Gy (16–35 Gy), the median number of fractions administered was 3 (1–5), and the median dose per fraction was 9 Gy. Pain was evaluated using the visual analogue scale at baseline and at the end of treatment. The statistically significant reduction in pain (p < 0.01) was associated with the total dose of radiotherapy delivered (p < 0.01). Only one patient developed grade 3 dermatitis. Female gender, adenocarcinoma tumors, and lack of previous surgery were associated with better response to SBRT (p < 0.05). Robotic spine SBRT is feasible, well-tolerated, and improves patients’ QoL through a statistically significant reduction in pain, so it should be offered to patients at an early stage in their process.
A Pavement Crack Detection Method via Deep Learning and a Binocular-Vision-Based Unmanned Aerial Vehicle
Authors: Zhang Jiahao , Xia Haiting , Li Peigen , Zhang Kaomin , Hong Wenqing , Guo Rongxin
Published: February 2024

This study aims to enhance pavement crack detection methods by integrating unmanned aerial vehicles (UAVs) with deep learning techniques. Current methods encounter challenges such as low accuracy, limited efficiency, and constrained application scenarios. We introduce an innovative approach that employs a UAV equipped with a binocular camera for identifying pavement surface cracks. This method is augmented by a binocular ranging algorithm combined with edge detection and skeleton extraction algorithms, enabling the quantification of crack widths without necessitating a preset shooting distance—a notable limitation in existing UAV crack detection applications. We developed an optimized model to enhance detection accuracy, incorporating the YOLOv5s network with an Efficient Channel Attention (ECA) mechanism. This model features a decoupled head structure, replacing the original coupled head structure to optimize detection performance, and utilizes a Generalized Intersection over Union (GIoU) loss function for refined bounding box predictions. Post identification, images within the bounding boxes are segmented by the Unet++ network to accurately quantify cracks. The efficacy of the proposed method was validated on roads in complex environments, achieving a mean Average Precision (mAP) of 86.32% for crack identification and localization with the improved model. This represents a 5.30% increase in the mAP and a 6.25% increase in recall compared to the baseline network. Quantitative results indicate that the measurement error margin for crack widths was 10%, fulfilling the practical requirements for pavement crack quantification.
Influence of Particle Size on Compressed Earth Blocks Properties and Strategies for Enhanced Performance
Authors: Turco Chiara , Paula Junior Adilson , Jacinto Cláudia , Fernandes Jorge , Teixeira Elisabete , Mateus Ricardo
Published: February 2024

In the context of sustainable building development, Compressed Earth Blocks (CEBs) have garnered increasing attention in recent years owing to their minimal environmental and economic impact. However, owing to the inherent diversity of raw soil and the production process’s reliance on expertise, the properties of these blocks are subjected to multifaceted influences. Among these, the significance of soil particle size variation often remains overlooked, leaving its impact ambiguous. This study endeavours to address this gap in existing research by delving into this aspect. Two distinct batches of CEBs were produced by adjusting the grain size curve of a single type of sieved soil with different maximum mesh openings: 2 mm for R1 CEBs and 12.5 mm for R2 CEBs. Experimental results reveal significant differences in thermophysical characteristics: on average, R1 blocks show superior thermal performance, boasting a 23% reduction in thermal conductivity compared to R2 blocks, and are lighter, with an 8% decrease in dry bulk density. Although no significant changes in mechanical parameters were observed, finer-structured R1 blocks showed a 25% greater tendency to absorb water due to changes in their porous structure. This study sheds light on the sensitivity of thermal parameters to changes in soil particle size and shows that blocks with finer particles exhibit poorer heat conduction and heat diffusion. Besides providing new insights into the literature, this research also provides a strategic approach to optimise the thermophysical properties of CEBs. By understanding the influence of particle size, researchers and practitioners can now develop strategies to enhance these properties and improve the overall performance of CEBs.
Importance of Lactic Acid Bacteria as an Emerging Group of Plant Growth-Promoting Rhizobacteria in Sustainable Agroecosystems
Authors: Yaghoubi Khanghahi Mohammad , Strafella Sabrina , Filannino Pasquale , Minervini Fabio , Crecchio Carmine
Published: February 2024

Increasing awareness of the problems caused by synthetic agrochemicals, such as chemical fertilizers, pesticides, and herbicides, makes it crucial to discover substitute approaches that can guarantee competitive plant production and protect the environment while maintaining the natural balance in agroecosystems. One of the leading alternatives is utilizing rhizobacterial strains named plant growth-promoting rhizobacteria (PGPR). The utilization of PGPR-based biofertilizers for advancement in the sustainability of farming productions has received considerable critical attention all over the world because of their contribution to not only improving plant growth but also inducing biotic and abiotic stress tolerance. This review updates the aforementioned eco-friendly strategy in sustainable agroecosystems and provides new insights into the phytostimulation and bioprotection ability of lactic acid bacteria (LAB), an emerging taxon of PGPR. In this regard, the ability of LAB to synthesize metabolites, including organic acids, phenolic acids and their flavonoid derivatives, phytohormones, and antimicrobial substrates, is presented. The use of LAB provides a bridge between PGPR and environmentally friendly crop productivity, which can lead to sustainable production systems by reducing the use of agrochemicals, improving soil quality, and minimizing environmental pollution. All the beneficial aspects of LAB need to be addressed by future research to plan systematic methodologies for their use and/or to combine the use of PGPR along with other organic or inorganic inputs in sustainable production systems.
Early Fire Detection System by Using Automatic Synthetic Dataset Generation Model Based on Digital Twins
Authors: Kim Hyeon-Cheol , Lam Hoang-Khanh , Lee Suk-Hwan , Ok Soo-Yol
Published: February 2024

Fire is amorphous and occurs differently depending on the space, environment, and material of the fire. In particular, the early detection of fires is a very important task in preventing large-scale accidents; however, there are currently almost no learnable early fire datasets for machine learning. This paper proposes an early fire detection system optimized for certain spaces using a digital-twin-based automatic fire learning data generation model for each space. The proposed method first automatically generates realistic particle-simulation-based synthetic fire data on an RGB-D image matched to the view angle of a monitoring camera to build a digital twin environment of the real space. In other words, our method generates synthetic fire data according to various fire situations in each specific space and then performs transfer learning using a state-of-the-art detection model with these datasets and distributes them to AIoT devices in the real space. Synthetic fire data generation optimized for a space can increase the accuracy and reduce the false detection rate of existing fire detection models that are not adaptive to space.
Effect of PEF Treatment on Chosen Properties of Raw and Hot Air- and Freeze-Dried Poultry Meat
Authors: Nowacka Malgorzata , Szymanska Iwona , Rybak Katarzyna , Karwacka Magdalena , Matys Aleksandra , Wiktor Artur , Slowinski Miroslaw , Witrowa-Rajchert Dorota
Published: February 2024

The properties of fresh meat, such as high water activity, a slightly acidic reaction, and the availability of carbohydrates and proteins, make it a microbiologically unstable and easily perishable raw material. One of the oldest food preservation techniques is drying. Furthermore, non-thermal food processing techniques such as pulse electric field (PEF) treatment can be used to support the drying process and change the properties of the obtained product. Thus, this study aimed to analyze the impact of pulsed electric field treatment on the hot air-drying and freeze-drying of poultry meat as well as on the quality of the dried meat. The PEF pretreatment and drying methods significantly altered the physical characteristics of the poultry meat. The PEF treatment enhanced the efficiency of freeze-drying by electroporation, reducing drying time and shrinkage. However, in the hot air-drying, the PEF-treated samples prolonged drying, potentially due to muscle structure damage and increased shrinkage. The pretreatment techniques affected the structure of the meat and positively influenced the higher porosity and lower shrinkage. Also, drying decreased the water activity and increased the dry matter content, which ensured the safety of the final product. The freeze-dried material exhibited a higher rehydration rate, improved hygroscopic properties, and better meat color compared to the hot air-dried material. Nevertheless, the selection of the process parameters, for both the pretreatment and drying process, is crucial to ensure a high quality of the dried meat product and should be selected carefully in order to guarantee that the highest quality of the dried product is obtained.
Tree-Based Modeling for Large-Scale Management in Agriculture: Explaining Organic Matter Content in Soil
Authors: Lee Woosik , Lee Juhwan
Published: February 2024

Machine learning (ML) has become more prevalent as a tool used for biogeochemical analysis in agricultural management. However, a common drawback of ML models is the lack of interpretability, as they are black boxes that provide little insight into agricultural management. To overcome this limitation, we compared three tree-based models (decision tree, random forest, and gradient boosting) to explain soil organic matter content through Shapley additive explanations (SHAP). Here, we used nationwide data on field crops, soil, terrain, and climate across South Korea (n = 9584). Using the SHAP method, we identified common primary controls of the models, for example, regions with precipitation levels above 1400 mm and exchangeable potassium levels exceeding 1 cmol+ kg−1, which favor enhanced organic matter in the soil. Different models identified different impacts of macronutrients on the organic matter content in the soil. The SHAP method is practical for assessing whether different ML models yield consistent findings in addressing these inquiries. Increasing the explainability of these models means determining essential variables related to soil organic matter management and understanding their associations for specific instances.
Iterative Camera Calibration Method Based on Concentric Circle Grids
Authors: Wei Liang , Huo Ju , Yue Lin
Published: February 2024

A concentric circle target is commonly used in the vision measurement system for its detection accuracy and robustness. To enhance the camera calibration accuracy, this paper proposes an improved calibration method that utilizes concentric circle grids as the calibration target. The method involves accurately locating the imaged center and optimizing camera parameters. The imaged concentric circle center obtained by cross-ratio invariance is not affected by perspective projection, which ensures the location accuracy of the feature point. Subsequently, the impact of lens distortion on camera calibration is comprehensively investigated. The sub-pixel coordinates of imaged centers are taken into the iterative calibration method, and camera parameters are updated. Through simulations and real experiments, the proposed method effectively reduces the residual error and improves the accuracy of camera parameters.
Parental Burnout, Negative Parenting Style, and Adolescents’ Development
Authors: Guo Xingchen , Hao Chenyu , Wang Wei , Li Yongxin
Published: February 2024

Research on parental burnout has focused more on its antecedents than on its consequences. Burned-out parents may experience a series of behavioral changes, negatively affecting their children’s physical and mental development. This study examined the effects of primary caregivers’ parental burnout on adolescents’ development and the mediating role of negative parenting styles. This study used a time-lagged design, and data were collected at three different time points. Adolescents were asked to identify their primary caregivers, and parents were asked whether they were the primary caregivers of their children. Thereafter, paired data from the children and primary caregivers were collected. A total of 317 junior middle school students (178 boys, Mage = 14.20 ± 0.8 years) and primary caregivers (71 fathers, Mage = 42.20 ± 4.53 years) from Henan province participated. Primary caregivers’ parental burnout was positively associated with negative parenting styles, and negative parenting styles mediated the relationship between parental burnout and adolescent development. From the perspective of prevention-focused interventions, it is necessary to focus on preventing the occurrence of parental burnout. Further, parents should try to avoid using abusive behaviors toward their children and neglecting them.
An Investigation into the Effect of Near-Fault Ground Motion Duration Parameters on the Nonlinear Seismic Response of Intake Towers
Authors: Chen Xi , Zhou Binpeng , Liu Xiaoxiao , Lu Junlong
Published: February 2024

Intake towers, essential for hydraulic hub projects, are integral to maintaining safety and efficiency, especially under seismic conditions that are prevalent near fault zones. These structures are vital for the integrity of hydraulic hubs, effective hydropower generation, and downstream safety. The duration of seismic events notably influences the towers’ structural response. In light of China’s initiative to build numerous high dams and large reservoirs, understanding the interplay between seismic duration and the intake tower response is crucial. This study, utilizing a duration-dependent composite parameter method based on seismic intensity, investigates the impact of near-fault ground motion duration effects on the response characteristics of intake towers. It analyzes the correlation between three types of six duration parameters and six duration-related composite parameters with the nonlinear seismic response of the physical model of the intake tower structure, as well as the rapid seismic response of the simplified model. This study investigates the impact of near-fault ground motion duration on intake towers, which is crucial for hydraulic hub projects, particularly in seismic-prone areas like those targeted by China’s dam construction initiative. Utilizing a duration-dependent composite parameter method, the study establishes a strong correlation between seismic duration parameters and the nonlinear response of intake towers, emphasizing the significance of uniform duration-related composite intensity parameters for characterizing near-fault seismic motion features. The findings reveal a pronounced correlation between the elasto-plastic response of intake towers and consistent duration-related composite strength parameters, offering crucial insights for optimizing structural design and enhancing seismic response assessment, particularly in the realm of large-scale dam projects.
Implementation of Building Information Modeling Technologies in Wood Construction: A Review of the State of the Art from a Multidisciplinary Approach
Authors: Lobos Calquin Danny , Mata Ramón , Correa Claudio , Nuñez Eduardo , Bustamante Guillermo , Caicedo Natalia , Blanco Fernandez David , Díaz Marcos , Pulgar-Rubilar Pablo , Roa Leonardo
Published: February 2024

This research raises questions about the possibilities and options of using the BIM methodology associated with software for the wood design and construction of structure modeling along an asset’s cycle life. Likewise, several academic and research initiatives are reviewed. In this sense, this paper aims to establish an appropriate link between two agendas that the architecture, engineering, and construction (AEC) industry, academia, and governments normally handle separately. By conducting several literature reviews (book, journals, and congresses) and extensive software tests (BIM software: Revit v2023, Archicad v27, Tekla, and wood plug-ins: AGACAD, Archiframe, Timber Framing 2015, WoodStud Frame, etc.), the state-of-the-art was assessed in both fields, and several cases linking BIM and wood are shown in detail and discussed. Various theoretical samples are modelled and shown, and the advantages and disadvantages of each technique and stage are explained. On the other hand, although wood construction has been most common for hundreds of years, this is not the case of BIM software developments associated with this materiality. Furthermore, since the appearance of materials such as steel and reinforced concrete, all software developments have focused on these materials, leaving aside the possibility of developing applications for use in wood projects. According to that previously discussed, it can be concluded that BIM for wood has been used more frequently in academia, that both fields have several common processes, and, in many cases, that only a few BIM-wood tools have been used, thus disregarding the high potential and high level of benefits that result with the application of these methodologies for the complete building life cycle (design, construction, and operation).
Enzymes in Biomedical, Cosmetic and Food Application
Authors: Kuo Chia-Hung , Wang Hui-Min David , Shieh Chwen-Jen
Published: February 2024

Enzymes play an important role in biomedical, cosmetic and food applications, and their effects are mainly related to their specific reactions and catalytic activity [...]
Adaptive Dual Aggregation Network with Normalizing Flows for Low-Light Image Enhancement
Authors: Wang Hua , Cao Jianzhong , Huang Jijiang
Published: February 2024

Low-light image enhancement (LLIE) aims to improve the visual quality of images taken under complex low-light conditions. Recent works focus on carefully designing Retinex-based methods or end-to-end networks based on deep learning for LLIE. However, these works usually utilize pixel-level error functions to optimize models and have difficulty effectively modeling the real visual errors between the enhanced images and the normally exposed images. In this paper, we propose an adaptive dual aggregation network with normalizing flows (ADANF) for LLIE. First, an adaptive dual aggregation encoder is built to fully explore the global properties and local details of the low-light images for extracting illumination-robust features. Next, a reversible normalizing flow decoder is utilized to model real visual errors between enhanced and normally exposed images by mapping images into underlying data distributions. Finally, to further improve the quality of the enhanced images, a gated multi-scale information transmitting module is leveraged to introduce the multi-scale information from the adaptive dual aggregation encoder into the normalizing flow decoder. Extensive experiments on paired and unpaired datasets have verified the effectiveness of the proposed ADANF.
The c-Differential-Linear Connectivity Table of Vectorial Boolean Functions
Authors: Eddahmani Said , Mesnager Sihem
Published: February 2024

Vectorial Boolean functions and codes are closely related and interconnected. On the one hand, various requirements of binary linear codes are needed for their theoretical interests but, more importantly, for their practical applications (such as few-weight codes or minimal codes for secret sharing, locally recoverable codes for storage, etc.). On the other hand, various criteria and tables have been introduced to analyse the security of S-boxes that are related to vectorial Boolean functions, such as the Differential Distribution Table (DDT), the Boomerang Connectivity Table (BCT), and the Differential-Linear Connectivity Table (DLCT). In previous years, two new tables have been proposed for which the literature was pretty abundant: the c-DDT to extend the DDT and the c-BCT to extend the BCT. In the same vein, we propose extended concepts to study further the security of vectorial Boolean functions, especially the c-Walsh transform, the c-autocorrelation, and the c-differential-linear uniformity and its accompanying table, the c-Differential-Linear Connectivity Table (c-DLCT). We study the properties of these novel functions at their optimal level concerning these concepts and describe the c-DLCT of the crucial inverse vectorial (Boolean) function case. Finally, we draw new ideas for future research toward linear code designs.
Challenges Faced by Students with Special Needs in Primary Education during Online Teaching
Authors: Bachtsis Rafail , Perifanou Maria , Economides Anastasios A.
Published: February 2024

This study investigates the psychological, educational, and technological difficulties faced by primary education students with special needs during online teaching. An interpretative phenomenological analysis was used for the qualitative analysis of data obtained through semi-structured interviews with twenty-two (22) teachers in primary education at a European country. The results revealed that their students showed negative emotions and behaviour. Those diagnosed with autism and learning disabilities had difficulty concentrating in class, while those with sensory disabilities had epileptic instances. Students with mild mental retardation in particular found it difficult to use digital tools. Many problems, however, are due to the lack of infrastructure and digital skills, as well as proper preparation of teachers for online teaching. Therefore, students and teachers should be equipped with the necessary digital skills, specialised digital tools and accessible open educational resources (OER) in order to effectively participate in online education.
Development of Transdisciplinary and Complex Learning in Inclusive Educational Practices
Authors: Hernaiz-Agreda Nerea , Soto-González María Dolores , Rodríguez-López Ramona
Published: February 2024

This study examines the process of creating digital resources to meet the specific educational needs of primary education students at a school in the province of Valencia (Spain). The resources were developed by students of the Teaching program at the University of Valencia as part of the special educational needs subject. The research focuses on transdisciplinarity and the use of new technologies and sustainable development objectives to enrich the educational experience at the school as well as to study this approach with students pursuing a bachelor’s degree in Primary Education. To analyze students’ perceptions of this practice and compare their reflective and transdisciplinary thinking, a validated questionnaire was administered to a sample of 39 participants. In addition, seven focus groups were organized. The results demonstrate that via transdisciplinarity and the use of technological resources, students enhance their abilities to address complex situations, fostering creativity and innovation by relating different areas of knowledge in real and contextualized settings. Furthermore, an increase in reflective capacity and research skills is highlighted, contributing to better understanding and learning based on digital technologies.
Brief Mindset Intervention Changes Attitudes but Does Not Improve Working Memory Capacity or Standardized Test Performance
Authors: Holden LaTasha R. , Goldstein Bear
Published: February 2024

There has been extensive research conducted on mindset and grit, involving both experimental and observational methods. However, the findings in the literature remain mixed. This should give educators and researchers pause from an intervention perspective—if we still do not have a good understanding of how mindset works, then more research is needed. We implemented a mindset intervention with undergraduate women to improve cognitive performance measures relevant to academic performance—working memory capacity and standardized test performance in math. To better understand how mindset interventions work, we also examined self-report measures (e.g., pertaining to academic attitudes and belonging) as well as post-intervention behavior. We expected the growth mindset intervention to significantly improve cognitive performance and to cause more positive academic attitudes and attitudinal change. The mindset intervention did change students’ beliefs about ability and also caused students to report higher grit overall (no condition difference), and to feel less belonging in terms of connection to their university—which was not in line with our hypotheses. We also found that the growth mindset intervention had no significant effects on improving WMC or standardized test performance. We discuss the implications of these findings and make suggestions for future work in this area.
A Systematic Literature Review of the Impact of Cognitive Stimulation Programs on Reading Skills in Children Aged between 6 and 12 Years Old
Authors: Reina-Reina Claudia , Antón Eneko , Duñabeitia Jon Andoni
Published: February 2024

The scientific evidence regarding the possibility of transferring benefits derived from cognitive training focused on working memory and inhibitory control to reading skills in children aged 6 to 12 is inconclusive. This study carries out a systematic review of recent published studies on this topic with the aim of analysing the specific role of various cognitive stimulation programs in the growth of executive functions and reading performance in children from ages 6 to 12. Here, we present the main results reported in the most recent literature, where the impact of intervention programs on working memory and inhibitory control in children with typical development are analysed. Even though the effectiveness of executive function training programs in terms of close transfer is conspicuous, there is still a lack of convergence in recently published articles, especially regarding the effects of far transfer in reading comprehension after cognitive stimulation programs are applied.
A Staged Real-Time Ground Segmentation Algorithm of 3D LiDAR Point Cloud
Authors: Deng Weiye , Chen Xiaoping , Jiang Jingwei
Published: February 2024

Ground segmentation is a crucial task in the field of 3D LiDAR perception for autonomous driving. It is commonly used as a preprocessing step for tasks such as object detection and road extraction. However, the existing ground segmentation algorithms often struggle to meet the requirements of robustness and real-time performance due to significant variations in ground slopes and flatness across different scenes, as well as the influence of objects such as grass, flowerbeds, and trees in the environment. To address these challenges, this paper proposes a staged real-time ground segmentation algorithm. The proposed algorithm not only achieves high real-time performance but also exhibits improved robustness. Based on a concentric zone model, the algorithm filters out reflected noise points and vertical non-ground points in the first stage, improving the validity of the fitted ground plane. In the second stage, the algorithm effectively addresses the issue of undersegmentation of ground points through three steps: ground plane fitting, ground plane validity judgment, and ground plane repair. The experimental results on the SemanticKITTI dataset demonstrate that the proposed algorithm outperforms the existing methods in terms of segmentation results.
Robust Testing of AI Language Model Resiliency with Novel Adversarial Prompts
Authors: Hannon Brendan , Kumar Yulia , Gayle Dejaun , Li J. Jenny , Morreale Patricia
Published: February 2024

In the rapidly advancing field of Artificial Intelligence (AI), this study presents a critical evaluation of the resilience and cybersecurity efficacy of leading AI models, including ChatGPT-4, Bard, Claude, and Microsoft Copilot. Central to this research are innovative adversarial prompts designed to rigorously test the content moderation capabilities of these AI systems. This study introduces new adversarial tests and the Response Quality Score (RQS), a metric specifically developed to assess the nuances of AI responses. Additionally, the research spotlights FreedomGPT, an AI tool engineered to optimize the alignment between user intent and AI interpretation. The empirical results from this investigation are pivotal for assessing AI models’ current robustness and security. They highlight the necessity for ongoing development and meticulous testing to bolster AI defenses against various adversarial challenges. Notably, this study also delves into the ethical and societal implications of employing advanced “jailbreak” techniques in AI testing. The findings are significant for understanding AI vulnerabilities and formulating strategies to enhance AI technologies’ reliability and ethical soundness, paving the way for safer and more secure AI applications.
MM-NeRF: Large-Scale Scene Representation with Multi-Resolution Hash Grid and Multi-View Priors Features
Authors: Dong Bo , Chen Kaiqiang , Wang Zhirui , Yan Menglong , Gu Jiaojiao , Sun Xian
Published: February 2024

Reconstructing large-scale scenes using Neural Radiance Fields (NeRFs) is a research hotspot in 3D computer vision. Existing MLP (multi-layer perception)-based methods often suffer from issues of underfitting and a lack of fine details in rendering large-scale scenes. Popular solutions are to divide the scene into small areas for separate modeling or to increase the layer scale of the MLP network. However, the subsequent problem is that the training cost increases. Moreover, reconstructing large scenes, unlike object-scale reconstruction, involves a geometrically considerable increase in the quantity of view data if the prior information of the scene is not effectively utilized. In this paper, we propose an innovative method named MM-NeRF, which integrates efficient hybrid features into the NeRF framework to enhance the reconstruction of large-scale scenes. We propose employing a dual-branch feature capture structure, comprising a multi-resolution 3D hash grid feature branch and a multi-view 2D prior feature branch. The 3D hash grid feature models geometric details, while the 2D prior feature supplements local texture information. Our experimental results show that such integration is sufficient to render realistic novel views with fine details, forming a more accurate geometric representation. Compared with representative methods in the field, our method significantly improves the PSNR (Peak Signal-to-Noise Ratio) by approximately 5%. This remarkable progress underscores the outstanding contribution of our method in the field of large-scene radiance field reconstruction.
The Temporal and Spatial Characteristics of Wind–Photovoltaic–Hydro Hybrid Power Output Based on a Cloud Model and Copula Function
Authors: Min Haoling , He Pinkun , Li Chunlai , Yang Libin , Xiao Feng
Published: February 2024

In a high proportion of wind–photovoltaic–hydro hybrid power systems, fluctuation and dispersion make it difficult to accurately quantify the output characteristics. Therefore, in this study, a cloud model and copula correlation coefficient matrix were constructed for a hybrid power generation system based on the output data. Multiple backward cloud transformation based on the sampling-with-replacement method was proposed to calculate the improved entropy and hyperentropy to analyze the fluctuation range and dispersion degree quantitatively. A similarity index was proposed to evaluate the similarity between wind power, PV power, and hydropower. A suitable copula function was selected, and the Kendall and Spearman coefficients show the correlation relationships of the hybrid systems. The temporal and spatial characteristics of the hybrid systems were analyzed based on the two models. A typical example in Qinghai proved the effectiveness and applicability of the method. The results show that the correlation between photovoltaic power and hydropower is better and that, in summer, hydropower can be used to adjust the output of renewable energy.
Advanced Exergy and Exergoeconomic Analysis of Cascade High-Temperature Heat Pump System for Recovery of Low-Temperature Waste Heat
Authors: Hu Xiaowei , Shi Chenyang , Liu Yong , Fu Xingyu , Ma Tianyao , Jin Mingsen
Published: February 2024

Cascade high-temperature heat pumps (CHTHPs) are often applied to recover low-temperature industrial waste heat owing to their large temperature lift. Through a comprehensive consideration of thermodynamic and economic performance, conventional and advanced exergy and exergoeconomic analyses are employed in this study to evaluate the potential for the improvement in CHTHP systems. The results show that the avoidable endogenous exergy destruction in a CHTHP system accounts for 62.26% of its total exergy destruction, indicating that most of the exergy destruction comes from the components. This suggests that CHTHP systems still have significant potential for improvement. The very low exergoeconomic factor of the total system (only 0.75%) implies that the exergy destruction cost has a great influence on the economic performance of a CHTHP system. The high- and low-temperature compressors are the two components with the highest exergy destruction, accounting for 34.14% and 26.79% of the total exergy destruction in the system, respectively. Moreover, their exergy destruction cost is much larger than that of the other components. Thus, the priorities for improvement should be the high- and low-temperature compressors. The decrease in exergy destruction in compressors produces a reduction in carbon emissions. This comprehensive analysis of thermodynamic and economic performance supplies guidance for the engineering application of CHTHPs in low-temperature waste heat recovery.
Quantum Computing as a Game Changer on the Path towards a Net-Zero Economy: A Review of the Main Challenges in the Energy Domain
Authors: Ricciardi Celsi Michela , Ricciardi Celsi Lorenzo
Published: February 2024

The aim of this paper is to report on the state of the art of the literature on the most recent challenges in the energy domain that can be addressed through the use of quantum computing technology. More in detail, to the best of the authors’ knowledge, the scope of the literature review considered in this paper is specifically limited to forecasting, grid management (namely, scheduling, dispatching, stability, and reliability), battery production, solar cell production, green hydrogen and ammonia production, and carbon capture. These challenges have been identified as the most relevant business needs currently expressed by energy companies on their path towards a net-zero economy. A critical discussion of the most relevant methodological approaches and experimental setups is provided, together with an overview of future research directions. Overall, the key finding of the paper, based on the proposed literature review, is twofold: namely, (1) quantum computing has the potential to trigger significant transformation in the energy domain by drastically reducing CO2 emissions, especially those relative to battery production, solar cell production, green hydrogen and ammonia production, as well as point-source and direct-air carbon capture technology; and (2) quantum computing offers enhanced optimization capability relative to relevant challenges that concern forecasting solar and wind resources, as well as managing power demand, facility allocation, and ensuring reliability and stability in power grids.
A New Method of Reducing the Inrush Current and Improving the Starting Performance of a Line-Start Permanent-Magnet Synchronous Motor
Authors: Szelag Wojciech , Jedryczka Cezary , Baranski Mariusz
Published: February 2024

This paper presents a new method of reducing the inrush current and improving the starting performance of a line-start permanent-magnet synchronous motor (LSPMSM). The novelty of the proposed method relies on the selection of the time instant of the connection of the stator winding to the grid, for which the smallest values of the amplitudes of inrush currents are obtained. To confirm the effectiveness of the developed method of limiting the inrush current, simulations and experimental studies were carried out. The algorithm and dedicated computer code developed by the authors for the analysis of transient coupled phenomena in the LSPMSM were used to study the impact of the time instant of connection of the winding to the grid on the motor start-up process. The algorithm was based on a field model of coupled electromagnetic and thermal phenomena in the studied motor. To verify the developed model of the phenomena and the proposed method, experimental research was carried out on a purpose-built computerised test stand. Good concordance between the results of the experiments and simulations confirmed the high reliability of the proposed model, as well as the effectiveness of the developed approach in limiting the inrush current and improving the starting performance of LSPMSMs.