bluuebunny/crossref_metadata_2025 huggingface dataset:
Do Laying Hens Form Stable Social Networks?—A Case Study
Authors: Hedlund Louise , Wright Dominic , Jensen Per
Published: January 2025

A social network is a group of individuals forming a non-random social structure where the number of interactions systematically vary among individuals. The Red Junglefowl, the ancestor of all domesticated chickens, has been shown to form social structures within a larger group; however, how poultry in egg production are socially organised is poorly understood. Egg layers in groups of thousands of individuals are suggested to either, just as Red Junglefowl, form subgroups or to lose social affiliations and replace these with a system of social tolerance. In this case study, a stable group of 20 laying hens with known genetic relationships housed in an outdoor aviary were observed for 3 h per day, during 20 days, in order to determine the social structure in the group. Social affiliations and centralisation measurements were assessed by proximity. The results show that some individuals were consistently more centralised compared to others, which indicates a stable individual variation in sociality. Furthermore, no individual spent more than 10% of her total number of interactions with a particular individual, i.e., little consistency in social affiliations could be found. The results of this case study support the theory of a higher social tolerance rather than a consistent social organisation in domesticated egg layers.
Study on the Corrosion Behavior of Graphite Materials in Molten CuSn Alloy
Authors: Cao Zhifei , Ye Zongbiao , Luo Xiangyang , Tian Hongrui , Guo Hengxin , Wei Jianjun , Gou Fujun
Published: January 2025

Graphite, a critical material for furnace walls, is pivotal to the reliability of the carbon-free hydrogen production industry through methane pyrolysis catalyzed by molten metals. This study systematically investigates the corrosion behavior of molten CuSn alloy on three typical commercial graphite materials—low-density graphite (LDG), high-density graphite (HDG), and pyrolytic graphite (PyG)—with a focus on their corrosion resistance and the underlying mechanisms responsible for graphite corrosion over a period of up to 1000 h at 1100 °C. The experimental results show that LDG suffered the most severe corrosion, with a mass loss of up to 60.09% and a hardness decrease from 0.73 GPa to 0.17 GPa, whereas PyG demonstrated the best corrosion resistance, with only a 5.64% mass loss and a hardness drop from 0.52 GPa to 0.35 GPa. SEM and XRD analyses revealed that the porous structures of LDG and HDG suffered significant macroscopic corrosion, caused by the stress from molten metal infiltration and aggregation in the pores, leading to structural collapse. Interestingly, all three types of graphite, including the non-porous PyG, exhibited disordered microstructural degradation as detected by Raman spectroscopy. Molecular dynamics (MD) simulations confirmed that the thermal motion of Cu and Sn atoms primarily drives the microstructural corrosion of graphite, suggesting that the corrosion process involves both micro- and macro-level damage. These findings provide crucial insight into the compatibility of different graphite materials with molten CuSn alloy and valuable guidance for material selection in methane pyrolysis devices.
Prophetism and Secularization: Kantian Hope as a Gnostic–Ebionite Synthesis
Authors: Abbate Stefano , Valentí Lluc
Published: January 2025

Kant’s philosophy of history is one of the best examples to trace the remains of certain theological concepts that emerged during the Christian tradition, which are secularized in order to shape a philosophical history. This process of secularization involves hidden causes through the weakening of the doctrinal truths of Christianism. These threats come from two antithetical poles whose attempts to demolish the Christian faith have been noticeable since its beginnings: Gnosticism and Ebionism. This article seeks to trace the influence of both doctrines in Kant’s philosophy of history. Based on his reinterpretation of certain theological concepts, it offers an adequate frame to understand the backgrounds that sustain Kant’s hope when regarding history. His great hesitation between an ultimate, intra-historical consummation and an infinite aspiration never fulfilled on Earth can be explained well from the opposite Ebionite and Gnostic perspectives. In conclusion, the article proposes that the tension that inspires Kant’s philosophy of history emerges from an unstable synthesis between Gnosticism and Ebionism.
Infrared Small Target Detection Algorithm Based on Improved Dense Nested U-Net Network
Authors: Du Xinyue , Cheng Ke , Zhang Jin , Wang Yuanyu , Yang Fan , Zhou Wei , Lin Yu
Published: January 2025

Infrared weak and small target detection technology has attracted much attention in recent years and is crucial in the application fields of early warning, monitoring, medical diagnostics, and anti-UAV detection.With the advancement of deep learning, CNN-based methods have achieved promising results in general-purpose target detection due to their powerful modeling capabilities; however, CNN-based methods cannot be directly applied to infrared small targets due to the disappearance of deep targets caused by multiple downsampling operations. Aiming at these problems, we proposed an improved dense nesting and attention infrared small target detection method based on U-Net called IDNA-UNet. A dense nested interaction module (DNIM) is designed as a feature extraction module to achieve level-by-level feature fusion and retain small targets’ features and detailed positioning information. To integrate low-level features into deeper high-level features, we designed a bottom-up feature pyramid fusion module, which can further retain high-level semantic information and target detail information. In addition, a more suitable scale and position sensitive (SLS) loss is applied to each prediction scale to help the detector locate the target more accurately and distinguish different scales of the target. With our IDNA-UNet, the contextual information of small targets can be well incorporated and fully exploited by repetitive fusion and enhancement. Compared with existing methods, IDNA-UNet has achieved significant advantages in the intersection over union (IoU), detection probability (Pd), and false alarm rate (Fa) of infrared small target detection.
Detection of Human Traffic Controllers Wearing Construction Workwear via Synthetic Data Generation
Authors: Baik Seunghyun , Kim Euntai
Published: January 2025

Developing Level 3 or higher autonomous vehicles requires the ability to follow human traffic controllers in situations where regular traffic signals are unavailable, such as during construction. However, detecting human traffic controllers at construction sites is challenging due to the lack of dedicated datasets and variations in their appearance. This paper proposes a method for detecting human traffic controllers by generating synthetic images with diffusion models. We introduce a color-boosting technique to enhance image diversity and employ a cut-and-paste mechanism for seamless integration into realistic road scenes. We generate 19,840 synthetic images, combined with 600 real-world images, to train a YOLOv7 model. The trained model achieves an AP50 score of 73.9%, improving by 32.9% over the baseline. The HTC600 dataset used in our experiments is publicly available to support autonomous driving research.
Machine Learning-Based Fault Detection and Exclusion for Global Navigation Satellite System Pseudorange in the Measurement Domain
Authors: Alghananim Ma’mon Saeed , Feng Cheng , Feng Yuxiang , Ochieng Washington Yotto
Published: January 2025

Global Navigation Satellite Systems (GNSS) support numerous applications, including mission-critical ones that require a high level of integrity for safe operations, such as air, maritime, and land-based navigation. Fault Detection and Exclusion (FDE) is crucial for mission-critical applications, as faulty measurements significantly impact system integrity. FDE can be applied within the positioning algorithm in the measurement’s domain and the integrity monitoring domain. Previous research has utilized a limited number of Machine Learning (ML) models and Quality Indicators (QIs) for the FDE process in the measurement domain. It has not evaluated the pseudorange measurement fault thresholds that need to be detected. In addition, ML models were mainly evaluated based on accuracy, which alone does not provide a comprehensive evaluation. This paper introduces a comprehensive framework for traditional ML-based FDE prediction models in the measurement domain for pseudorange in complex environments. For the first time, this study evaluates the fault detection thresholds across 40 values, ranging from 1 to 40 m, using six ML models for FDE. These models include Decision Tree, K-Nearest Neighbors (KNN), Discriminant, Logistic, Neural Network, and Trees (Boosted, Bagged, and Rusboosted). The models are comprehensively assessed based on four key aspects: accuracy, probability of misdetection, probability of fault detection, and the percentage of excluded data. The results show that ML models can provide a high level of performance in the FDE process, exceeding 95% accuracy when the fault threshold is equal to or greater than 4 m, with KNN providing the highest FDE performance.
A Vision Transformer Model for the Prediction of Fatal Arrhythmic Events in Patients with Brugada Syndrome
Authors: Randazzo Vincenzo , Caligari Silvia , Pasero Eros , Giustetto Carla , Saglietto Andrea , Bertarello William , Averbuch Amir , Marcus-Kalish Mira , Zheludev Valery , Gaita Fiorenzo
Published: January 2025

Brugada syndrome (BrS) is an inherited electrical cardiac disorder that is associated with a higher risk of ventricular fibrillation (VF) and sudden cardiac death (SCD) in patients without structural heart disease. The diagnosis is based on the documentation of the typical pattern in the electrocardiogram (ECG) characterized by a J-point elevation of ≥2 mm, coved-type ST-segment elevation, and negative T wave in one or more right precordial leads, called type 1 Brugada ECG. Risk stratification is particularly difficult in asymptomatic cases. Patients who have experienced documented VF are generally recommended to receive an implantable cardioverter defibrillator to lower the likelihood of sudden death due to recurrent episodes. However, for asymptomatic individuals, the most appropriate course of action remains uncertain. Accurate risk prediction is critical to avoiding premature deaths and unnecessary treatments. Due to the challenges associated with experimental research on human cardiac tissue, alternative techniques such as computational modeling and deep learning-based artificial intelligence (AI) are becoming increasingly important. This study introduces a vision transformer (ViT) model that leverages 12-lead ECG images to predict potentially fatal arrhythmic events in BrS patients. This dataset includes a total of 278 ECGs, belonging to 210 patients which have been diagnosed with Brugada syndrome, and it is split into two classes: event and no event. The event class contains 94 ECGs of patients with documented ventricular tachycardia, ventricular fibrillation, or sudden cardiac death, while the no event class is composed of 184 ECGs used as the control group. At first, the ViT is trained on a balanced dataset, achieving satisfactory results (89% accuracy, 94% specificity, 84% sensitivity, and 89% F1-score). Then, the discarded no event ECGs are attached to additional 30 event ECGs, extracted by a 24 h recording of a singular individual, composing a new test set. Finally, the use of an optimized classification threshold improves the predictions on an unbalanced set of data (74% accuracy, 95% negative predictive value, and 90% sensitivity), suggesting that the ECG signal can reveal key information for the risk stratification of patients with Brugada syndrome.
Biofeedback’s Effect on Orthosis Use: Insights from Continuous Six-Week Monitoring of Ankle Fracture Loading
Authors: Merkle Tobias Peter , Hofmann Nina , Knop Christian , Da Silva Tomas
Published: January 2025

There is limited understanding of how well patients adhere to postoperative instructions following ankle surgery, particularly in outpatient settings regarding partial weight bearing (15–30 kg) and orthosis use. This study aims to assess orthosis compliance and load frequency over six weeks post-surgery using pressure-sensitive insoles, while also evaluating the effectiveness of continuous biofeedback. A total of 84 patients with isolated ankle fractures were enrolled. All participants were instructed to maintain partial weight bearing of 15–30 kg for six weeks with a lower leg orthosis equipped with insoles that continuously recorded daily step counts and maximum loads. In a prospective randomized design, the control group received no biofeedback, while the intervention group received audiovisual feedback whenever loads exceeded 20 kg. Adherence to the prescribed partial weight bearing and orthosis use was low in both groups, with only 10% of the control group wearing the orthosis by week three and overload occurring as early as week one. However, the implementation of biofeedback resulted in significant improvements in orthosis utilization (57.4% vs. 29.1%) and adherence to prescribed loading. The implementation of continuous biofeedback significantly enhanced adherence to prescribed loading and orthosis usage, highlighting its critical role in postoperative rehabilitation for ankle fractures.
Integrated Analysis of Indoor Air Quality and Fungal Microbiota in Educational Heritage Buildings: Implications for Health and Sustainability
Authors: Ilies Alexandru Bogdan , Burtă Ovidiu , Hadeel Sa’ad Al-Hyari , Mircea Cristina , Al Shomali Maisa , Caciora Tudor , Ratiu Mariana , Pereș Ana Cornelia , Hassan Thowayeb H. , Janzakov Bekzot , Lazar Liviu
Published: January 2025

Indoor air quality is paramount for the health and well-being of individuals, especially in enclosed spaces like office buildings, schools, hospitals, and homes where people spend a significant amount of time. Ensuring good indoor air quality is not only essential for reducing symptoms such as headaches, eye and respiratory irritation, fatigue, and difficulty in concentration, but it is also a key component of sustainable building practices aimed at promoting long-term health and environmental balance. This study aims to explore the impact of the microclimate and fungal microbiota on the health and cognitive performance of occupants in a university classroom, which is part of a cultural heritage building. The research delves into various microclimatic parameters, including temperature, relative humidity, CO2, volatile organic compounds, O2, and particulate matters (PM2.5 and PM10), to understand their influence on the development of microbiota and the manifestation of symptoms associated with Sick Building Syndrome. Over the course of a year-long investigation, microbiological samples were collected, revealing the presence of 19 fungal species, with Cladosporium, Alternaria, and Aureobasidium being the most prevalent genera. These species were found to thrive in an environment characterized by inadequate ventilation, posing potential health risks to occupants, such as allergic reactions and respiratory infections. Microclimatic parameter values such as mean temperature of 22.9 °C and mean relative humidity of 38.5% indicated moderate conditions for fungal proliferation, but occasional high levels of PM2.5 and CO2 indicated periods of poor indoor air quality, negatively influencing the comfort and health of the occupants. The questionnaires completed by 190 students showed that 51.5% reported headaches, 44.2% frequent sneezing, and 43.7% severe fatigue, linking these symptoms to increased levels of CO2 and PM2.5. The novelty of the study lies in the integrated approach to indoor air quality assessment in a heritage educational building, highlighting the need for improved ventilation and air management to enhance health and cognitive performance, while emphasizing sustainable indoor environment management that balances occupant well-being with the preservation of cultural heritage.
Application of Interpretable Artificial Intelligence for Sustainable Tax Management in the Manufacturing Industry
Authors: Han Ning , Xu Wen , Song Qian , Zhao Kai , Xu Yao
Published: January 2025

The long-term development of the manufacturing industry relies on sustainable tax management, which plays a key role in optimizing production costs. While artificial intelligence models have been applied to tax-related predictions, research on their application for predicting tax management levels is quite limited, with no studies focused on the manufacturing industry in China. To enhance digital innovation in corporate management, this study applies interpretable artificial intelligence models to predict the tax management level, which helps decision-makers maintain it within a sustainable range. The ratio of total tax expense to total profits (ETR) is used to represent the tax management level, which is predicted using decision trees, random forests, linear regression, support vector regression, and artificial neural networks with eight input features. Comparisons among the developed models indicate that the random forest model exhibits the best performance in terms of prediction accuracy and generalization capability. Additionally, the Shapley additive explanations (SHAP) technique is integrated with the developed model to enhance the interpretability of its predictions. The SHAP results reveal the importance of the input features and also highlight the dominance of certain features. The results show that the ETR from the previous year holds the greatest importance, being more than twice as significant as the second most important factor, whereas the effect of board size is negligible. Moreover, benefiting from the local interpretations using SHAP values, this approach aids managers in making rational tax management decisions.
European Tourism Sustainability and the Tourismphobia Paradox: The Case of the Canary Islands
Authors: Sánchez-Bayón Antonio , Daumann Frank
Published: January 2025

The growth of the tourism sector has led to overtourism in many destinations, which can negatively affect sections of the local population. This often results in a phenomenon known as tourismphobia, which describes the fact that residents develop reservations about tourists and take -not always legal- countermeasures; these measures are not sustainable in the long term, leading to the tourism paradox (with less economic activity). The origins of this phenomenon have so far been insufficiently analyzed. This article aims to present an explanatory approach for the emergence of this phenomenon based on the structural-cognitive model and to illustrate it using the example of the Canary Islands. For the Canary Islands, it can be shown that both the political opportunity structures allow for significant scope, and resource mobilization is comparatively easy to achieve. On the micro level, the high costs of overtourism for many residents, combined with relatively low returns from tourism and the non-prohibitive costs of countermeasures, lead to collective action against overtourism –manifesting as tourismphobia. In this way, this article provides a viable explanation for the emergence of tourismphobia and thus makes a significant contribution to knowledge in this field of research. Complementary analytical resources are also outlined, combining Austrian economics and new-institutional economics.
The Impact of World Uncertainty, Environmental Policy Stringency, and Technological Innovation on Environmental Sustainability: Evidence from High-Income Countries
Authors: Alatrash MotazBellah Abdalmuiz , Bein Murad Abdurahman , Samour Ahmed
Published: January 2025

Industrialization and economic growth in high-income countries have considerably increased consumption-based CO2 emissions (CCO2), adversely affecting environmental sustainability and contributing to climate change. This study assesses the impacts of Environmental Policy Stringency (EPS), World Uncertainty (WU), and Technological Innovation (TI) on environmental sustainability, aiming to guide the development of balanced policies that foster growth. Utilizing panel data from 1990 to 2021 across high-income countries, we employed the Method of Moments Quantile Regression (MMQR) to capture the varying effects of these factors at different levels of CCO2 emissions. Our findings reveal that WU consistently and significantly reduces CCO2 emissions across all quantiles, while the effects of EPS are minimal and largely insignificant. Similarly, TI demonstrates a weak and statistically non-significant impact, indicating that technological advancements alone are insufficient for meaningful improvements in environmental sustainability. Importantly, renewable energy consumption (REC) significantly lowers CCO2 emissions, while economic growth (GDP) has a strong tendency to increase emissions, particularly at lower quantiles. These insights highlight the necessity for high-income countries to adopt comprehensive fiscal and environmental policies to mitigate emissions and enhance sustainability, with a focus on prioritizing renewable energy, encouraging innovation, and strengthening policy measures to achieve long-term ecological objectives.
Detection of Defective Solar Panel Cells in Electroluminescence Images with Deep Learning
Authors: Karakan Abdil
Published: January 2025

In this study, faults in solar panel cells were detected and classified very quickly and accurately using deep learning and electroluminescence images together. A unique and new dataset was created for this study. Monocrystalline and polycrystalline solar panel cells were used in the dataset. The dataset included intact, cracked and broken images for each solar panel cell. The dataset was preprocessed and multiplied to equalize the intact, cracked and broken numbers. Seven different deep learning architectures were used in this study. As a result of this study, 97.82% accuracy was achieved for the monocrystalline solar panel cells and 96.29% for the polycrystalline solar panel cells in the SqueezeNet architecture.
Four Classes of Symmetric Sums over Cyclically Binomial Products
Authors: Chen Marta Na , Chu Wenchang
Published: January 2025

Four classes of multiple symmetric sums over cyclic products of binomial coefficients are examined. By incorporating the generating function approach and recursive construction method, they are expressed analytically as coefficients of rational functions. Several recurrence relations and generating functions are explicitly determined when the dimension of the multiple sums does not exceed five.
Supplier Selection Model Considering Sustainable and Resilience Aspects for Mining Industry
Authors: Becerra Pablo , Diaz Javier
Published: January 2025

Supplier selection plays a pivotal role in the mining industry, forming a key component of the supply chain management. It has been established that the integration of sustainability and resilience into this process can significantly enhance the industry’s ability to withstand economic, environmental, and social shocks. Despite a large body of literature investigating supplier selection, there is a notable gap in research specifically addressing the incorporation of sustainability and resilience criteria in the mining industry. The objective of this research is to bridge this knowledge gap and contribute to the understanding of sustainable and resilient supplier selection in the mining industry. A constructive research approach was employed, identifying both practical and theoretical problems and proposing a construction—a mathematical model. This model was developed in collaboration with industry key actors, ensuring its practical applicability and validity. The main result of this research is an optimization mathematical programming model that allows practitioners to evaluate and select suppliers considering both sustainability and resilience criteria. The model facilitates a comprehensive assessment of suppliers, incorporating a wide range of factors beyond cost, including environmental impact, social responsibility, and the ability to maintain supply under various potential disruptions.
Multiple Co-Infecting Caliciviruses in Oral Fluid and Enteric Samples of Swine Detected by a Novel RT-qPCR Assay and a 3′RACE-PCR-NGS Method
Authors: László Zoltán , Pankovics Péter , Urbán Péter , Herczeg Róbert , Balka Gyula , Igriczi Barbara , Cságola Attila , Albert Mihály , Tóth Fruzsina , Reuter Gábor , Boros Ákos
Published: January 2025

Caliciviruses including noro- and sapoviruses of family Caliciviridae are important enteric human and swine pathogens, while others, like valoviruses, are less known. In this study, we developed a detection and typing pipeline for the most prevalent swine enteric caliciviruses—sapovirus GIII (Sw-SaV), norovirus GII (Sw-NoV), and valovirus GI (Sw-VaV). The pipeline integrates triplex RT-qPCR, 3′RACE semi-nested PCR, and next-generation sequencing (NovaSeq, Illumina) techniques. A small-scale epidemiological investigation was conducted on archived enteric and, for the first time, on oral fluid/saliva samples of diarrheic and asymptomatic swine of varying ages from Hungary and Slovakia. In enteric samples, Sw-SaV was the most prevalent, detected in 26.26% of samples, primarily in diarrheic pigs with low Cq values, followed by Sw-NoV (2.53%) in nursery pigs. In oral fluid samples, Sw-NoV predominated (7.46%), followed by Sw-SaV (4.39%). Sw-VaVs were sporadically found in both sample types. A natural, asymptomatic Sw-SaV outbreak was retrospectively detected where the transient shedding of the virus was <2 weeks. Complete capsid sequences (n = 59; 43 Sw-SaV, 13 Sw-NoV, and 3 Sw-VaV) including multiple (up to five) co-infecting variants were identified. Sw-SaV sequences belong to seven genotypes, while Sw-NoV and Sw-VaV strains clustered into distinct sub-clades, highlighting the complex diversity of these enteric caliciviruses in swine.
Statistical Distributions of Genome Assemblies Reveal Random Effects in Ancient Viral DNA Reconstructions
Authors: Antoneli Fernando , Peter Cristina M. , Briones Marcelo R. S.
Published: January 2025

Ancient human viruses have been detected in ancient DNA (aDNA) samples of both Anatomically Modern Humans and Neanderthals. Reconstructing genomes from aDNA using reference mapping presents numerous problems due to the unique nature of ancient samples, their degraded state, smaller read sizes and the limitations of current methodologies. The spurious alignments of reads to reference sequences (mapping) are a main source of false positives in aDNA assemblies and the assessment of signal-to-noise ratios is essential to differentiate bona fide reconstructions from random, noisy assemblies. Here, we analyzed the statistical distributions of viral genome assemblies, ancient and modern, and their respective random “mock” controls used to evaluate the signal-to-noise ratio. We tested if differences between real and random assemblies could be detected from their statistical distributions. Our analysis shows that the coverage distributions of (1) real viral aDNA assemblies of adenovirus (ADV), herpesvirus (HSV) and papillomavirus (HPV) do not follow power laws nor log-normal laws, (2) (ADV) and control aDNA assemblies are well approximated by log-normal laws, (3) negative control parvovirus B19 (real and random) follow a power law with infinite variance and (4) the mapDamage negative control with non-ancient DNA (modern ADV) and the mapDamage positive control (human mtDNA) are well approximated by the negative binomial distribution, consistent with the Lander–Waterman model. Our results show that the tails of the distributions of aDNA and their controls reveal the weight of random effects and can differentiate spurious assemblies, or false positives, from bona fide assemblies.
Identification of Diverse Bacteriophages Associated with Bees and Hoverflies
Authors: Bandoo Rohan A. , Kraberger Simona , Ozturk Cahit , Lund Michael C. , Zhu Qiyun , Cook Chelsea , Smith Brian , Varsani Arvind
Published: January 2025

Bacteriophages are the most numerous, ubiquitous, and diverse biological entities on the planet. Prior studies have identified bacteriophages associated with pathogenic and commensal microbiota of honeybees. In this study we expand on what is known about bacteriophages from the lineages Caudoviricetes, Inoviridae, and Microviridae, which are associated with honeybees (Apidae, Apis mellifera), solitary bees of the genus Nomia (Halictidae, Nomia), and hoverflies (Syrphidae). The complete genomes of seven caudoviruses, seven inoviruses, and 288 microviruses were assembled from honeybees (n = 286) and hoverflies in Arizona (n = 2). We used bacterial host predictive software and sequence read mapping programs to infer the commensal and transient bacterial hosts of pollinating insects. Lastly, this study explores the phylogenetic relationships of microviruses sampled from bees, opportunistically sampled pollinating insects such as hoverflies, and blackflies.
Manufacturing and Financial Evaluation of Peptide-Based Neoantigen Cancer Vaccines for Triple-Negative Breast Cancer in the United Kingdom: Opportunities and Challenges
Authors: Novakova Adriana , Morris Stephen A. , Vaiarelli Ludovica , Frank Stefanie
Published: January 2025

This review evaluates the financial burden of current treatments for triple-negative breast cancer (TNBC) and projects potential financial scenarios to assess the feasibility of introducing a peptide-based neoantigen cancer vaccine (NCV) targeting the disease, using the UK as a healthcare system model. TNBC, the most aggressive breast cancer subtype, is associated with poor prognosis, worsened by the lack of personalised treatment options. Neoantigen cancer vaccine therapies present a personalised alternative with the potential to enhance T-cell responses independently of genetic factors, unlike approved immunotherapies for TNBC. Through a systematic literature review, the underlying science and manufacturing processes of NCVs are explored, the direct medical costs of existing TNBC treatments are enumerated, and two contrasting pricing scenarios for NCV clinical adoption are evaluated. The findings indicate that limited immunogenicity is the main scientific barrier to NCV clinical advancement, alongside production inefficiencies. Financial analysis shows that the UK spends approximately GBP 230 million annually on TNBC treatments, ranging from GBP 2200 to GBP 54,000 per patient. A best-case pricing model involving government-sponsored NCV therapy appears financially viable, while a worst-case, privately funded model exceeds the National Institute for Health and Care Excellence (NICE) cost thresholds. This study concludes that while NCVs show potential clinical benefits for TNBC, uncertainties about their standalone efficacy make their widespread adoption in the UK unlikely without further clinical research.
Cultured Bacteria in the Abdominal Wall Incision During the Realisation of Caesarean Section in Cows: A Preliminary Study
Authors: Djebala Salem , Coria Elise , Munaut Florian , Gille Linde , Eppe Justine , Moula Nassim
Published: January 2025

Although caesarean section (CS) is a common surgery, the bacteria contaminating the abdominal incision during this operation are not documented. This study aimed to enhance perioperative antibiotic therapy and reduce post-operative surgical site infections by describing the bacteria contaminating the abdominal incision during CS. Bacterial culture was performed on swabs sampled from the abdominal incision (muscular layers) of 23 cows during the realisation of CS. Bacteriology was positive in 6/22 samples. while 16/22 were negative (p = 0.03), and one sample was contaminated. All identified bacteria were aerobic, and 8/14 isolates were Gram-positive and 6/14 were Gram-negative (p = 0.7). The most encountered species were Acinetobacter spp., Aerococcus viridans, Neisseria spp. and Streptococcus spp.; other species were isolated only once. The abdominal incision of elective CS can be contaminated by ubiquitous aerobic Gram-positive and Gram-negative species coming from the environment. This contamination might be avoided by using a better aseptic protocol and by improving the conditions in which CSs are performed. However, more samples are required, and antibiotic susceptibility determination is needed to identify the adequate pre-operative antibiotic.
Enhanced Removal of Nitrate and Tetracycline by Bacillus cereus W2 Immobilized on Biochar
Authors: Xue Jiangpeng , Lu Xinyue , Wei Lianghuan , Zha Xianghao , Xu Wenjie
Published: January 2025

The co-contamination of nitrate nitrogen (NO3−-N) and tetracycline (TC) in aquaculture water has caused serious environmental and health problems. Bioremediation is a promising approach for the removal of NO3−-N and TC. However, free bacteria are sensitive to environmental variation, limiting its application. In this study, a bacterial strain with high NO3−-N and TC degradation ability, Bacillus cereus W2, was isolated and immobilized on wheat straw biochar by an adsorption method. The effect of immobilization conditions, including biochar dosage, inoculum amount, and immobilization time on NO3−-N and TC removal was explored. The degradation abilities of the biochar-immobilized Bacillus cereus W2 under different nitrate and TC concentrations was investigated. Results showed that the prepared biochar had abundant functional groups such as -COOH, -OH, -C=C-OH, etc., which have good affinity for microbial cell membranes and are conducive to the adhesion and proliferation of microbial cells. The highest NO3−-N and TC removal efficiencies of 99.50% and 78.60% after 24 h were obtained under a biochar dosage of 4 mg·mL−1, microbe inoculation amount of 40%, and immobilization time of 24 h. The immobilized Bacillus cereus W2 performed better NO3−-N and TC removal than the free cells under different initial NO3−-N and TC concentrations. The enhanced removal of NO3−-N by the biochar-immobilized Bacillus cereus W2 may be attributed to the promoted expression level of functional genes involved in denitrification (nirS, norB, and nosZ). The biochar-immobilized Bacillus cereus W2 demonstrates potential for treating various nitrate-antibiotic co-contaminated wastewaters, including those from livestock farming, aquaculture systems, and pharmaceutical industries.
The Theoretical Basis of qPCR and ddPCR Copy Number Estimates: A Critical Review and Exposition
Authors: McNair James N. , Frobish Daniel , Rediske Richard R. , Hart John J. , Jamison Megan N. , Szlag David C.
Published: January 2025

The polymerase chain reaction (PCR) is a molecular biology tool with diverse applications in the aquatic sciences. Classical PCR is a nonquantitative method that can be used to detect target DNA sequences that are characteristic of particular microbial taxa but cannot determine their concentrations in water samples. Various quantitative forms of PCR have been developed to remove this limitation. Of these, the two that currently are used most widely are real-time quantitative PCR (qPCR) and droplet digital PCR (ddPCR). Several outlines of the mathematical and statistical basis of these methods for estimating target sequence concentrations are available in the literature, but we are aware of no thorough and rigorous derivation of the theoretical underpinnings of either. The purpose of this review is to provide such derivations, and to identify and compare the main strengths and weaknesses of the two methods. We find that both estimation methods are sound, provided careful attention is paid to specific details that differ between the two. With qPCR, it is especially important to reduce any significant PCR inhibition by sample constituents and to properly fit the standard curve to heteroskedastic calibration data. With ddPCR, it is important to ensure that the value of the mean droplet volume used in calculating concentrations is correct for the particular combination of droplet generator and master mix used. The advantages of qPCR include lower instrument and per-sample costs, a shorter turnaround time for obtaining results, a higher upper limit of quantification, and a wider dynamic range. The advantages of ddPCR include freedom from dependence on a standard curve, an inherently lower sensitivity to PCR inhibitors, a lower limit of quantification, a simpler theoretical basis, and simpler data analysis. We suggest qPCR often will be preferable in laboratory studies where investigators have significant control over the range of target sequence concentrations in samples, concentrations are sufficiently high so proper calibration does not require standards with concentrations low enough to exhibit exaggerated variability in the threshold cycle, and no significant inhibition is present, or more generally, in studies where funding levels do not permit the higher cost of instrumentation and supplies required by ddPCR or where the shorter turnaround time for qPCR is essential. If sufficient funds are available, ddPCR often will be preferable when the ability to quantify low concentrations is important, especially if inhibitors are likely to be present at concentrations that are problematic for qPCR.
Water Surface Temperature Dynamics of the Three Largest Ice-Contact Lakes in the Patagonia Icefield over the Last 20 Years
Authors: Zhao Shaochun , Sun Hongyan , Cheng Jie , Zhang Guoqing
Published: January 2025

The Patagonia Icefield, the largest ice mass in the Southern Hemisphere outside Antarctica, has experienced significant growth and expansion of ice-contact lakes in recent decades, with lake surface water temperature (LSWT) being one of the key influencing factors. LSWT affects glacier melting at the waterline and accelerates glacier mass loss. However, the observations of ice-contact LSWT are often limited to short-term, site-based field measurements, which hinders long-term, whole-lake monitoring. This study examines LSWT for the three largest ice-contact lakes in the Patagonia Icefield—Lake Argentino, Lake Viedma, and Lake O’Higgins, each exceeding 1000 km2—and the three largest nearby non-ice-contact lakes for comparison using MODIS data between 2002 and 2022. In 2022, the mean LSWTs for Lake Argentino, Lake Viedma, and Lake O’Higgins were 7.2, 7.0, and 6.4 °C, respectively. In summer, ice-contact lakes exhibited wider LSWT ranges and more pronounced cooling near glacier termini and warming farther away compared to other seasons, demonstrating glacier melt cooling and its seasonal variability. Over the past 20 years, both Lake Viedma and Lake O’Higgins showed a warming rate of +0.20 °C dec−1, p > 0.1, with slower warming near the glacier, reflecting glacier contact suppression on the LSWT trend. Conversely, Lake Argentino displayed a significant warming rate of +0.43 °C dec−1 (p < 0.05), with faster rates near the glacier terminus, possibly linked to a prolonged and large (>64 km2) iceberg accumulation event from March 2010 to October 2011 in Glacier Upsala’s fjord. Iceberg mapping shows that larger events caused more pronounced short-term (24 days) LSWT cooling in Lake Argentino’s ice-proximal region. This study highlights the role of glacier–lake interactions including calving events in regulating ice-contact lake water temperature.
Review of AI Image Enhancement Techniques for In-Vehicle Vision Systems Under Adverse Weather Conditions
Authors: Mo Tiande , Zheng Siqian , Chan Wai-Yat , Yang Renhua
Published: January 2025

Nowadays, rapid advancements in computer vision, image processing, and artificial intelligence (AI) have significantly benefited autonomous vehicles. Visual perception is crucial for enhancing the functionality and safety of self-driving technology. However, adverse weather and illumination conditions can impair visual capabilities, affecting environmental awareness, decision-making, and safe navigation. This work provides a comprehensive review of AI image enhancement methods and benchmark datasets, including deblurring, deraining, dehazing, and low-light enhancement, along with the integration of multiple image enhancement techniques in computer vision tasks. Specifically, this review focuses on advancements for real-world applications and summarizes performance metrics for real-time operation in automotive vision systems. Furthermore, the paper highlights efforts and challenges in real-world testing to ensure the effectiveness and reliability of these solutions in practical applications, which is essential for enabling autonomous vehicles to operate safely and efficiently under various challenging conditions, thereby contributing to the future of intelligent transportation systems.
Academic Resilience and Motivation as Predictors of Academic Engagement Among Rural and Urban High School Students in Ghana
Authors: Amoadu Mustapha , Hagan John Elvis , Obeng Paul , Agormedah Edmond Kwesi , Srem-Sai Medina , Schack Thomas
Published: January 2025

Academic resilience and motivation are two key positive psychological constructs that have the capacity to influence learners’ engagement even in difficult conditions. Surprisingly, research investigating these relational constructs is limited in Ghana. The study examined the influence of academic resilience and motivation on engagement, highlighting rural–urban variations among senior high school students. This cross-sectional survey involved 190 senior high school students in Ghana, employing stratified sampling. Academic resilience, motivation, and engagement were assessed using the academic resilience scale (ARS-30), the motivated strategies for learning questionnaire (MSLQ), and the university student engagement inventory (USEI). Data were analysed using descriptive, Pearson correlation, and hierarchical multiple regression analyses. An independent t-test was also conducted to compare the study variables between rural and urban students, utilising IBM Statistical Package for the Social Sciences (SPSS) version 27. The correlational analysis revealed that academic resilience positively correlates with engagement and motivation. A regression model indicated that socio-demographic factors have a minimal impact on academic engagement, while academic resilience and motivation significantly predict it. No significant differences were found between urban and rural students regarding academic engagement, motivation, or resilience. Educators and policymakers are urged to implement strategies, including social–emotional learning and mentorship programmes, to support and cultivate academic resilience, motivation, and engagement among students. Future studies could investigate the relationship between academic, motivational intensity, and learners’ academic achievement across a larger sample.
Deprem Bölgesi Sınıf Öğretmenlerinin Deprem Sonrası Eğitim Öğretimle İlgili Görüşleri
Authors: Yaşaroğlu Cihat , Sürmeli Semih , Atlı Taner
Published: January 2025

6 Şubat 2023’te Kahramanmaraş merkezli iki şiddetli depremden tüm ülke olumsuz etkilenmesine rağmen özellikle 11 il çok boyutlu olarak etkilenmiştir. Depremden belli bir süre sonra deprem bölgesinde de okullar kademeli olarak açılmış, eğitim öğretime başlanmıştır. Bu çalışmada, deprem bölgesinde görev yapan sınıf öğretmenlerinin, depremden sonra başlayan eğitim – öğretim süreçlerine ilişkin görüşlerinin belirlenmesi amaçlanmıştır. Bu amaca uygun olarak tarama modeli bir çalışma tasarlanmış, 220 sınıf öğretmenine ulaşılmıştır. Verilerin toplanmasında, deprem bölgesinde bulunan araştırmacılar tarafından oluşturulan veri toplama aracı kullanılmıştır. Veri toplama aracı hem açık uçlu hem de kapalı uçlu sorular yer almaktadır. Ankette yer alan sorular, öğretmenlerin depremle ilgili duygu durumları ve kişisel tecrübeleri, paydaşlarla iletişim süreci, öğrencilere ilişkin gözlemler ile eğitim – öğretim süreçlerine ilişkin sorular yer almaktadır. Öğretmenler anketi doldurduktan sonra eksik bırakılan anketler analiz dışı bırakılmıştır. Veri analizinde açık uçlu olan soruların cevapları da nicelleştirilmiş tüm soruların cevapları betimsel olarak analiz edilmiştir. Öğretmenlerin okulların yeniden açılmasını olumlu gördükleri, stres düzeylerinin arttığı, dikkat ve motivasyon sürelerinin azaldığı gibi sonuçlar araştırmanın başlıca sonuçları arasındadır.
Del Sueño a la Realidad: Estrategias de Calidad para Emprendedores Exitosos
Authors: Consuegra Bosquez Ricardo
Published: January 2025

El camino del emprendimiento está lleno de desafíos y oportunidades, convertir una idea en un negocio exitoso necesita más que solo pasión, requiere estrategias bien definidas y una gestión de calidad que asegure la viabilidad y el crecimiento sostenido del emprendimiento. Este artículo aborda estas necesidades, proporcionando a los emprendedores las herramientas necesarias para superar obstáculos y alcanzar el éxito. El objetivo principal es equipar a los emprendedores con un conjunto de estrategias de calidad que les permitan transformar sus sueños en negocios prósperos y sostenibles; el estudio busca inspirar a los participantes a adoptar una mentalidad de mejora continua y a implementar procesos que aseguren la eficiencia y efectividad de sus operaciones. Además, se pretende conseguir que los emprendedores comprendan la importancia de la gestión de calidad como un diferenciador competitivo en el mercado. La metodología del artículo se divide en dos fases principales: bibliográfica y descriptiva.
The Alexandria Effect. City Foundation in Ptolemaic Culture and the Egyptian Histories of Manetho and Diodorus
Authors: Trnka-Amrhein Yvona
Published: January 2025

Cet article examine le discours ptolémaïque sur les fondations de cités à travers les récits de fondation de cités égyptiennes anciennes tels qu’on les trouve dans les Aigyptiaca de Manéthon et dans le premier livre de la Bibliothèque historique de Diodore de Sicile. Il montre comment les manières grecques et égyptiennes de raconter les fondations de cités ont été modifiées pour s’adapter au contexte ptolémaïque. Il soutient que l’ascension fulgurante d’Alexandrie et les différentes réactions qu’elle suscita influencèrent les récits composés pour trois villes égyptiennes antérieures : Thèbes, Memphis et Avaris.
Devastation of Cultivated Land in Herodotus
Authors: Foster Edith
Published: January 2025

Cet article soutient que chez Hérodote il faut distinguer la dévastation de terres cultivées des atteintes à la nature. Il précise les principaux aspects que lesquels insiste l’historien quand il évoque la dévastation et le pillage de terres agricoles, et analyse la relation entre ces thèmes et les événements de la guerre du Péloponnèse. Il montre enfin qu’Hérodote a dépeint la dévastation des terres agricoles comme un acte de guerre, et que ses récits de telles attaques devraient être considérés comme faisant partie de sa présentation de la guerre.
Stylistic traditions and conjunctural constraints. The Palaeolithic female figurine from Piatra Neamț
Authors: Cârciumaru Marin
Published: January 2025

The first Palaeolithic female figurine in Romania was discovered in 2019. It was found near a hearth, in a level assigned to the Early Epigravettian, in the Piatra Neamț 1 settlement. 14 C dates have provided the age of 17,200 BP. The figurine was carved on a sandstone pebble collected from the alluviums of the Bistrița River near the settlement. The flat shape of the pebble required certain stylistic restrictions specific to figurines of this age. In particular, those anatomical parts suggesting obesity, such as breasts and buttocks, could not be volumetrically represented at revealing dimensions.