bluuebunny/crossref_metadata_2025_split huggingface dataset:
Aspirations versus adaptive achievements in the face of climate change in Sri Lanka
Authors: van den Berg Julia , Mallick Bishawjit 
Published: July 2024

AbstractThis research explores the linkages between threat perception and adaptive behaviour in the face of climate change in Galle, Sri Lanka. We use an analytical framework to analyse the complexity of aspiration versus adaptive practices, centring around the synthesis of threat perception, vulnerability, adaptive capacity and livelihood resilience. Our findings reveal that individual threat perception incentivises climate adaptation strategies, bolstering livelihood resilience. However, unfavourable societal circumstances affecting the participants significantly impede the implementation of these resilience strategies. In addition to a knowledge gap and lack of climate change awareness in popular discourse, we observe a conspicuous discrepancy between individual aspirations and adaptive capacity with the ongoing economic crisis and the need for governmental climate legislation at the time of research. This research, therefore, emphasises a nuanced approach to researching climate change adaptation, considering the gravity of societal circumstances that delimit how individuals can engage in livelihood resilience strategies.
A novel colorectal cancer test combining microsatellite instability and BRAF/RAS analysis: Clinical validation and impact on Lynch syndrome screening
Authors: Gallon Richard , Herrero-Belmonte Patricia , Phelps Rachel , Hayes Christine , Sollars Elizabeth , Egan Daniel , Spiewak Helena , Nalty Sam , Mills Sarah , Loo Peh Sun , Borthwick Gillian M. , Santibanez-Koref Mauro , Burn John , McAnulty Ciaron , Jackson Michael S. 
Published: July 2024

Abstract
Background
Lynch syndrome (LS) is under-diagnosed. UK National Institute for Health and Care Excellence guidelines recommend multistep molecular testing of all colorectal cancers (CRCs) to screen for LS. However, the complexity of the pathway has resulted in limited improvement in diagnosis.

Methods
One-step multiplex PCR was used to generate sequencing-ready amplicons from 14 microsatellite instability (MSI) markers and 22 BRAF, KRAS, and NRAS mutation hotspots. MSI and BRAF/RAS variants were detected using amplicon-sequencing and automated analysis. The assay was clinically validated and deployed into service in northern England, followed by regional and local audits to assess its impact.

Results
MSI analysis achieved 99.1% sensitivity and 99.2% specificity and was reproducible (r = 0.995). Mutation hotspot analysis had 100% sensitivity, 99.9% specificity, and was reproducible (r = 0.998). Assay-use in service in 2022–2023 increased CRC testing (97.2% (2466/2536) versus 28.6% (601/2104)), halved turnaround times, and identified more CRC patients at-risk of LS (5.5% (139/2536) versus 2.9% (61/2104)) compared to 2019–2020 when a multi-test pathway was used.

Conclusion
A novel amplicon-sequencing assay of CRCs, including all biomarkers for LS screening and anti-EGFR therapy, achieved >95% testing rate. Adoption of this low cost, scalable, and fully automatable test will complement on-going, national initiatives to improve LS screening.
Transmission dynamics of avian influenza viruses in Egyptian poultry markets
Authors: Mahmoud Sara H. , Gomaa Mokhtar , El Taweel Ahmed , Moatasim Yassmin , Kamel Mina Nabil , El Sayes Mohamed , Abo Shama Noura M. , Badra Rebecca , Mahmoud Mona , McKenzie Pamela P. , Webby Richard J. , Kandeil Ahmed , Ali Mohamed Ahmed , El-Shesheny Rabeh , Kayali Ghazi 
Published: July 2024

AbstractLive bird markets (LBMs) are considered hotspots for Avian Influenza Viruses (AIVs). In such markets, AIVs pose threats to both poultry and public health. Within LBMs, AIVs spread through various routes, including direct contact, environmental contamination, and aerosol transmission. Unique factors in Egyptian LBMs, such as the coexistence of wild and domestic birds, increase transmission risks between birds as well as spill-overs into exposed humans. Understanding the transmission dynamics of AIVs is vital for implementing effective control measures. We conducted a study in four Egyptian LBMs located in Mediterranean coast cities from November 2021 to March 2023. In this study we tested 3,971 samples from poultry, wild birds, and the environment, out of which 692 (17.4%) were positive for AIV. Poultry exhibited a higher prevalence (42.2%) than wild birds (34.4%). Environmental samples, including water (30.8%), surfaces (17.2%), and air (18.2%), also tested positive for AIV. Diverse AIV subtypes, including H5N1, H9N2, H5/H9 co-infection, and H5N8, were detected among avian species and the environment. Temporal analysis revealed fluctuating IAV positivity rates from November 2021 to March 2023. These results emphasize the importance of continuous surveillance, resource allocation, and multisectoral collaboration to protect poultry and human health, and prevent novel influenza strains’ emergence in Egyptian LBMs.
Emergence of exchange bias in van der Waals magnetic alloy CrxPt1−xTe2
Authors: Bailey-Crandell Ryan , Huey Warren L. B. , Williams Archibald J. , Zhou Wenyi , Goldberger Joshua E. , Kawakami Roland K. 
Published: July 2024

AbstractCrxPt1−xTe2 is a recently developed van der Waals magnetic alloy noted for its stability under ambient conditions. Here, we report the emergence of an exchange bias effect in CrxPt1−xTe2, without typical exchange bias sources such as an adjacent antiferromagnetic layer. We find that the exchange bias is present for x = 0.45 and absent for x = 0.35, which is correlated to the presence of a Cr modulation where the Cr concentration alternates each vdW layer (modulation period of 2 layers) for x ≥ 0.4. We perform Monte Carlo simulations utilizing exchange parameters from first-principles calculations, which recreate the exchange bias in hysteresis loops of Cr0.45Pt0.55Te2. From our simulations, we infer the source of exchange bias to be magnetic moments locked into free energy minima that resist magnetization reversal. This work presents a way to introduce desirable magnetic properties to van der Waals magnets.
Magnetic anisotropy and GGG substrate stray field in YIG films down to millikelvin temperatures
Authors: Serha Rostyslav O. , Voronov Andrey A. , Schmoll David , Verba Roman , Levchenko Khrystyna O. , Koraltan Sabri , Davídková Kristýna , Budinská Barbora , Wang Qi , Dobrovolskiy Oleksandr V. , Urbánek Michal , Lindner Morris , Reimann Timmy , Dubs Carsten , Gonzalez-Ballestero Carlos , Abert Claas , Suess Dieter , Bozhko Dmytro A. , Knauer Sebastian , Chumak Andrii V. 
Published: July 2024

AbstractQuantum magnonics investigates the quantum-mechanical properties of magnons, such as quantum coherence or entanglement for solid-state quantum information technologies at the nanoscale. The most promising material for quantum magnonics is the ferrimagnetic yttrium iron garnet (YIG), which hosts magnons with the longest lifetimes. YIG films of the highest quality are grown on a paramagnetic gadolinium gallium garnet (GGG) substrate. The literature has reported that ferromagnetic resonance (FMR) frequencies of YIG/GGG decrease at temperatures below 50 K despite the increase in YIG magnetization. We investigated a 97 nm-thick YIG film grown on 500 μm-thick GGG substrate through a series of experiments conducted at temperatures as low as 30 mK, and using both analytical and numerical methods. Our findings suggest that the primary factor contributing to the FMR frequency shift is the stray magnetic field created by the partially magnetized GGG substrate. This stray field is antiparallel to the applied external field and is highly inhomogeneous, reaching up to 40 mT in the center of the sample. At temperatures below 500 mK, the GGG field exhibits a saturation that cannot be described by the standard Brillouin function for a paramagnet. Including the calculated GGG field in the analysis of the FMR frequency versus temperature dependence allowed the determination of the cubic and uniaxial anisotropies. We find that the total crystallographic anisotropy increases more than three times with the decrease in temperature down to 2 K. Our findings enable accurate predictions of the YIG/GGG magnetic systems behavior at low and ultralow millikelvin temperatures, crucial for developing quantum magnonic devices.
Spintronic devices for biomedical applications
Authors: Mostufa Shahriar , Liang Shuang , Chugh Vinit Kumar , Wang Jian-Ping , Wu Kai 
Published: July 2024

AbstractIn the past decade, there has been a significant rise in the development of novel spintronic device architectures specifically designed to meet the demands of diverse biomedical applications. These advancements have notably focused on enhancing various bioassay detection techniques, including magnetocardiography and neural signal recording. Through collaboration within the spintronics community, these devices are rapidly transitioning from laboratory prototypes to practical applications, catering to diverse biomedical applications and benefiting both researchers and medical practitioners alike. In this review, we comprehensively explore the biomedical applications of spintronic devices, due to their inherent sensitivity to external magnetic fields, ease of fabrication into large arrays of nano/micro-sized devices within confined spaces, resilience under harsh environmental conditions, and high repeatability. Established spintronics devices that exploit various magnetoresistive effects have already been extensively deployed as magnetic biosensors for disease diagnosis, medical imaging, and bio-magnetic field detection, offering superior sensitivity and robustness. This review aims to provide peers with an up-to-date overview of spintronic devices in biomedical contexts while also commenting on future research trends and challenges. With advancements in nano/microfabrication techniques enhancing device robustness and magnetic field sensitivity, it is foreseeable that these spintronic devices could catalyze revolutionary transformations in healthcare.
Frequency-modulated timer regulates torpor–arousal cycles during hibernation in distinct small mammalian hibernators
Authors: Gibo Shingo , Yamaguchi Yoshifumi , Gracheva Elena O. , Bagriantsev Sviatoslav N. , Tokuda Isao T. , Kurosawa Gen 
Published: July 2024

AbstractHibernation allows mammals to endure harsh seasons by reducing their basal metabolism and body temperature (Tb) to minimize energy expenditure. During hibernation in small animals such as Syrian hamsters and 13-lined ground squirrels, Tb decreases to an ambient level ( < 5 °C) and remains constant for days to weeks in a physiological condition termed deep torpor. Torpor is interrupted by periods of arousal, during which Tb recovers to a euthermic level (approximately 37 °C), and these torpor–arousal cycles are repeated multiple times during hibernation. However, little is known about the mechanisms governing Tb fluctuations during hibernation. In this study, we employed an unbiased model selection approach to Tb data and revealed that a model incorporating frequency modulation quantitatively reproduced Tb fluctuation during hibernation in Syrian hamsters. We found that an unexpectedly long period of 120–430 days modulates a shorter period of several days. In addition, the aforementioned model reproduced Tb fluctuation in 13-lined ground squirrels, which can undergo repeated hibernation according to intrinsic circannual rhythms in constant laboratory conditions. This is the first quantitative study to demonstrate the concerted action of two endogenous periods, one lasting a few days and the other lasting a year, in the torpor–arousal cycles of distinct mammalian hibernators. We anticipate that our theoretical analysis of Tb fluctuation will be a starting point for quantitative comparisons of hibernation patterns across various hibernating species. Furthermore, quantification of Tb data using models will foster our understanding of the molecular mechanisms of hibernation by revealing the biological processes operating within these periods.
Fusing crops representation into snippet via mutual learning for weakly supervised surveillance anomaly detection
Authors: Zhang Bohua , Xue Jianru 
Published: July 2024

AbstractIn recent years, the challenge of detecting anomalies in real‐world surveillance videos using weakly supervised data has emerged. Traditional methods, utilising multi‐instance learning (MIL) with video snippets, struggle with background noise and tend to overlook subtle anomalies. To tackle this, the authors propose a novel approach that crops snippets to create multiple instances with less noise, separately evaluates them and then fuses these evaluations for more precise anomaly detection. This method, however, leads to higher computational demands, especially during inference. Addressing this, our solution employs mutual learning to guide snippet feature training using these low‐noise crops. The authors integrate multiple instance learning (MIL) for the primary task with snippets as inputs and multiple‐multiple instance learning (MMIL) for an auxiliary task with crops during training. The authors’ approach ensures consistent multi‐instance results in both tasks and incorporates a temporal activation mutual learning module (TAML) for aligning temporal anomaly activations between snippets and crops, improving the overall quality of snippet representations. Additionally, a snippet feature discrimination enhancement module (SFDE) refines the snippet features further. Tested across various datasets, the authors’ method shows remarkable performance, notably achieving a frame‐level AUC of 85.78% on the UCF‐Crime dataset, while reducing computational costs.
Prediction of voltage stability index in buses without measurement in distribution systems
Authors: Hemmatpour Mohammad Hasan 
Published: June 2024

AbstractIn power systems, voltage collapse during overload can be a significant threat. Accurate forecasting of critical operational conditions within power grids is crucial for preventing such situations. Precise predictions of voltage collapse enable operators to monitor the system closely and implement necessary corrective measures promptly, avoiding potential issues. However, monitoring networks can be costly due to the numerous loads and transformers in the distribution system. A comprehensive approach known as the voltage stability index (VSI) forecast without measurement buses (VFWMB) has been introduced to address this challenge. This approach involves innovative methods, including the seeking observation zone with weight least square (SOZWLS) technique for determining the number and location of measurements in the network based on its topology. Additionally, short‐term load forecasting is performed using the long short‐term memory (LSTM) forecasting method, followed by voltage estimation for buses without measurements. Finally, the proposed method calculates the modern voltage stability index for distribution systems (MVSIDS) for upcoming hours. All indicators and techniques in the VFWMB method have been validated. The algorithm has been thoroughly tested on various networks, including small and large, balanced and unbalanced, and both real and test networks, showing high efficiency in the electricity industry.
A metaheuristic image cryptosystem using improved parallel model and many‐objective optimization
Authors: Xin Zihao , Liu Sheng , Zhou Xiaoyi , Han Wenbao , Ma Jianqiang 
Published: July 2024

AbstractMetaheuristic is one of the techniques to improve the security of image encryption. However, existing metaheuristic image cryptosystems based on metaheuristic may have convergence difficulties during the optimization process, which cause insecurity and slow convergence. Besides, the time cost of the parallel execution model applied to metaheuristic image cryptosystems is not low enough. Therefore, a parallel many‐objective optimized key generation framework is proposed. Firstly, if four or more security indicators of cryptosystem, which are the results of security test, need to be optimized, the many‐objective optimization algorithm is employed to the proposed framework. With method adjusts the chaotic system parameters as the optimization key, which effectively avoid the convergence difficulty of the encryption key. Secondly, a master‐slave parallel model is improved to metaheuristic encryption. The model allocates the most time‐consuming fitness calculation work to slave nodes, which makes the modified model more reasonable and thus reduces the encryption time. To evaluate the performance of the proposed framework, a specific encryption scheme is constructed, that utilizes a 2D quadric map and many‐objective optimization algorithm based on dominance and decomposition (MOEA/DD) to optimize five security indicators. Experimental results reveal that this scheme has good security performances and less parallel encryption time.
Machine learning‐assisted anomaly detection for power line components: A case study in Pakistan
Authors: Basit Abdul , Manzoor Habib Ullah , Akram Muhammad , Gelani Hasan Erteza , Hussain Sajjad 
Published: July 2024

AbstractA continuous supply of electricity is necessary to maintain an acceptable standard of life, and the power distribution system's overhead line components play a crucial role in this matter. In Pakistan, identifying defective parts often necessitates human involvement. An unmanned aerial vehicle was used to gather a collection of 10,343 photos to automate this procedure. Using supervised and unsupervised machine learning methods, a number of automated anomaly detection systems were created. Support vector machine, random forest, VGG16, and ResNet50 were used as supervised machine learning models, and a convolutional auto‐encoder was used as the unsupervised machine learning model. VGG16 achieved the best accuracy of 99.00% while random forest achieved the worst accuracy of 72.49%. The convolutional auto‐encoder was successful in distinguishing between normal and abnormal components. The aforementioned machine learning models can be put on unmanned aerial vehicles to immediately identify defective parts.
Computer vision for eye diseases detection using pre‐trained deep learning techniques and raspberry Pi
Authors: Al‐Naji Ali , Khalid Ghaidaa A. , Mahmood Mustafa F. , Chahl Javaan 
Published: July 2024

AbstractEarly diagnosis of eye diseases is very important to prevent visual impairment and guide appropriate treatment methods. This paper presents a unique approach that can detect numerous eye diseases automatically. Initially, this approach used the pre‐trained ImageNet models that provides various pre‐trained models for training the acquired data. The existing data sets are composed of 645 data images acquired clinically, represented by two groups of subjects as healthy and others holding the proposed eye defect like cataracts, foreign bodies, glaucoma, subconjunctival haemorrhage, and viral conjunctivitis. Followed by comparisons of the pre‐trained model's coefficients and prediction performance. Later, the first‐class execution model is integrated within the Raspberry Pi staging and the real‐time digital camera detection. The evaluation process used the confusion matrix, model accuracy, precision factor, recall coefficient, F1 score, and the Matthews Correlation Coefficient (MCC). Resulting in the performance of these pre‐trained ImageNet models used in this study represented by 93% (InceptionResNetV2), 90% (MobileNet), 86% (Residual Network ResNet50), 85% (InceptionV3), 78% (Visual Geometry Group VGG19), and 72% (Neural Architecture Search Network NASNetMobile). The results show that the InceptionResNetV2 achieved the highest performance. This proposed approach shows its efficiency and strength by early detection of the subject's unhealthy eyes through real‐time monitoring in the field of ophthalmology.
The Cygnus Allscale Survey of Chemistry and Dynamical Environments: CASCADE
Authors: Christensen I. Barlach , Wyrowski F. , Veena V. S. , Beuther H. , Semenov D. , Menten K. M. , Jacob A. M. , Kim W.-J. , Cunningham N. , Gieser C. , Hacar A. , Li S. , Schneider N. , Skretas I. , Winters J. M. 
Published: July 2024

Context. Deuterated molecules and their molecular D/H-ratios (RD (D)) are important diagnostic tools with which to study the physical conditions of star-forming regions. The degree of deuteration, RD (D), can be significantly enhanced over the elemental D/H-ratio depending on physical parameters such as temperature, density, and the ionization fraction.
Aims. Within the Cygnus Allscale Survey of Chemistry and Dynamical Environments (CASCADE), we aim to explore the large-scale distribution of deuterated molecules in the nearby (d ~ 1.5 kpc) Cygnus-X region, a giant molecular cloud complex that hosts multiple sites of high-mass star formation. We focus on the analysis of large-scale structures of deuterated molecules in the filamentary region hosting the prominent HII region DR21 and DR21(OH), a molecular hot core that is in an earlier evolutionary state.
Methods. The DR21 filament has been imaged using the IRAM 30-m telescope in a variety of deuterated molecules and transitions. Here, we discuss the HCO+, HNC, and HCN molecules and their deuterated isotopologs DCO+, DNC, and DCN, and their observed line emissions at 3.6, 2, and 1.3 mm.
Results. The spatial distributions of integrated line emissions from DCO+, DNC, and DCN reveal morphological differences. Notably, DCO+ displays the most extended emission, characterized by several prominent peaks. Likewise, DNC exhibits multiple peaks, although its emission appears less extended compared to DCO+. In contrast to the extended emission of DCO+ and DNC, DCN appears the least extended, with distinct peaks. Focusing only on the regions where all three molecules are observed, the mean deuteration ratios for each species, RD, are 0.01 for both DNC and DCN, and = 0.005 for DCO+, respectively. Anticorrelations are found with deuterated molecules and dust temperature or N(H2).
Conclusions. The strongest anticorrelation is found with RD(DCO+) and N(H2), with a Pearson correlation coefficient of ρ = −0.74. We analyzed the SiO emission as a tracer for shocks and the N (HCO)/N (H13CO+) as a tracer for increased photodissociation by ultraviolet radiation. It is suggested that the anticorrelation of RD (DCO+) and N(H2) is a result of a combination of an increased photodissociation degree and shocks. A strong positive correlation between the ratio of integrated intensities of DCN and DNC with their 13C-isotopologs is found in high-column-density regions. The positive relationship between the ratios implies that the D-isotopolog of the isomers could potentially serve as a tracer for the kinetic gas temperature.