bluuebunny/crossref_metadata_2025 huggingface dataset:
Large, rugged and remote: The challenge of wolf–livestock coexistence on federal lands in the American West
Authors: Anderson Robert M. , Charnley Susan , Martin Jeff Vance , Epstein Kathleen
Published: August 2024

The expansion of grey wolves (Canis lupus) across the western United States, including on public lands used for extensive livestock grazing, requires tools and techniques for reducing wolf–livestock conflict and supporting coexistence. We examined approaches used on forested lands managed by the U.S. Forest Service, which we characterize as large, rugged and remote (LRR) landscapes. Research on the spatial aspects of where tools and techniques are deployed and their effectiveness across geographic settings is scant.

We selected six national forests located in six western states where managers seek to mitigate ongoing wolf–livestock conflict and conducted semistructured qualitative interviews with stakeholders (n = 69) to discuss approaches to conflict mitigation and perceptions of the effectiveness of different tools and techniques.

We developed a three‐part typology categorizing tools and techniques for conflict reduction: livestock husbandry, non‐lethal deterrents and targeted lethal removal of wolves. Across these categories, interviewees noted that many approaches are challenging to scale up to the geographic context of Forest Service grazing allotments and LRR landscapes.

The techniques perceived by our informants to be most effective for mitigating conflict in LRR landscapes include: human presence (mobile range riders); flexibility in grazing management; husbandry practices to minimize livestock vulnerability; and targeted lethal removal of wolves (used in combination with non‐lethal techniques).

Social, economic and political factors also influence the ability of producers to implement desired conflict mitigation tools in the national forest context. For example, the economic costs of implementation in LRR landscapes, and policies associated with grazing on federal lands, can limit mitigation options.

Policy implications. Geography matters! Efforts to address wolf–livestock conflict are more likely to be effective if they are designed to fit the local geographic context. Effectiveness may also increase if mitigation efforts go beyond technical approaches, considering the social, economic and institutional context in which conflict is embedded.

Read the freePlain Language Summaryfor this article on the Journal blog.
The impact of migrant remittances on poverty reduction in Bangladesh
Authors: Dey Sima R. , Basak Bishwajit
Published: August 2024

This study aims to assess the impact of international remittances on poverty alleviation in Bangladeshi migrant families using household surveys. A Multi‐Stage Stratified Random Sampling approach was employed to collect data from four upazilas of the Tangail and Comilla districts. The propensity score matching (PSM) method is used to evaluate the impact of returnee workers' remittances on household economic welfare that to ensure poverty alleviation. The average treatment effect in treated outcomes using the PSM technique demonstrates that the land asset, income, and expenditure scenario of migrant households has improved significantly compared to nonmigrants, indicating a notable reduction in poverty. Moreover, international remittances also improved savings and investment status substantially, along with wealth accrual and land possession. Hence, international remittances proved to be a boon for migrant families as remittances drove returnee households towards lower poverty in Bangladesh. Intervention programs that include financial literacy components (including digital banking) for safe money transfer and low interest bank loans for migration costs are required to maintain remittance inflows and sustainable reductions in poverty.
Revamping hemothorax management: The promise of low‐dose intrapleural fibrinolytic therapy as an alternative
Authors: Mohamad Jailaini Mas Fazlin , Hashim Yusra , Abdul Hamid Mohamed Faisal
Published: August 2024

Surgical evacuation has long been the standard treatment for hemothorax. However, some patients are not suitable candidates for surgery. Intrapleural fibrinolytic therapy (IPFT) has recently emerged as an effective alternative for managing retained hemothorax. This case report describes two patients with retained hemothorax who were unfit for surgery and were successfully treated with IPFT at our centre. Both patients were deemed unsuitable for surgery due to comorbidities and their overall functional status. They received three cycles of IPFT, each consisting of 2.5 mg of alteplase. This treatment effectively evacuated the retained hemothorax, achieving complete radiological resolution without immediate or delayed complications up to 3 months post‐discharge.
Highly Selective Recovery of Gold by In Situ Magnetic Field‐Assisted Fe/Co‐MOF@PDA/NdFeB Double Network Gel
Authors: Ren Jianran , Li Qiang , Zhu Zhiliang , Qiu Yanling , Yu Fei , Zhou Tao , Yang Xue , Ye Kang , Wang Yabo , Ma Jie , Zhao Jianfu
Published: August 2024

There are enormous economic benefits to conveniently increasing the selective recovery capacity of gold. Fe/Co‐MOF@PDA/NdFeB double‐network organogel (Fe/Co‐MOF@PDA NH) is synthesized by aggregation assembly strategy. The package of PDA provides a large number of nitrogen‐containing functional groups that can serve as adsorption sites for gold ions, resulting in a 21.8% increase in the ability of the material to recover gold. Fe/Co‐MOF@PDA NH possesses high gold recovery capacity (1478.87 mg g−1) and excellent gold selectivity (Kd = 5.71 mL g−1). With the assistance of an in situ magnetic field, the gold recovery capacity of Fe/Co‐MOF@PDA NH is increased from 1217.93 to 1478.87 mg g−1, and the recovery rate increased by 24.7%. The above excellent performance is attributed to the efficient reduction of gold by FDC/FC+, Co2+/Co3+ double reducing couple, and the optimization of the reduction reaction by the magnetic field. After the samples are calcined, high‐purity gold (95.6%, 22K gold) is recovered by magnetic separation. This study proposes a forward‐looking in situ energy field‐assisted strategy to enhance precious metal recovery, which has a guiding role in the development of low‐carbon industries.
Magnetic Monopole‐Like Behavior in Superparamagnetic Nanoparticle Coated With Chiral Molecules
Authors: Zhu Qirong , Cohen Sidney R. , Brontvein Olga , Fransson Jonas , Naaman Ron
Published: August 2024

Superparamagnetic iron oxide nanoparticles (SPIONs) have attracted wide attention due to their promising applications in biomedicine, chemical catalysis, and magnetic memory devices. In this work, the force is measured between a single SPION coated with chiral molecules and a ferromagnetic substrate by atomic force microscopy (AFM), with the substrate magnetized either toward or away from the approaching AFM tip. The force between the coated SPION and the magnetic substrate depends on the handedness of the molecules adsorbed on the SPION and on the direction of the magnetization of the substrate. By inserting nm‐scale spacing layers between the coated SPION and the magnetic substrate it is shown that the SPION has a short‐range magnetic monopole‐like magnetic field. A theoretical framework for the nature of this field is provided.
Lattice Strain in High Entropy Oxides Promote CO2 Photomethanation
Authors: Shi Xian , Dai Weidong , Li Xiaoqian , Yu Yangyang , Zhu Zirui , Cui Zhihao , Dong Xing'an
Published: August 2024

Lattice strain is widely investigated to improve the performance of heterogeneous catalysts, however, the effect of lattice strain is under‐explored in high‐entropy oxide based photocatalyst. In this study, noble‐metal‐free (CoCrMnFeNi)Ox with lattice strain is synthesized using a temperature‐controlled, template‐free and salt‐assisted strategy. In the presence of lattice strain, an intensive internal electric field is formed in (CoCrMnFeNi)Ox, promoting the separation of photoinduced charge carriers. The size of the (CoCrMnFeNi)Ox can be tuned by varying the calcination temperature. Specifically, (CoCrMnFeNi)Ox prepared at a higher temperature possesses a smaller grain size exposing more active sites, resulting in an enhanced CO2 photomethanation performance. This work provides valuable insights for the rational design of the photocatalysts and highlights the promising role of high‐entropy oxides in heterogeneous photocatalysis.
Nonlinear global design resistance: Case studies of post‐tensioned concrete bridges made of I‐73 and KA‐61 girders
Authors: Lipowczan Martin , Novák Lukáš , Lehký David , Novák Drahomír
Published: August 2024

The paper portrays a comprehensive computational procedure for determining the global structural resistances of two existing bridges made of I‐73 and KA‐61 precast post‐tensioned concrete girders using advanced statistical assessment methods in combination with nonlinear fracture mechanics‐based finite element method analysis. Although this combination is a powerful tool for realistic modeling of structures, its practical application is still very time consuming. Therefore, a statistical sampling approach for the determination of the structural design resistance is compared to selected efficient semi‐probabilistic methods based on the estimation of coefficient of variation—estimation of coefficient of variation (ECoV) method according to fib Model Code 2010 and improved approach called Eigen ECoV method. Load‐bearing capacity is determined for the ultimate as well as several serviceability limit states. The sensitivity of the input parameters burdened with uncertainties on the response of the structure is quantified using a sensitivity analysis supported by a surrogate model based on polynomial chaos expansion. The paper shows that the applicability of nonlinear modeling with respect to uncertainties is possible when using these ECoV methods and a surrogate model and can be applied in a routine manner. The shortcomings and advantages of all the used safety design/assessment methods are discussed.
Anchorage capacity of bent looped wire ropes in precast concrete wall elements for T‐ and L‐ connections
Authors: Veyhe Torkil , Jørgensen Henrik Brøner , Hansen Søren Gustenhoff
Published: August 2024

Within the last decade, a construction‐friendly design principle using flexible wire ropes instead of traditional u‐bars for connecting wall elements has gained popularity. However, anchoring wire ropes in T and L connections remains a complex challenge. Here, the wire ropes stick out of the side of the wall element and are bent in the anchorage zone. This paper presents the findings of an extensive experimental program with 40 specimens, investigating the anchorage capacity of wire ropes in wall elements under these conditions. The study reveals that specially designed reinforcements significantly increase the anchorage capacity, while traditional wall reinforcements have minimal impact. Failure mechanisms primarily involve concrete cone failures, providing valuable insights for construction‐friendly connections. Based on the observed failure mechanism, the paper presents an analytical failure mechanism. The model is based on the upper‐bound theorem of plasticity theory. The model predicts both the capacity and the failure mechanism with satisfactory accuracy.
Digital Transformation and AI in Energy Systems: Applications, Challenges, and the Path Forward
Authors: Olson Eric
Published: August 2024

The integration of digital technologies like Machine Learning (ML), Artificial Intelligence (AI), and the Internet of Things is transforming energy systems. This digital transformation aims to enhance efficiency, sustainability, and resilience in power generation, transmission, and consumption. A key focus is developing smart grids that leverage real-time data and intelligent algorithms to optimise operations. In response, deep learning and reinforcement learning techniques are being applied to bolster cybersecurity in the energy sector. Deep learning excels at detecting threats by identifying patterns in large datasets. Meanwhile, reinforcement learning can simulate attack scenarios to train adaptive defence strategies. However, cybersecurity threats pose a major risk as energy infrastructure becomes more interconnected. The Colonial Pipeline ransomware attack in 2021 demonstrated the vulnerabilities of critical infrastructure to cyberattacks. Despite great potential, challenges remain regarding model transparency, ethics, and data availability. Overall, realising the promise of AI in the energy sector requires navigating technical complexities and prioritising explainable, trustworthy systems. If implemented thoughtfully, these technologies can catalyse the transition to smarter, more efficient, resilient, and sustainable energy systems.
Introduction: Setting the Scene
Authors: Tausch Arno
Published: August 2024

This chapter sets the scene for our research. It starts from the assumption that homonegativity is not only a matter of growing scientific interest in the global scientific community, but also of growing concern for public security and political stability worldwide. The death penalty for homosexuality exists in Afghanistan, Brunei, Iran, Mauretania, Nigeria, Pakistan, Qatar, Saudi Arabia, Somalia, Uganda, the United Arab Emirates and Yemen. It outlines the basic research question of this publication: to estimate homonegativity in 88 countries and territories around the world, using open data from the World Values Survey, and to examine the relationship between homonegativity and religiously motivated political extremism. It outlines the structure of our study.
Climate Change-Related Displacement: Inter-Island and Rural–Urban Migration in the Solomon Islands: Options for a Viable National Resettlement Plan
Authors: Otoiasi Lovelyn
Published: August 2024

Global environmental change has been projected to have a greater impact on human mobility across the globe. In the recent past, the movement of people between islands in the Solomon Islands was voluntary, either through marriage or other social interactions. However, recently climate change-related impacts have begun to force people to move, particularly from the low-lying atolls. Although there is a drive from the international community to address climate change-related resettlement and relocation, there is an urgent need to better understand local migration intervening factors and address these intervening obstacles to the resettlement programme in the Solomon Islands. This chapter discusses the intervening aspects of climate change-related migration and rural–urban migration to Honiara City, the capital of Solomon Islands, the options to address resettlement challenges, and makes recommendations for institutional, legal, and financial mechanisms for the nation’s Resettlement Programme. It is hoped that discussions from this chapter will provide the needed insights and a better understanding of various social, economic, and cultural attributes of climate-related human mobility in relation to Honiara and thereby inform the development and implementation of sound and viable legal and policy measures.
Climate Change, Agriculture, and Internal Human Mobility in the Bhutan Himalayas
Authors: Katel Om N. , Nair Anooja , Yangchen Ugyen , Wangmo Chogyel
Published: August 2024

When it comes to the impact of climate change, Bhutan is one of the world’s most vulnerable countries. Although Bhutan has not contributed anything to cause climate change, it is facing the brunt of it impact. Bhutan’s climate is strongly influenced by the rugged mountainous terrain surrounding it as well as the Indian summer monsoon and changes in climate, which are increasing the vulnerability of vital economic sectors such as agriculture, forestry, and water resources. Climate change also impacts the country’s rich biodiversity and fragile community livelihoods through the increased risks of hazardous events such as glacier lake outburst floods, flash floods, droughts, and forest fires. This chapter analyses the impacts of climate change on agricultural livelihoods and associated internal human mobility. Despite being a small country, Bhutan has been doing its best to adapt to the impacts of climate change, but its topography and inadequate resources due to the small economy have been constraining Bhutan's developmental philosophy of Gross National Happiness and its achievement of its global sustainable development goals.
Achieving productivity and operator well-being: a dynamic task allocation strategy for collaborative assembly systems in Industry 5.0
Authors: Calzavara Martina , Faccio Maurizio , Granata Irene , Trevisani Alberto
Published: August 2024

Collaborative robots, or cobots, offer a unique combination of productivity and flexibility that has led to significant growth in adoption over the past decade. Moreover, recently, there has been a shift towards a human-centered design of the workspace, known as one of the drivers of Industry 5.0, which prioritizes the well-being of operators. To achieve this, various human factors such as ergonomics, mental workload, personal skills, and capabilities need to be considered in the workspace design, and their impact on system productivity must be evaluated. The integration of a human and a cobot in the same workplace can affect the performance of the human operator, as the perception of the cobot can impact their work. This highlights the importance of taking human factors into account, as a lack of consideration in these aspects has contributed to the failure of many implementations. To link the objectives of productivity, flexibility, and human factors consideration, a dynamic real-time multi-objective task allocation strategy for collaborative assembly systems is developed. This approach considers the different characteristics of the resources and optimizes for two objectives, makespan, and energy expenditure of the operator. By using this approach, it is possible to modify the behavior of the cobot by reallocating tasks between the two resources based on the operator’s current needs. In other words, if the operator appears too stressed due to time constraints or their energy rate level is too high, some of their assigned tasks can be transferred to the cobot. This helps to maintain a balanced system while reducing the operator’s stress.
Insufficient compliance of the German Therapeutic Products Advertising Act in product catalogues of online pharmacies
Authors: Barlage Lara , Seifert Roland
Published: August 2024

The importance of ordering drugs from online pharmacies in Germany is increasing constantly. At the same time, there are many online pharmacies that try to increase their own market share through advertising. In Germany, the advertising of drugs is regulated by the Therapeutic Products Advertising Act (Heilmittelwerbegesetz (HWG)) and must therefore also be complied with by online pharmacies in their product catalogues. One important purpose of the HWG is to protect consumers by ensuring that they are presented with all necessary medical-pharmacological information about the advertised product. This paper examines the implementation of as well as the compliance with Section 4 of the HWG in the product catalogues of two online pharmacies. For this purpose, the spring/summer catalogue of 2023 was considered in the allergy, cold and gastrointestinal tract categories, resulting in the inclusion of 143 drugs from online pharmacy 1 (OP1) and 102 drugs from online pharmacy 2 (OP2) for the analysis. The information on the drugs was taken from the respective catalogue and the pharmaceutical index ‘Gelbe Liste’, collected in tabular form and was encoded. Subsequently, the required mandatory information according to Section 4 was checked by comparing the collected information with the information from the package leaflet. The analysis revealed that both catalogues insufficiently complied with Section 4 of the HWG. OP1 complied with 75% of the mandatory information required under Section 4 of the HWG and OP2 with 64%. In the OP1 catalogue, there is no indication for 6% of the drugs and no specification for 29% of the traditionally registered drugs. In addition, the mandatory notice on risks and adverse effects in the OP1 catalogue is not presented in a consumer-friendly way. In the OP2 catalogue, further concerns about the compliance with the HWG were raised due to the missing indication for 17% of the drugs, no warning for 64% of the ethanol-containing drugs and no active ingredient for 31% of the monopreparations. The insufficient compliance with the HWG in the product catalogues of OP1 and OP2 means that consumer protection as a stated objective of the HWG cannot be guaranteed. Advertising for drugs should only be possible under the condition that the content of the advertisement complies with the HWG. Insufficient compliance, as in the product catalogue of OP1 and OP2, should be prohibited as it directly contradicts the law’s objective.
Roadmap on computational methods in optical imaging and holography [invited]
Authors: Rosen Joseph , Alford Simon , Allan Blake , Anand Vijayakumar , Arnon Shlomi , Arockiaraj Francis Gracy , Art Jonathan , Bai Bijie , Balasubramaniam Ganesh M. , Birnbaum Tobias , Bisht Nandan S. , Blinder David , Cao Liangcai , Chen Qian , Chen Ziyang , Dubey Vishesh , Egiazarian Karen , Ercan Mert , Forbes Andrew , Gopakumar G. , Gao Yunhui , Gigan Sylvain , Gocłowski Paweł , Gopinath Shivasubramanian , Greenbaum Alon , Horisaki Ryoichi , Ierodiaconou Daniel , Juodkazis Saulius , Karmakar Tanushree , Katkovnik Vladimir , Khonina Svetlana N. , Kner Peter , Kravets Vladislav , Kumar Ravi , Lai Yingming , Li Chen , Li Jiaji , Li Shaoheng , Li Yuzhu , Liang Jinyang , Manavalan Gokul , Mandal Aditya Chandra , Manisha Manisha , Mann Christopher , Marzejon Marcin J. , Moodley Chané , Morikawa Junko , Muniraj Inbarasan , Narbutis Donatas , Ng Soon Hock , Nothlawala Fazilah , Oh Jeonghun , Ozcan Aydogan , Park YongKeun , Porfirev Alexey P. , Potcoava Mariana , Prabhakar Shashi , Pu Jixiong , Rai Mani Ratnam , Rogalski Mikołaj , Ryu Meguya , Choudhary Sakshi , Salla Gangi Reddy , Schelkens Peter , Şener Sarp Feykun , Shevkunov Igor , Shimobaba Tomoyoshi , Singh Rakesh K. , Singh Ravindra P. , Stern Adrian , Sun Jiasong , Zhou Shun , Zuo Chao , Zurawski Zack , Tahara Tatsuki , Tiwari Vipin , Trusiak Maciej , Vinu R. V. , Volotovskiy Sergey G. , Yılmaz Hasan , De Aguiar Hilton Barbosa , Ahluwalia Balpreet S. , Ahmad Azeem
Published: August 2024

Computational methods have been established as cornerstones in optical imaging and holography in recent years. Every year, the dependence of optical imaging and holography on computational methods is increasing significantly to the extent that optical methods and components are being completely and efficiently replaced with computational methods at low cost. This roadmap reviews the current scenario in four major areas namely incoherent digital holography, quantitative phase imaging, imaging through scattering layers, and super-resolution imaging. In addition to registering the perspectives of the modern-day architects of the above research areas, the roadmap also reports some of the latest studies on the topic. Computational codes and pseudocodes are presented for computational methods in a plug-and-play fashion for readers to not only read and understand but also practice the latest algorithms with their data. We believe that this roadmap will be a valuable tool for analyzing the current trends in computational methods to predict and prepare the future of computational methods in optical imaging and holography.
Lite approaches for long-range multi-step water quality prediction
Authors: Islam Md Khaled Ben , Newton M. A. Hakim , Trevathan Jarrod , Sattar Abdul
Published: August 2024

Forecasting accurate water quality is very important in aquaculture, environment monitoring, and many other applications. Many internal and external factors influence water quality. Therefore, water quality parameters exhibit complex time series characteristics. Consequently, long-range accurate prediction of water quality parameters suffers from poor propagation of information from past timepoints to further future timepoints. Moreover, to synchronise the prediction model with the changes in the time series characteristics, periodic retraining of the prediction model is required and such retraining is to be done on resource-restricted computation devices. In this work, we present a low-cost training approach to improve long-range multi-step water quality prediction. We train a short-range predictor to save training effort. Then, we strive to achieve and/or improve long-range prediction using multi-step iterative ensembling during inference. Experimental results on 9 water quality datasets demonstrate that the proposed method achieves significantly lower error than the existing state-of-the-art approaches. Our approach significantly outperforms the existing approaches in several standard metrics, even in the case of future timepoints at long distances.
Singularities of the hyperbolic elastic flow: convergence, quantization and blow-ups
Authors: Schlierf Manuel
Published: August 2024

We study the elastic flow of closed curves and of open curves with clamped boundary conditions in the hyperbolic plane. While global existence and convergence toward critical points for initial data with sufficiently small energy is already known, this study pioneers an investigation into the flow’s singular behavior. We prove a convergence theorem without assuming smallness of the initial energy, coupled with a quantification of potential singularities: Each singularity carries an energy cost of at least 8. Moreover, the blow-ups of the singularities are explicitly classified. A further contribution is an explicit understanding of the singular limit of the elastic flow of$$\lambda $$λ-figure-eights, a class of curves that previously served in showing sharpness of the energy threshold 16 for the smooth convergence of the elastic flow of closed curves.
In-vitro study on type I collagen synthesis in low-level laser therapy on the early ligament fibroblasts’ healing process
Authors: Cárdenas-Sandoval R. P. , Bernal-Bernal L. D. , Cabrera-Salazar S. , Gómez-Ramírez D. M. , González-Ballesteros L. M. , Hooker-Mendoza K. M. , Ospina-Piedrahíta L. N. , Hernández-Charry C. X. , Ardila-Rojas G. , Velásquez-Durán A. M. , Cucarián-Hurtado J. D. , Ondo-Méndez A. O. , Barbosa-Santibañez J. , Carvajal-Calderón L.L. , Navarrete-Jimenez M. L.
Published: August 2024

Background

Low-level Laser Therapy (LLLT) has demonstrated its potential in promoting fiber matrix maturation, collagen synthesis, and fibroblast proliferation, contributing to tissue regeneration. Our study aimed to investigate the impact of LLLT on collagen type I synthesis, cell proliferation, and viability in human ligament fibroblasts derived from the Anterior Cruciate Ligament (ACL).

Methods

Tissue samples were obtained from individuals undergoing arthroscopic ACL reconstruction surgery. Primary human fibroblasts were isolated, and immunohistochemical assays confirmed their characteristics. LLLT at 850 nm was administered in three groups: Low dose (1.0 J/cm²), High dose (5.0 J/cm²), and Control (0.0 J/cm²). Cell viability was calculated using a membrane integrity assay, proliferation was determined by automated counting, and collagen type I concentration in cell culture was measured using an immunoassay.

Results

Fibroblasts showed decreased viability after low and high doses of LLLT, increased proliferation at the low dose, and increased collagen synthesis at the high dose on day 10 for both sexes after treatment.

Conclusion

Our study demonstrated that LLLT may improve the early ligament healing process by increasing cell proliferation at the low dose and enhancing collagen type I synthesis at the high dose in human ligament fibroblasts.
Deep-learning-based image reconstruction with limited data: generating synthetic raw data using deep learning
Authors: Zijlstra Frank , While Peter Thomas
Published: August 2024

Object

Deep learning has shown great promise for fast reconstruction of accelerated MRI acquisitions by learning from large amounts of raw data. However, raw data is not always available in sufficient quantities. This study investigates synthetic data generation to complement small datasets and improve reconstruction quality.

Materials and methods

An adversarial auto-encoder was trained to generate phase and coil sensitivity maps from magnitude images, which were combined into synthetic raw data.

On a fourfold accelerated MR reconstruction task, deep-learning-based reconstruction networks were trained with varying amounts of training data (20 to 160 scans). Test set performance was compared between baseline experiments and experiments that incorporated synthetic training data.

Results

Training with synthetic raw data showed decreasing reconstruction errors with increasing amounts of training data, but importantly this was magnitude-only data, rather than real raw data. For small training sets, training with synthetic data decreased the mean absolute error (MAE) by up to 7.5%, whereas for larger training sets the MAE increased by up to 2.6%.

Discussion

Synthetic raw data generation improved reconstruction quality in scenarios with limited training data. A major advantage of synthetic data generation is that it allows for the reuse of magnitude-only datasets, which are more readily available than raw datasets.
SAD: semi-supervised automatic detection of BOLD activations in high temporal resolution fMRI data
Authors: Schmidt Tim , Nagy Zoltán
Published: August 2024

Objective

Despite the prevalent use of the general linear model (GLM) in fMRI data analysis, assuming a pre-defined hemodynamic response function (HRF) for all voxels can lead to reduced reliability and may distort the inferences derived from it. To overcome the necessity of presuming a specific model for the hemodynamic response, we introduce a semi-supervised automatic detection (SAD) method.

Materials and methods

The proposed SAD method employs a Bi-LSTM neural network to classify high temporal resolution fMRI data. Network training utilized an fMRI dataset with 75-ms temporal resolution in an iterative scheme. Classification performance was evaluated on a second fMRI dataset from the same participant, collected on a different day. Comparative analysis with the standard GLM approach was conducted to evaluate the cooperative effectiveness of the SAD method.

Results

The SAD method performed well based on the classification scores: true-positive rate = 0.961, area under the receiver operating curve = 0.998, true-negative rate = 0.99, F1-score = 0.979, False-negative rate = 0.038, false-discovery rate = 0.002, false-positive rate = 0.002 at 75-ms temporal resolution.

Conclusion

SAD can detect hemodynamic responses at 75-ms temporal resolution without relying on a specific shape of an HRF. Future work could expand the use cases to include more participants and different fMRI paradigms.
First year-round movement tracking of the Mediterranean Gull (Ichthyaetus melanocephalus) in Europe: insights and conservation recommendations for declining population
Authors: Jankowiak Łukasz , Sidelnik Marcin , Siekiera Joachim , Szymczak Jakub , Zieliński Piotr , Marchowski Dominik
Published: August 2024

This study examines the Mediterranean Gull (Ichthyaetus melanocephalus), utilizing GPS/GSM transmitters year-round for the first time on this species. The research covers habitat use, migration patterns, and breeding behaviors, tracking 19 birds captured at breeding colonies in Poland. To capture the birds and fit transmitters, we selected representative locations: islands in dam reservoirs, gravel pits, rivers, and lakes. Mediterranean Gulls nested mainly in colonies of Black-headed Gulls (Chroicocephalus ridibundus). By tracking birds with transmitters, we identified nine additional breeding sites within Poland, the Czechia, and Germany. We determined the mean length of phenological periods: breeding (76, April 10–June 26), autumn migration (11, June 26–July 7), winter (261, July 5–March 23), and spring migration (18, March 23–April 10). According to residence sites analysis, cropland was the most frequently used habitat during breeding (52.9%), while the open sea was predominant in winter (25.8%). We identified key wintering areas: the English Channel, the Utrecht–Antwerpen area, the North Sea coast, and the Irish Sea coast. One individual wintered along the coast of Portugal and near Malaga in Spain. An interesting discovery was the longer spring migration than autumn what involved visits to multiple potential breeding sites, leading to the mapping of a network of potential breeding areas. This underscores the need for proactive habitat protection and comprehensive conservation strategies. Given the Mediterranean Gull’s migratory behavior and lifecycle, the study advocates for it as a model for conserving migratory waterbirds.
Probability of entering an orthant by correlated fractional Brownian motion with drift: exact asymptotics
Authors: Dȩbicki Krzysztof , Ji Lanpeng , Novikov Svyatoslav
Published: August 2024

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Lived Experiences of Migrant Fathers in the Perinatal Period: A Systematic Review and Analysis
Authors: Vo Huy N. , McKenzie-McHarg Kirstie , Bennett Pauleen C. , Mai Dac L.
Published: August 2024

The worldwide population of migrant families is on the rise, and there is growing acknowledgement of the significance of supporting parental mental health within these families. However, understanding of the experiences of migrant fathers during the perinatal period remains incomplete. The objective of this review is to provide an overview of existing research on perinatal migrant fathers’ experiences in different cultures. Multiple searches were conducted in April 2023 for quantitative, qualitative, and mixed-methods studies across six electronic databases: Medline, CINAHL, Embase, PsycINFO, Web of Science, and Scopus. Fourteen eligible articles were identified, including nine qualitative studies, five quantitative studies, and no mixed-methods studies. The Mixed-methods Appraisal Tool was used to assess the quality of these studies. The quantitative findings were transformed into narrative summaries to be analysed thematically along with the qualitative data. Three themes were identified: (1) Cultural competence (dealing with cultural differences, needs related to original country); (2) Parenthood in a new country (challenges and adaptation to fatherhood, challenging traditional gender norms, lack of extended family and building new support networks, being the main supporter for the family); (3) Needs of the fathers and their personal difficulties. The findings of this review suggest a direction for future research in perinatal psychology. The review also provides insights into the need for social and community support for migrant fathers and how healthcare services can support this group during the perinatal period.
Epistemic Logics for Relevant Reasoners
Authors: Sedlár Igor , Vigiani Pietro
Published: August 2024

We present a neighbourhood-style semantic framework for modal epistemic logic modelling agents who process information using relevant logic. The distinguishing feature of the framework in comparison to relevant modal logic is that the environment the agent is situated in is assumed to be a classical possible world. This framework generates two-layered logics combining classical logic on the propositional level with relevant logic in the scope of modal operators. Our main technical result is a general soundness and completeness theorem.
Modelling spatiotemporal patterns of wildfire risk in the Garden Route District biodiversity hotspots using analytic hierarchy process in South Africa
Authors: Shinga Phindile Siyasanga , Tesfamichael Solomon G. , Sibandze Phila , Kalumba Ahmed Mukalazi , Afuye Gbenga Abayomi
Published: August 2024

The increasing frequency and intensity of wildfires necessitate effective risk management in biodiversity hotspots to mitigate the potential impacts of wildfire hazards. The study utilised a multi-criteria decision analysis-analytic hierarchy process (MCDA-AHP) model to analyse wildfire risk patterns in the Garden Route District (GRD), focusing on biodiversity hotspots in the Western Cape, South Africa. The study used weight assignment and overlay analysis to evaluate wildfire risk factors, including human, topographic, and climatic factors, using data from Landsat and WorldClim from 1991 to 2021. The wildfire risk model was validated using MODIS historical fire data from the Global Forest Watch database and Confusion Matrix, with the burned area extent identified using differenced Normalized Burn Ratio (dNBR). The results show that despite 53% of the most burned area, only 12% was burned, with the high-risk zone accounting for only 11%, indicating a higher likelihood of wildfires spreading and intensifying. The results reveal a weak positive correlation (r = 0.28) between historical fire occurrences and burned areas and a negative correlation (r = − 0.27) between historical fire occurrences and fire seasons. Human and climatic factors significantly impact wildfire propagation in high-risk zones, while topographic factors have less influence, indicating a lower risk of ignition. The findings show that 26% of high-risk zones in the southwestern region dominated GRD biodiversity hotspots, while 27% were in the low-moderate-risk zone in the northwestern parts. The results of this study can aid in assigning fire risk-based criterion weights to support decision-makers in regional and global wildfire prevention and management.
The Bergen–Yale Sexual Addiction Scale (BYSAS): Longitudinal Measurement Invariance Across a Two-Year Interval
Authors: Gomez Rapson , Brown Taylor , Stavropoulos Vasileios
Published: August 2024

The Bergen–Yale Sexual Addiction Scale (BYSAS; [1]) is arguably the most popular questionnaire at present for assessing sex addiction. Employing Confirmatory Factor Analysis (CFA) and treating item scores as ordered categorical, we applied Weighted Least Square Mean and Variance Adjusted Chi-Square (WLSMV) extraction to investigate the longitudinal measurement and structural invariance of ratings on the BYSAS among 276 adults (mean = 31.86 years; SD = 9.94 years; 71% male) over a two-year period, with ratings at three yearly intervals. Overall, there was support for configural invariance, full loading, full threshold, the full unique factor invariance; and all structural (latent variances and covariances) components. Additionally, there was no difference in latent mean scores across the three-time points. The psychometric and practical implications of the findings are discussed.
He/She/They - gender inclusivity in developing and using health-related questionnaires: a scoping review
Authors: Scott Desiree , Derrett Sarah , Rupel Valentina Prevolnik , Jelsma Jennifer , Gurung Gagan , Oduro Georgina Yaa , Withey-Rila Cassie
Published: August 2024

Purpose

To ensure the recognition and participation of all potential respondents in health research, surveys and care, including LGBTQIA + broadly, and trans people, specifically, the use of inclusive language should be considered. This scoping review aimed to identify and describe strategies considered for gender inclusivity in development and use of health questionnaires and Patient Reported Outcomes Measures (PROMs).

Methods

A systematic search of peer reviewed publications between January 2000 and September 2022 was conducted in Scopus, ProQuest Central, Ovid Medline (PubMed and EBSCO). Two reviewers independently screened identified publications titles and abstracts, followed by full text screening and data extraction from eligible articles.

Results

The search of over 5000 publications, retained 18; most acknowledged gaps in representation and advocated for gender-inclusive language. Eight articles discussed exclusion from health care and health research for gender minority groups due to the use of gendered language. Improved reliability, validity and response rates were associated with the use of gender-neutral language in seven articles. Only one article reported finding irritation among cisgender males when non-binary gender response options were used. One paper, focussing on instruments for Rheumatoid Arthritis, discussed gaps in representation if diverse gender identities were not considered when developing PROMs.

Conclusion

This scoping review points to the importance of adopting gender-inclusive language in health questionnaires and surveys to reduce the risk of excluding gender minority groups. Despite finding very few specific examples of how others have used gender-inclusive language in health questionnaires, many strengths of gender-inclusive language usage were identified.
First impressions and the built environment: exploring zero acquaintance judgments in socio-spatial contexts
Authors: Tooley Jack
Published: August 2024

The well-researched sociological concept known as Zero Acquaintance Judgment frames first impression scenarios and highlights their prevalence and importance to our everyday lives, yet sociology so far overlooks how these might be affected by the built environment where first impressions are typically situated. Broadly, spatial discriminatory discourse investigates how spaces can affect social judgments, yet no research has investigated how this dynamic might unfold within a first impression scenario. Using the Zero Acquaintance Judgment concept as a lens of inquiry, a comprehensive review of feminist, queer, racial, and disability frameworks (which comprise spatial discriminatory discourse) was undertaken. From this investigation, three broad theoretical themes were articulated and rationalized as to how the built environment can potentially affect first impressions between strangers. The discussion bears relevance for researchers and designers of spaces where first impressions are paramount to function as well as highlights how the settings of research into first impressions may affect outcomes.
A hybrid approach for efficient feature selection in anomaly intrusion detection for IoT networks
Authors: Ayad Aya G. , Sakr Nehal A. , Hikal Noha A.
Published: August 2024

The exponential growth of Internet of Things (IoT) devices underscores the need for robust security measures against cyber-attacks. Extensive research in the IoT security community has centered on effective traffic detection models, with a particular focus on anomaly intrusion detection systems (AIDS). This paper specifically addresses the preprocessing stage for IoT datasets and feature selection approaches to reduce the complexity of the data. The goal is to develop an efficient AIDS that strikes a balance between high accuracy and low detection time. To achieve this goal, we propose a hybrid feature selection approach that combines filter and wrapper methods. This approach is integrated into a two-level anomaly intrusion detection system. At level 1, our approach classifies network packets into normal or attack, with level 2 further classifying the attack to determine its specific category. One critical aspect we consider is the imbalance in these datasets, which is addressed using the Synthetic Minority Over-sampling Technique (SMOTE). To evaluate how the selected features affect the performance of the machine learning model across different algorithms, namely Decision Tree, Random Forest, Gaussian Naive Bayes, and k-Nearest Neighbor, we employ benchmark datasets: BoT-IoT, TON-IoT, and CIC-DDoS2019. Evaluation metrics encompass detection accuracy, precision, recall, and F1-score. Results indicate that the decision tree achieves high detection accuracy, ranging between 99.82 and 100%, with short detection times ranging between 0.02 and 0.15 s, outperforming existing AIDS architectures for IoT networks and establishing its superiority in achieving both accuracy and efficient detection times.
An optimized intelligent open-source MLaaS framework for user-friendly clustering and anomaly detection
Authors: ElDahshan Kamal A. , Abutaleb Gaber E. , Elemary Berihan R. , Ebeid Ebeid A. , AlHabshy AbdAllah A.
Published: August 2024

As data grow exponentially, the demand for advanced intelligent solutions has become increasingly urgent. Unfortunately, not all businesses have the expertise to utilize machine learning algorithms effectively. To bridge this gap, the present paper introduces a cost-effective, user-friendly, dependable, adaptable, and scalable solution for visualizing, analyzing, processing, and extracting valuable insights from data. The proposed solution is an optimized open-source unsupervised machine learning as a service (MLaaS) framework that caters to both experts and non-experts in machine learning. The framework aims to assist companies and organizations in solving problems related to clustering and anomaly detection, even without prior experience or internal infrastructure. With a focus on several clustering and anomaly detection techniques, the proposed framework automates data processing while allowing user intervention. The proposed framework includes default algorithms for clustering and outlier detection. In the clustering category, it features three algorithms: k-means, hierarchical clustering, and DBScan clustering. For outlier detection, it includes local outlier factor, K-nearest neighbors, and Gaussian mixture model. Furthermore, the proposed solution is expandable; it may include additional algorithms. It is versatile and capable of handling diverse datasets by generating separate rapid artificial intelligence models for each dataset and facilitating their comparison rapidly. The proposed framework provides a solution through a representational state transfer application programming interface, enabling seamless integration with various systems. Real-world testing of the proposed framework on customer segmentation and fraud detection data demonstrates that it is reliable, efficient, cost-effective, and time-saving. With the innovative MLaaS framework, companies may harness the full potential of business analysis.
Multi-objective application placement in fog computing using graph neural network-based reinforcement learning
Authors: Lera Isaac , Guerrero Carlos
Published: August 2024

We propose a framework designed to tackle a multi-objective optimization challenge related to the placement of applications in fog computing, employing a deep reinforcement learning (DRL) approach. Unlike other optimization techniques, such as integer linear programming or genetic algorithms, DRL models are applied in real time to solve similar problem situations after training. Our model comprises a learning process featuring a graph neural network and two actor-critics, providing a holistic perspective on the priorities concerning interconnected services that constitute an application. The learning model incorporates the relationships between services as a crucial factor in placement decisions: Services with higher dependencies take precedence in location selection. Our experimental investigation involves illustrative cases where we compare our results with baseline strategies and genetic algorithms. We observed a comparable Pareto set with negligible execution times, measured in the order of milliseconds, in contrast to the hours required by alternative approaches.