bluuebunny/crossref_metadata_2025 huggingface dataset:
Analyzing the impact of thermophoresis particle deposition and magnetohydrodynamic cross‐flow of Williamson hybrid nanofluids across a permeable deformable surface
Authors: Alharbi Latifah Falah , Khan Umair , Zaib Aurang , Ishak Anuar
Published: April 2024

The need to examine the thermal transport of hybrid nanofluids has arisen from the need for improved heat transfer to control the rising heat density of small‐scale and many other technical procedures. Hybrid nanofluids are made up of two different kinds of nanoparticles suspended in the primary fluid. This improves the ability of regular fluids to transport heat and makes them better heat exponents than nanofluids. In this study, we evaluate the cross‐flow and heat transport characteristics of Williamson hybrid nanofluid across an extendable surface. The impacts of thermal radiation, non‐uniform heat sources, and magnetic effects are provoked in this study. The set of partial different equations is obtained as a result of the theoretical formulation. The similarity variables are hired to get the ordinary differential equations. The bvp4c solver is availed to compute the numerical dual solutions for different physical parameters. The results indicate that the friction factor is enhanced due to the presence of magnetic, suction, and Williamson parameters. Whereas, the heat transfer rate decreases due to Williamson and magnetic parameters, whilst the opposite impact is seen due to suction. Moreover, the Williamson parameter reduces the Sherwood number, whereas the suction uplifts the Sherwood number.
Artifact Report: Trocq: Proof Transfer for Free, With or Without Univalence
Authors: Cohen Cyril , Crance Enzo , Mahboubi Assia
Published: April 2024

Trocq [5] is both the name of a calculus, describing a parametricity framework, and of a plugin [6] that provides tactics for performing representation changes in goals, as well as vernacular commands for specifying the expected translations.
The World Citizen School Model. Learning Philosophy and Learning System for Global, Socially Innovative and Value-Based Future Learning
Authors: Wihlenda Michael
Published: April 2024

This chapter presents the learning philosophy and learning system of the World Citizen School. The project started in 2013 at the Weltethos Institute of the University of Tübingen (The Global Ethic Project goes back to Küng (1997). ‘Weltethos’ is the German name for Global Ethic after which the Weltethos Institute and the Weltethos Foundation, which Küng co-founded, are named). Early in the development process, the founder and author of this paper was particularly influenced by the concept of the so-called “twenty-first Century Skills”, the 4C skill set of collaboration, critical thinking, communication, and creativity. Against this background, the ability to think and act procedurally (following the inquiry-based learning approach) receives special attention as an action-guiding process logic. This is practiced in particular with the didactic method which can be described as “dialogue around a common vision, mission, goals, and values, based on the concept of the ‘learning organization’”. With the latter, the primary focus of the learning philosophy moves to ethical competence development, for which the Weltethos Project and the Capability Approach serve as a reflective framework.
Using Real-World Problems and Project-Based Learning for Future Skill Development: An Approach to Connect Higher Education Students and Society Through User-Centered Design
Authors: Santana André Luiz Maciel , de Deus Lopes Roseli
Published: April 2024

Although technologies are increasingly present in people’s pockets through powerful smartphones, the development of solutions centered on the user does not always follow this movement, creating a gap, mainly in hardware solutions. At the same time, the role of the modern engineer is rethought as the demands of the industry and the way the population consumes technology are also changing. Furthermore, Future Skills, such as Design Thinking, Real-World Problems Solving, User-Centered Design, and Digital Literacy, are increasingly necessary to connect higher education students with complex social problems and their contexts. This paper analyses and compiles the results of an engineering program to operationalize real-world problem solutions in higher education by a User-Centered Design that aims to approach Future Skills in a Computer Engineering Course. It promotes discussion on a case study with 49 students from a Microcontroller and Internet of Things class, from a Brazilian university, in the year 2020 and during the pandemic period. It was possible to conclude that the program stimulates the development of modern skills and encourages students to design solutions based on people’s real needs. The results also indicate the enhancement of competencies such as Design Thinking, developing in teams, Digital Literacy, design with and for the user, solving complex problems by interacting with real-world issues, and communication skills.
Active Learning and Integrated Assessment: Minerva’s Approach to Teaching Future Skills
Authors: Gahl Megan K. , Ahuja Abha , Ribeiro Raquel H. , Averett Maia , Genone James
Published: April 2024

The Minerva Project works with global higher education partners to build transformational education programs based on Future Skills. Within our learning taxonomy, we identified practical knowledge as distinct Learning Outcomes (LOs). What differentiates Minerva programs from other Future Skills-focused programs is: 1) active learning pedagogy, 2) direct feedback and assessment of Future Skills, and 3) systematic reinforcement throughout students’ learning experience. The goal in our lessons is to practice skills and broad concepts, therefore we use flipped classrooms with active learning techniques. To guide students’ improvement of Future Skills in our programs, we directly assess LOs and provide feedback on specific LO applications in both synchronous (e.g., in-class polls) and asynchronous (e.g., assignments) aspects of each course. Learning Outcomes from the learning taxonomy may be introduced in one course but are assessable in any other course in the curriculum, ensuring that skills are reinforced throughout a student’s learning experience. In this chapter, we outline principles that underlie the development of learning taxonomies and curricular approaches in Minerva programs. We employ the same principles to collaborate with partners and build custom learning taxonomies and curricula; throughout this chapter we illustrate partner adaptations using examples from our collaborations.
Future-Skilling the Workforce: SkillsFuture Movement in Singapore
Authors: Gog Soon-Joo , Tan Edwin , Tan Kelsie
Published: April 2024

Workforce development is an ongoing undertaking in Singapore. This includes both the pre-employment education of students in the education system, and the continuing education and learning of incumbent workers. The workforce development system in Singapore is anchored on the tradition that stakeholders within the skills ecosystem must collaborate and coordinate closely to anticipate skills needs, disseminate insights, and measure skills progression and training outcomes. This is achieved through the regular charting of industry transformation from the business and economic perspectives, and the parallel review of human capital and skills demands for the future economy. A skilled and competitive workforce is the foundation of an inclusive society where everyone benefits from economic growth.
Commentary of the SKLM to the EFSA opinion on risk assessment of N-nitrosamines in food
Authors: Eisenbrand Gerhard , Buettner Andrea , Diel Patrick , Epe Bernd , Först Petra , Grune Tillman , Haller Dirk , Heinz Volker , Hellwig Michael , Humpf Hans-Ulrich , Jäger Henry , Kulling Sabine , Lampen Alfonso , Leist Marcel , Mally Angela , Marko Doris , Nöthlings Ute , Röhrdanz Elke , Spranger Joachim , Steinberg Pablo , Vieths Stefan , Wätjen Wim , Hengstler Jan G.
Published: April 2024

Dietary exposure to N-nitrosamines has recently been assessed by the European Food Safety Authority (EFSA) to result in margins of exposure that are conceived to indicate concern with respect to human health risk. However, evidence from more than half a century of international research shows that N-nitroso compounds (NOC) can also be formed endogenously. In this commentary of the Senate Commission on Food Safety (SKLM) of the German Research Foundation (DFG), the complex metabolic and physiological biokinetics network of nitrate, nitrite and reactive nitrogen species is discussed with emphasis on its influence on endogenous NOC formation. Pioneering approaches to monitor endogenous NOC have been based on steady-state levels of N-nitrosodimethylamine (NDMA) in human blood and on DNA adduct levels in blood cells. Further NOC have not been considered yet to a comparable extent, although their generation from endogenous or exogenous precursors is to be expected. The evidence available to date indicates that endogenous NDMA exposure could exceed dietary exposure by about 2–3 orders of magnitude. These findings require consolidation by refined toxicokinetics and DNA adduct monitoring data to achieve a credible and comprehensive human health risk assessment.
Smart hydropower management: utilizing machine learning and deep learning method to enhance dam’s energy generation efficiency
Authors: Sahin Muhammet Emin , Ozbay Karakus Mucella
Published: April 2024

Renewable energy sources and hydroelectric power generation in large parts of the electricity market are crucial as environmental pollution worsens . Utilizing meteorological data from the region, where the Hirfanlı Dam is located, this study employs machine learning (ML) and introduces a novel hybrid Genetic Grey Wolf Optimizer (GGW0)-based Convolutional Neural Network/Recurrent Neural Network (CNN/RNN) regression technique to predict hydroelectric power production (PP). In the first section of the study, various ML techniques SVR (Support Vector Regression), ELM (Extreme Learning Machine), RFR (Random Forest Regression), ANN (Artificial Neural Networks) and WKNNR (Weighted K-Nearest Neighbor) are presented with the Principal Component Analysis (PCA) method and the minimum–maximum method in the normalization of the features. A novel GGWO and CNN/RNN model)-Long Short-Term Memory (LSTM) regression technique is introduced in the second section. GGWO is used to select features, while the proposed CNN/RNN-LSTM model is employed for feature extraction and prediction of PP. The study demonstrates that the ELM algorithm in Method I outperforms other ML models, achieving a correlation coefficient (r) of 0.977 and the mean absolute error (MAE) of 0.4 with the best feature subset. Additionally, the proposed CNN/RNN hybrid model in Method II yields even better results, with r and MAE values of 0.9802 and 0.314, respectively. The research contributes to the field of renewable energy prediction, and the results can aid in efficient decision making for electricity generation and resource management.
Applying the ethics of AI: a systematic review of tools for developing and assessing AI-based systems
Authors: Ortega-Bolaños Ricardo , Bernal-Salcedo Joshua , Germán Ortiz Mariana , Galeano Sarmiento Julian , Ruz Gonzalo A. , Tabares-Soto Reinel
Published: April 2024

Artificial Intelligence (AI)-based systems and their increasingly common use have made it a ubiquitous technology; Machine Learning algorithms are present in streaming services, social networks, and in the health sector. However, implementing this emerging technology carries significant social and ethical risks and implications. Without ethical development of such systems, there is the potential for this technology to undermine people’s autonomy, privacy, and equity, even affecting human rights. Considering the approaches necessary for ethical development and effective governance of AI, such as ethical principles, guidelines, and technical tools, the question arises regarding the limitations of implementing these measures by the highly technical personnel involved in the process. In this context, we propose the creation of a typology that distinguishes the different stages of the AI life-cycle, the high-level ethical principles that should govern their implementation, and the tools with the potential to foster compliance with these principles, encompassing both technical and conceptual resources. In addition, this typology will include relevant information such as developmental level, related tasks, sectors, and language. Our research is based on a systematic review in which we identified 352 resources and tools. We expect this contribution to be valuable in promoting ethical AI development for developers and leaders who manage these initiatives. The complete typology and the comprehensive list of resources are available for consultation at https://ricardo-ob.github.io/tools4responsibleai.
“Be Not Conformed to this World”: MacIntyre’s Critique of Modernity and Amish Business Ethics
Authors: Jeong Sunny , Sinnicks Matthew , Burton Nicholas , Vu Mai Chi
Published: April 2024

This paper draws on MacIntyre’s ethical thought to illuminate a hitherto underexplored religious context for business ethics, that of the Amish. It draws on an empirical study of Amish settlements in Holmes County, Ohio, and aims to deepen our understanding of Amish business ethics by bringing it into contact with an ethical theory that has had a significant impact within business ethics, that of Alasdair MacIntyre. It also aims to extend MacIntyrean thought by drawing on his neglected critique of modernity in the context of business ethics. The Amish context allows us to appreciate the relationship between MacIntyre’s critique of modernity, his conception of practices and communities, and his distinctive approach to the virtues. It also helps us to better understand how the ethical life is possible within our emotivist culture.
Algor-ethics: charting the ethical path for AI in critical care
Authors: Montomoli Jonathan , Bitondo Maria Maddalena , Cascella Marco , Rezoagli Emanuele , Romeo Luca , Bellini Valentina , Semeraro Federico , Gamberini Emiliano , Frontoni Emanuele , Agnoletti Vanni , Altini Mattia , Benanti Paolo , Bignami Elena Giovanna
Published: April 2024

The integration of Clinical Decision Support Systems (CDSS) based on artificial intelligence (AI) in healthcare is groundbreaking evolution with enormous potential, but its development and ethical implementation, presents unique challenges, particularly in critical care, where physicians often deal with life-threating conditions requiring rapid actions and patients unable to participate in the decisional process. Moreover, development of AI-based CDSS is complex and should address different sources of bias, including data acquisition, health disparities, domain shifts during clinical use, and cognitive biases in decision-making. In this scenario algor-ethics is mandatory and emphasizes the integration of ‘Human-in-the-Loop’ and ‘Algorithmic Stewardship’ principles, and the benefits of advanced data engineering. The establishment of Clinical AI Departments (CAID) is necessary to lead AI innovation in healthcare, ensuring ethical integrity and human-centered development in this rapidly evolving field.
Robust and efficient airplane cockpit video coding leveraging temporal redundancy
Authors: Mitrica Iulia , Fiandrotti Attilio , Ruellan Christophe , Cagnazzo Marco
Published: April 2024

Airplane cockpit screens consist of virtual instruments where characters, numbers, and graphics are overlaid on a black or natural background. Recording the cockpit screen allows one to log vital plane data, as aircraft manufacturers do not offer direct access to raw data. However, traditional video codecs struggle at preserving character readability at the required low bit-rates. We showed in a previous work that large rate-distortion gains can be achieved if the characters are encoded as text rather than as pixels. We now leverage temporal redundancy to both achieve robust character recognition and improve encoding efficiency. A convolutional neural network is trained for character classification over synthetic samples augmented with occlusions to gain robustness against overlapping graphics. Further robustness to background occlusions is brought by a probabilistic framework that error-corrects the output of the convolutional neural network. Next, we propose a predictive text coding technique specifically tailored for text in cockpit videos that achieves competitive performance over commodity lossless methods. Experiments with real cockpit video footage show large rate-distortion gains for the proposed method with respect to three different video compression standards. Notably, the H.264/AVC codec retrofitted with our method outperforms H.265/HEVC-SCC and is competitive with the much more complex H.266/VVC while preserving text and graphics. The entire pipeline described in this work has been implemented at Safran Electronics as an embedded avionics system drawing just 2W of power thanks to a combination of software and FPGA implementation.
Efficient Bayesian CNN Model Compression using Bayes by Backprop and L1-Norm Regularization
Authors: Shaikh Ali Muhammad , Zhao Yun-bo , Kumar Aakash , Ali Munawar , Kang Yu
Published: April 2024

The swift advancement of convolutional neural networks (CNNs) in numerous real-world utilizations urges an elevation in computational cost along with the size of the model. In this context, many researchers steered their focus to eradicate these specific issues by compressing the original CNN models by pruning weights and filters, respectively. As filter pruning has an upper hand over the weight pruning method because filter pruning methods don’t impact sparse connectivity patterns. In this work, we suggested a Bayesian Convolutional Neural Network (BayesCNN) with Variational Inference, which prefaces probability distribution over weights. For the pruning task of Bayesian CNN, we utilized a combined version of L1-norm with capped L1-norm to help epitomize the amount of information that can be extracted through filter and control regularization. In this formation, we pruned unimportant filters directly without any test accuracy loss and achieved a slimmer model with comparative accuracy. The whole process of pruning is iterative and to validate the performance of our proposed work, we utilized several different CNN architectures on the standard classification dataset available. We have compared our results with non-Bayesian CNN models particularly, datasets such as CIFAR-10 on VGG-16, and pruned 75.8% parameters with float-point-operations (FLOPs) reduction of 51.3% without loss of accuracy and has achieved advancement in state-of-art.
Exploring intertemporal decision-making dynamics through functional data analysis: investigating variations in different discount function's dimensions
Authors: Martino Roberta , Porreca Annamaria , Ventre Viviana , Maturo Fabrizio
Published: April 2024

Intertemporal preferences are closely related to essential aspects of an individual’s emotional and cognitive domains. Discount functions are used to quantify these preferences, which can help us understand conditions such as addiction, depression, and Attention Deficit Hyperactivity Disorder (ADHD). However, traditional parametric models are limited when dealing with intertemporal preferences, mainly when behavioural biases are involved. This study exploits Functional Data Analysis (FDA) to investigate the properties of discount functions in intertemporal choices comparing people suffering from the Hikikomori pathology (a condition that involves social withdrawal) and normal people. Notably, the goal of this research is to look for statistically significant differences in the dynamics of intertemporal decision-making according to different gravity of the Hikikomori condition through the magnified FDA lens on different functional dimensions; the distinctive curves of discount functions categorised by Hikikomori scores prompted a more profound investigation via the so-called augmented functional analysis of variance. The original curves and their derivatives, and the discount rates and their first derivatives provide the different functional dimensions explored. This original approach of analyzing differences between subgroups according to decision-making behaviours is exciting from a methodological and practical perspective.
Causal effect of gut microbiota on the risk of prostatitis: a two-sample Mendelian randomization study
Authors: Liu Dalu , Mei Yangyang , Ji Nuo , Zhang Bo , Feng Xingliang
Published: April 2024

Background

Recent studies demonstrated that chronic prostatitis (CP) is closely related to the gut microbiota (GM). Nevertheless, the causal relationship between GM and CP has not been fully elucidated. Therefore, the two-sample Mendelian randomization (MR) analysis was employed to investigate this association.

Methods

The summary data of gut microbiota derived from a genome-wide association study (GWAS) involving 18,340 individuals in the MiBioGen study served as the exposure, and the corresponding summary statistics for CP risk, representing the outcome, were obtained from the FinnGen databases (R9). The causal effects between GM and CP were estimated using the inverse-variance weighted (IVW) method supplemented with MR-Egger, weighted median, weighted mode, and simple mode methods. Additionally, the false discovery rate (FDR) correction was performed to adjust results. The detection and quantification of heterogeneity and pleiotropy were accomplished through the MR pleiotropy residual sum and outlier method, Cochran’s Q statistics, and MR-Egger regression.

Results

The IVW estimates indicated that a total of 11 GM taxa were related to the risk of CP. Seven of them was correlated with an increased risk of CP, while the remained linked with a decreased risk of CP. However, only Methanobacteria (OR 0.86; 95% CI 0.74–0.99), Methanobacteriales (OR 0.86; 95% CI 0.74–0.99), NB1n (OR 1.16; 95% CI 1.16–1.34), Methanobacteriaceae (OR 0.86; 95% CI 0.74–0.99), Odoribactergenus Odoribacter (OR 1.43; 95% CI 1.05–1.94), and Sutterellagenus Sutterella (OR 1.33; 95% CI 1.01–1.76) still maintain significant association with CP after FDR correction. Consistent directional effects for all analyses were observed in the supplementary methods. Subsequently, sensitivity analyses indicated the absence of heterogeneity, directional pleiotropy, or outliers concerning the causal effect of specific gut microbiota on CP (p > 0.05).

Conclusion

Our study demonstrated a gut microbiota–prostate axis, offering crucial data supporting the promising use of the GM as a candidate target for CP prevention, diagnosis, and treatment. There is a necessity for randomized controlled trials to validate the protective effect of the linked GM against the risk of CP, and to further investigate the underlying mechanisms involved.
Differences in neuroinflammation in people who started antiretroviral treatment during primary versus chronic HIV infection: an 18kDa Translocator protein (TSPO) positron emission tomography (PET) study
Authors: Alagaratnam Jasmini , Thornhill John P. , Fan Zhen , Vera Jaime H. , Underwood Jonathan , Hall Rebecca , Searle Graham , Owen David , Edison Paul , Fidler Sarah , Winston Alan
Published: April 2024

Persistent inflammation is described in people with HIV (PWH) on antiretroviral treatment (ART). Early ART initiation is associated with reduced inflammation. We aimed to evaluate neuroinflammation, using translocator protein (TSPO) [11C]PBR28 PET neuroimaging in PWH who initiated ART during acute HIV (aPWH) versus chronic HIV infection (cPWH) versus a control population. This was a cross-sectional, observational study. All participants underwent [11C]PBR28 PET-CT neuroimaging. Using a two-tissue compartment model, total volume of distribution (VT) and distribution volume ratios (DVR) using cortical grey matter as a pseudo-reference region at 20 regions of interest (ROIs) were calculated. Differences in VT and DVR were compared between groups using the Kruskall-Wallis test. Seventeen neuro-asymptomatic male PWH on ART (9 aPWH, 8 cPWH) and 8 male control participants (CPs) were included. Median (interquartile range, IQR) age was 40 (30, 46), 44 (41, 47) and 21 (20, 25) years in aPWH, cPWH and CPs, respectively. Median (IQR) CD4 (cells/µL) and CD4:CD8 were 687 (652, 1014) and 1.37 (1.24, 1.42), and 700 (500, 720) and 0.67 (0.64, 0.82) in aPWH and cPWH, respectively. Overall, no significant difference in VT and DVR were observed between the three groups at any ROIs. cPWH demonstrated a trend towards higher mean VT compared with aPWH and CPs at most ROIs. No significant differences in neuroinflammation, using [11C]PBR28 binding as a proxy, were identified between cPWH, aPWH and CPs. A trend towards lower absolute [11C]PBR28 binding was seen amongst aPWH and CPs, suggesting early ART may mitigate neuroinflammation.
Challenges and opportunities for increasing the use of low-risk plant protection products in sustainable production. A review
Authors: Lankinen Åsa , Witzell Johanna , Aleklett Kristin , Furenhed Sara , Karlsson Green Kristina , Latz Meike , Liljeroth Erland , Larsson Rebecca , Löfkvist Klara , Meijer Johan , Menkis Audrius , Ninkovic Velemir , Olson Åke , Grenville-Briggs Laura
Published: April 2024

Plant production systems worldwide are struggling to meet the diverse and increasing needs of humankind while also facing challenges such as climate change and biodiversity loss. This, combined with the desirable transition from the use of conventional pesticides to more sustainable plant protection solutions, has led to an urgent, and increasing, need for low-risk plant protection products (PPPs) to be developed, applied, and integrated into management practices across all types of plant production systems. Despite a high demand from end users and consumers together with joint political goals at the EU level to replace conventional pesticides, the number of low-risk PPPs on the European market remains low, in comparison to synthetic agrochemicals. In this review, we summarize knowledge about the policy, technical, and administrative issues hampering the process of bringing new low-risk PPPs to the European market. We present an overview of the challenges in using the low-risk PPPs that are currently available within the EU agricultural, horticultural, and forestry sectors. We describe the variation in modes of action and the limitations associated with different application techniques and give concrete examples of problems and solutions from Swedish plant production sectors, in contrast to global perspectives as demonstrated by examples from African agriculture. Finally, we conclude that trans-sectoral, multi-actor approaches are required and provide suggestions on how to address the remaining knowledge gaps related to efficiency, application, and economics of low-risk PPP use in Integrated Pest Management (IPM) solutions for plant protection to improve future food security in Europe.
The residual stress state in meso- and micro-milling processes with a ball-end mill in Ti–6Al–4V titanium alloy
Authors: García-Barbosa Jorge Andrés , Córdoba-Nieto Ernesto
Published: April 2024

An experimental investigation and comparative analysis of the residual stress state between micro- and meso-milling processes with a ball-end mill on the Ti–6Al–4V titanium alloy were carried out. A methodology to study the characteristic kinematics of a five‑axis machining process in a three-axis vertical machining centre was proposed. The study considers the chip formation process according to the lead and tilt angles of the tool axis concerning the normal vector of the surface. When using the up‑milling cutting strategy, the defect of smeared/adhered material to the surface occurs in both the micro- and meso-milling levels, associated with the build-up edge and build-up layer phenomenon. The residual stress tensor of the surface was obtained through the X-ray diffraction technique. The down-milling cutting strategy produced the best surface finish and higher compressive residual stresses. The experiments showed higher compressive residual stresses in the feed direction than in the cross-feed direction. The micro-milling process produced higher compressive residual stresses than those observed in the meso-milling process.
Solar-Induced Chlorophyll Fluorescence (SIF): Towards a Better Understanding of Vegetation Dynamics and Carbon Uptake in Arctic-Boreal Ecosystems
Authors: Cheng Rui
Published: April 2024

Purpose of Review

Terrestrial ecosystems in the Arctic-Boreal region play a crucial role in the global carbon cycle as a carbon sink. However, rapid warming in this region induces uncertainties regarding the future net carbon exchange between land and the atmosphere, highlighting the need for better monitoring of the carbon fluxes. Solar-Induced chlorophyll Fluorescence (SIF), a good proxy for vegetation CO$$^{2}$$

2

uptake, has been broadly utilized to assess vegetation dynamics and carbon uptake at the global scale. However, the full potential and limitations of SIF in the Arctic-Boreal region have not been explored. Therefore, this review aims to provide a comprehensive summary of the latest insights into Arctic-Boreal carbon uptake through SIF analyses, underscoring the advances and challenges of SIF in solving emergent unknowns in this region. Additionally, this review proposes applications of SIF across scales in support of other observational and modeling platforms for better understanding Arctic-Boreal vegetation dynamics and carbon fluxes.

Recent Findings

Cross-scale SIF measurements complement each other, offering valuable perspectives on Arctic-Boreal ecosystems, such as vegetation phenology, carbon uptake, carbon-water coupling, and ecosystem responses to disturbances. By incorporating SIF into land surface modeling, the understanding of Arctic-Boreal changes and their climate drivers can be mechanistically enhanced, providing critical insights into the changes of Arctic-Boreal ecosystems under global warming.

While SIF measurements are more abundant and with finer spatiotemporal resolutions, it is important to note that the coverage of these measurements is still limited and uneven in the Arctic-Boreal region. To address this limitation and further advance our understanding of the Arctic-Boreal carbon cycle, this review advocates for fostering a SIF network providing long-term and continuous measurements across spatial scales. Simultaneously measuring SIF and other environmental variables in the context of a multi-modal sensing system can help us comprehensively characterize Arctic-Boreal ecosystems with spatial details in land surface models, ultimately contributing to more robust climate projections.
Forest Supply Chains During Digitalization: Current Implementations and Prospects in Near Future
Authors: Palander Teijo , Tokola Timo , Borz Stelian Alexandru , Rauch Peter
Published: April 2024

Purpose of Review

The forest industry has deployed systems of information and communication technology to improve the productivity of forest supply chains. In this sense, the digitalization of data flows significantly impacts wood supply chains, from forest to mills, which must react flexibly to market fluctuations of forest products. The goal of this study was to conduct a literature review on data flow implementations in management systems of wood supply chains and to evaluate their applicability in supply chains to analyze the opportunities for improving them in practice.

Recent Findings

We utilized the ScienceDirect database, Scopus, and Web of Science in order to document data flows in systems and actual applications. Due to ongoing outsourcing, the wood procurement chains and the wood supply chains were identified in the forest industry. In addition to industrial wood data, several different data collection technologies can be implemented in wood supply chains to digitalize the forest data depending on the specific needs of organizations. In this regard, the digitalization of big data causes significant changes in available data elements of practical operations that are integrated and standardized in the optimization and simulation systems.

A modeling guide is suggested for accurate dynamic solution of problems of forest logistics. Then, the opportunities in digitalization for connecting different data flows, technologies, and stakeholders are discussed thoroughly. We conclude that data-driven dynamic optimization and locally implemented digitalization contribute to wood supply modeling in the ongoing establishment of standards for cloud-based platforms because modeling time-related and sequential measures will ensure successful forest logistics through planning and monitoring wood supply chains.
Low mutual coupling miniaturized dual-band quad-port MIMO antenna array using decoupling structure for 5G smartphones
Authors: Elabd Rania Hamdy , Al-Gburi Ahmed Jamal Abdullah
Published: April 2024

Maintaining the compactness of 5G smartphones while accommodating millimeter-wave (mm-wave) bands presents a significant challenge due to the substantial difference in frequency. To tackle this issue, we introduce a miniaturized quad-port dual-band multiple-input, multiple-output (MIMO) antenna with low mutual coupling (MC) and a considerable frequency difference. This quad-port MIMO antenna, built on a Rogers TMM4 substrate, measures 17.76 × 17.76 mm2 and boasts a dielectric constant of 4.5. It incorporates four planar patch antennas, positioned at the corners in perpendicular orientations. For dual-band operation at 28/38 GHz, each antenna element features a rectangular patch with four rectangular slots, complemented by a full ground plane. The spacing between these elements is 0.5 λo, and we've included a decoupling structure (DS) to minimize mutual coupling (MC) among the MIMO antenna elements with minimal complexity and cost. Simulation and measurement results reveal a significant reduction in mutual coupling between the array elements, ranging from − 25 to − 60 dB. As a result, we’ve developed the envelope correlation coefficient (ECC) and made advancements in the total active reflection coefficient (TARC), mean effective gain (MEG), and diversity gain (DG). The measured gains for this design are approximately 8.9 dBi at both 28 GHz and 38 GHz, with a radiation efficiency of nearly 93%. Furthermore, specific absorption rate (SAR) analysis confirms the MIMO antenna's suitability for smartphone handsets operating within the target frequency band.
Real-time IoT architecture for water management in smart cities
Authors: Iancu George , Ciolofan Sorin N. , Drăgoicea Monica
Published: April 2024

This paper presents a digital system that aims to analyze real-time data obtained from sensors installed in a city's water distribution infrastructure. The system’s primary objective is to monitor water quality and generate alerts when necessary. The following water quality metrics are used: Flow, pH, Turbidity, Free Chlorine, Nitrate, and Fluoride. The data gathered from sensors is initially processed by a distributed system, which generates multiple visualizations that synthesize large amounts of information. These visualizations facilitate real-time monitoring of the sensor's status. Additionally, citizens can receive updates on any possible issues in the water distribution network through WhatsApp messages. By addressing the limitations of traditional water quality monitoring methods, this system contributes to a noteworthy enhancement in public water supply services. Consequently, it improves the overall quality of life for the citizens.
A network of transcriptomic signatures identifies novel comorbidity mechanisms between schizophrenia and somatic disorders
Authors: Zhang Youcheng , Bharadhwaj Vinay S. , Kodamullil Alpha T. , Herrmann Carl
Published: April 2024

The clinical burden of mental illness, in particular schizophrenia and bipolar disorder, are driven by frequent chronic courses and increased mortality, as well as the risk for comorbid conditions such as cardiovascular disease and type 2 diabetes. Evidence suggests an overlap of molecular pathways between psychotic disorders and somatic comorbidities. In this study, we developed a computational framework to perform comorbidity modeling via an improved integrative unsupervised machine learning approach based on multi-rank non-negative matrix factorization (mrNMF). Using this procedure, we extracted molecular signatures potentially explaining shared comorbidity mechanisms. For this, 27 case–control microarray transcriptomic datasets across multiple tissues were collected, covering three main categories of conditions including psychotic disorders, cardiovascular diseases and type II diabetes. We addressed the limitation of normal NMF for parameter selection by introducing multi-rank ensembled NMF to identify signatures under various hierarchical levels simultaneously. Analysis of comorbidity signature pairs was performed to identify several potential mechanisms involving activation of inflammatory response auxiliarily interconnecting angiogenesis, oxidative response and GABAergic neuro-action. Overall, we proposed a general cross-cohorts computing workflow for investigating the comorbid pattern across multiple symptoms, applied it to the real-data comorbidity study on schizophrenia, and further discussed the potential for future application of the approach.
Effective strategies and interventions for engaging at-risk youth: a rapid systematic review of the literature
Authors: Toews Alexa J. , Letourneau Nicole , Pohl Carrie , Ruse Kim
Published: April 2024

Purpose

Family violence, including physical abuse, sexual abuse, and exposure to intimate partner violence, has been linked to mental health problems and increased substance abuse in youth. This rapid systematic review evaluated the most recent literature on effective strategies and/or interventions to engage youth at-risk due to family violence, mental health problems, and/or substance abuse.

Methods

A rapid systematic review of the literature on engaging youth identified as at-risk for mental health difficulties, substance abuse, or family violence in interventions was undertaken. Searches were run in PsycINFO, CINAHL, SocINDEX, Family & Society Studies Worldwide and Social Work Abstracts. To be included, studies had to be quantitative or qualitative, examine youth-targeted strategies aimed at increasing engagement with interventions and have a target population aged between 12 to 18 years old. Studies published prior to 2014 were excluded.

Results

A total of 13 studies were retrieved that included 2,527 high-risk youth. Strategies were identified and categorized based on the following themes: (1) Technology-based, (2) Experiential therapy-based, (3) Counselling-based, (4) Program-based and (5) Other engagement strategies. Technology-based, experiential therapy-based and program-based strategies showed high levels of engagement in at-risk youth. Counselling-based strategies demonstrated variable outcomes between studies.

Conclusions

Many of the engagement strategies retrieved in this evidence assessment were reported to be successful, suggesting that there is no single best approach to engaging at-risk youth. A variety of strategies can be combined and tailored to fit the individual needs of youth and the resources available to the intervention program at that time.
CPET Made Simple
Authors: Lawson Tom , Anderson Helen
Published: April 2024

Cardiopulmonary exercise testing (CPET) is an increasingly important investigation for anaesthesiologists, surgeons, cardiologists, pulmonologists, physiologists, and peri-operative physicians. For trainees, non-experts, or simply those seeking an introduction to interpreting CPETs, this essential book builds layers of knowledge that can be applied in case studies and in future practice. It will help you learn and understand: • What is CPET and what are the principles of exercise physiology • How to conduct a CPET • How to interpret a CPET nine-panel plot • How to assess exercise capacity and limitation. This practical and clinical guide is both easy to read and easy to understand and features diagrams illustrating key principles. It outlines the basic physiological principles and takes a step-by-step approach to conducting and interpreting a cardiopulmonary exercise test, together with case studies to consolidate learning. Chapters cover the indications and contraindications of CPET, test safety and complications, spirometry, writing a CPET report, and shared decision-making.
Ecology in Action
Authors: Singer Fred D.
Published: April 2024

Providing students with a solid understanding of core ecological concepts while explaining how ecologists raise and answer real-world questions, this second edition weaves together classic and cutting-edge case studies to bring the subject to life. It is fully updated throughout, including two chapters devoted to climate change ecology, along with extensive coverage of disease ecology, and has been designed specifically to equip students with the tools to analyze and interpret real data. Each chapter emphasizes the linkage between observations, ideas, questions, hypotheses, predictions, results, and conclusions. Additional summary sections describe the development and evolution of research programs in each of ecology's core areas, providing students with essential context. Integrated discussion questions, along with end-of-chapter questions, encourage active learning. These are supported by online resources including tutorials that teach students to use the R programming language for statistical analyses of data presented in the text.
Canadian Practice and Recommendations on Functional MRI to Lateralize Language in Epilepsy
Authors: Falby Madeleine R. , Brien Donald C. , Boissé Lomax Lysa , Shukla Garima , Winston Gavin P.
Published: April 2024

Background/Objective:

Identifying a patient’s dominant language hemisphere is an important evaluation performed prior to epilepsy surgery and is commonly assessed using functional magnetic resonance imaging (fMRI). However, the lack of standardization and resultant heterogeneity of fMRI paradigms used in clinical practice limits the ability of cross-center comparisons to be made regarding language laterality results.

Methods:

Through surveying Canadian Epilepsy Centres in combination with reviewing supporting literature, current fMRI language lateralization practices for the clinical evaluation of patients with epilepsy were assessed. To encourage standardization of this practice, we outlined a two-part paradigm series that demonstrates widespread acceptance, reliability and accessibility in lateralizing various aspects of language functioning in individuals with average or near-average IQ and normal literacy skills.

Results:

The collected data confirm a lack of standardization in fMRI laterality assessments leading to clinical heterogeneity in stimulation and control tasks, paradigm design and timing, laterality index calculations, thresholding values and analysis software and technique. We suggest a Sentence Completion (SC) and Word Generation (WG) paradigm series as it was most commonly employed across Canada, demonstrated reliability in lateralizing both receptive and expressive language areas in supporting literature, and could be readily intelligible to an inclusive population.

Conclusion:

Through providing recommendations for a two-part paradigm series, we hope to contribute to the standardization of this practice across Canada to reduce clinical heterogeneity, encourage communicability between institutions, and enhance methodologies for the surgical treatment of epilepsy for the benefit of all individuals living with epilepsy in Canada.
Density of mode-locking property for quasi-periodically forced Arnold circle maps
Authors: WANG JIAN , ZHANG ZHIYUAN
Published: April 2024

We show that the mode-locking region of the family of quasi-periodically forced Arnold circle maps with a topologically generic forcing function is dense. This gives a rigorous verification of certain numerical observations in [M. Ding, C. Grebogi and E. Ott. Evolution of attractors in quasiperiodically forced systems: from quasiperiodic to strange nonchaotic to chaotic.Phys. Rev. A 39(5) (1989), 2593–2598] for such forcing functions. More generally, under some general conditions on the base map, we show the density of the mode-locking property among dynamically forced maps (defined in [Z. Zhang. On topological genericity of the mode-locking phenomenon.Math. Ann. 376(2020), 707–72]) equipped with a topology that is much stronger than the$C^0$topology, compatible with smooth fiber maps. For quasi-periodic base maps, our result generalizes the main results in [A. Avila, J. Bochi and D. Damanik. Cantor spectrum for Schrödinger operators with potentials arising from generalized skew-shifts.Duke Math. J.146(2009), 253–280], [J. Wang, Q. Zhou and T. Jäger. Genericity of mode-locking for quasiperiodically forced circle maps.Adv. Math.348(2019), 353–377] and Zhang (2020).
RFI detection with spiking neural networks
Authors: Pritchard N.J. , Wicenec A. , Bennamoun M. , Dodson R.
Published: April 2024

Detecting and mitigating radio frequency interference (RFI) is critical for enabling and maximising the scientific output of radio telescopes. The emergence of machine learning (ML) methods capable of handling large datasets has led to their application in radio astronomy, particularly in RFI detection. Spiking neural networks (SNNs), inspired by biological systems, are well suited for processing spatio-temporal data. This study introduces the first exploratory application of SNNs to an astronomical data processing task, specifically RFI detection. We adapt the nearest latent neighbours (NLNs) algorithm and auto-encoder architecture proposed by previous authors to SNN execution by direct ANN2SNN conversion, enabling simplified downstream RFI detection by sampling the naturally varying latent space from the internal spiking neurons. Our subsequent evaluation aims to determine whether SNNs are viable for future RFI detection schemes. We evaluate detection performance with the simulated HERA telescope and hand-labelled LOFAR observation dataset the original authors provided. We additionally evaluate detection performance with a new MeerKAT-inspired simulation dataset that provides a technical challenge for machine-learnt RFI detection methods. This dataset focuses on satellite-based RFI, an increasingly important class of RFI and is an additional contribution. Our SNN approach remains competitive with the original NLN algorithm and AOFlagger in AUROC, AUPRC, and F1-scores for the HERA dataset but exhibits difficulty in the LOFAR and Tabascal datasets. However, our method maintains this accuracy while completely removing the compute and memory-intense latent sampling step found in NLN. This work demonstrates the viability of SNNs as a promising avenue for ML-based RFI detection in radio telescopes by establishing a minimal performance baseline on traditional and nascent satellite-based RFI sources and is the first work to our knowledge to apply SNNs in astronomy.
Campaign communication and legislative leadership
Authors: Müller Stefan , Fujimura Naofumi
Published: April 2024

Do policy priorities that candidates emphasize during election campaigns predict their subsequent legislative activities? We study this question by assembling novel data on legislative leadership posts held by Japanese politicians and using a fine-tuned transformer-based machine learning model to classify policy areas in over 46,900 statements from 1270 candidate manifestos across five elections. We find that a higher emphasis on a policy issue increases the probability of securing a legislative post in the same area. This relationship remains consistent across multiple elections and persists even when accounting for candidates' previous legislative leadership roles. We also discover greater congruence in distributive policy areas. Our findings indicate that campaigns provide meaningful signals of policy priorities.