bluuebunny/crossref_metadata_2025 huggingface dataset:
An interpretable Bayesian deep learning-based approach for sustainable clean energy
Authors: Ezzat Dalia , Ahmed Eman , Soliman Mona , Hassanien Aboul Ella
Published: June 2024

Sustainable Development Goal 7 is dedicated to ensuring access to clean and affordable energy that can be utilized in various applications. Solar panels (SP) are utilized to convert sunlight into electricity, acting as a renewable energy source. It is important to keep SP clean to obtain the required performance, as the accumulation of snow and dust on SP greatly affects the amount of electricity generated. On the other hand, excessive cleaning has some detrimental effects on the SP, therefore cleaning should only be done when necessary and not on a regular basis. Consequently, it is critical to determine whether the cleaning procedure is necessary by automatically detecting the presence of dust or snow on the panels while avoiding inaccurate predictions. Research efforts have been made to detect the presence of dust and snow on SP, but most of the proposed methods do not guarantee accurate detection results. This paper proposes an accurate, reliable, and interpretable approach called Solar-OBNet. The proposed Solar-OBNet can detect dusty SP and snow-covered SP very efficiently and be used in conjunction with the methods used to clean SP. The proposed Solar-OBNet is based on a Bayesian convolutional neural network, which enables it to express the amount of confidence in its predictions. Two measurements are used to estimate the uncertainty in the outcomes of the proposed Solar-OBNet, namely predictive entropy and standard deviation. The proposed Solar-OBNet can express confidence in the correct predictions by showing low values for predictive entropy and standard deviation. The proposed Solar-OBNet can also give an uncertainty warning in the case of erroneous predictions by showing high values of predictive entropy and standard deviation. The proposed Solar-OBNet’s efficacy was verified by interpreting its results using a method called Weighted Gradient-Directed Class Activation Mapping (Grad-CAM). The proposed Solar-OBNet has achieved a balanced accuracy of 94.07% and an average specificity 95.83%, outperforming other comparable methods.
Cultural heritage digital twin: modeling and representing the visual narrative in Leonardo Da Vinci’s Mona Lisa
Authors: Amelio Alessia , Zarri Gian Piero
Published: June 2024

In this paper, Artificial Intelligence/Knowledge Representation methods are used for the digital modeling of cultural heritage elements. Accordingly, the new concept of digital cultural heritage twin is presented as composed of a physical component and an immaterial component of the cultural entity. The former concerns the physical aspects, i.e. style, name of the artist, execution time, dimension, etc. The latter represents the emotional and intangible aspects transmitted by the entity, i.e. emotions, thoughts, opinions. In order to digitally model the physical and immaterial components of the twin, the Narrative Knowledge Representation Language has been formally introduced and described. It is particularly suitable for representing the immaterial aspects of the cultural entity, as it is capable of modeling in a simple but rigorous and efficient way complex situations and events, behaviours, attitudes, etc. As an experiment, NKRL has been adopted for representing some of the most relevant intangible items of the visual narrative underlying the hidden painting that lies beneath the Mona Lisa (La Gioconda) image painted by Leonardo Da Vinci on the same poplar panel. Real-time application of the resulting knowledge base opens up novel possibilities for the development of virtual objects, chatbots and expert systems, as well as the definition of semantic search platforms related to cultural heritage.
Editorial to the special issue on JCDL 2022
Authors: Mayr Philipp , Hinze Annika , Schaer Philipp
Published: June 2024

This special issue features the selected works of authors who have presented papers at the 2022 iteration of the Joint Conference on Digital Libraries (JCDL) in Cologne, Germany. The motto of the conference was “Bridging Worlds” and was run as a fully hybrid event. Ten papers covering all aspects of Digital Libraries, namely Natural Language Processing, Information Retrieval, User Behavior, Scholarly Communication, Classification, Information Extraction are included in this issue.
Knee-Loading Predictions with Neural Networks Improve Finite Element Modeling Classifications of Knee Osteoarthritis: Data from the Osteoarthritis Initiative
Authors: Paz Alexander , Lavikainen Jere , Turunen Mikael J. , García José J. , Korhonen Rami K. , Mononen Mika E.
Published: June 2024

Physics-based modeling methods have the potential to investigate the mechanical factors associated with knee osteoarthritis (OA) and predict the future radiographic condition of the joint. However, it remains unclear what level of detail is optimal in these methods to achieve accurate prediction results in cohort studies. In this work, we extended a template-based finite element (FE) method to include the lateral and medial compartments of the tibiofemoral joint and simulated the mechanical responses of 97 knees under three conditions of gait loading. Furthermore, the effects of variations in cartilage thickness and failure equation on predicted cartilage degeneration were investigated. Our results showed that using neural network-based estimations of peak knee loading provided classification performances of 0.70 (AUC, p < 0.05) in distinguishing between knees that developed severe OA or mild OA and knees that did not develop OA eight years after a healthy radiographic baseline. However, FE models incorporating subject-specific femoral and tibial cartilage thickness did not improve this classification performance, suggesting there exists an optimal point between personalized loading and geometry for discrimination purposes. In summary, we proposed a modeling framework that streamlines the rapid generation of individualized knee models achieving promising classification performance while avoiding motion capture and cartilage image segmentation.
Reinforcement learning based autonomous multi-rotor landing on moving platforms
Authors: Goldschmid Pascal , Ahmad Aamir
Published: June 2024

Multi-rotor UAVs suffer from a restricted range and flight duration due to limited battery capacity. Autonomous landing on a 2D moving platform offers the possibility to replenish batteries and offload data, thus increasing the utility of the vehicle. Classical approaches rely on accurate, complex and difficult-to-derive models of the vehicle and the environment. Reinforcement learning (RL) provides an attractive alternative due to its ability to learn a suitable control policy exclusively from data during a training procedure. However, current methods require several hours to train, have limited success rates and depend on hyperparameters that need to be tuned by trial-and-error. We address all these issues in this work. First, we decompose the landing procedure into a sequence of simpler, but similar learning tasks. This is enabled by applying two instances of the same RL based controller trained for 1D motion for controlling the multi-rotor’s movement in both the longitudinal and the lateral directions. Second, we introduce a powerful state space discretization technique that is based on i) kinematic modeling of the moving platform to derive information about the state space topology and ii) structuring the training as a sequential curriculum using transfer learning. Third, we leverage the kinematics model of the moving platform to also derive interpretable hyperparameters for the training process that ensure sufficient maneuverability of the multi-rotor vehicle. The training is performed using the tabular RL method Double Q-Learning. Through extensive simulations we show that the presented method significantly increases the rate of successful landings, while requiring less training time compared to other deep RL approaches. Furthermore, for two comparison scenarios it achieves comparable performance than a cascaded PI controller. Finally, we deploy and demonstrate our algorithm on real hardware. For all evaluation scenarios we provide statistics on the agent’s performance. Source code is openly available at https://github.com/robot-perception-group/rl_multi_rotor_landing.
How plant and insect host characteristics affect pepper weevil Anthonomus eugenii parasitism efficacy by the pteromalid Jaliscoa hunteri
Authors: Leo Serena , Labbé Roselyne , Scott-Dupree Cynthia
Published: June 2024

The pepper weevil Anthonomus eugenii (Cano) (Coleoptera: Curculionidae) is an economically important pest of both field and greenhouse pepper crops, Capsicum annuum Linnaeus (Solanaceae) in North America. Among its natural enemies is the parasitoid wasp Jaliscoa (= Catolaccus) hunteri (Crawford) (Hymenoptera: Pteromalidae), whose potential control of A. eugenii appears promising but is currently unclear. We conducted controlled environment trials to quantify A. eugenii offspring emergence following exposure to J. hunteri, examining how parasitoid exposure period, weevil host life stage, pepper cultivar, fruit size and abscission status affect weevil-natural enemy dynamics. In pepper cultivar comparison trials, we identified a significant reduction in weevil offspring emergence from three different cultivars of ornamental peppers infested by A. eugenii, but no difference among them. In trials comparing wasp exposure period and weevil larval instar, pepper plants of one cultivar (Blaze) were exposed to A. eugenii adults, and subsequently to J. hunteri wasps for either three or seven days, when offspring weevils were either at the L1 or L3 instar. Offspring emergence was significantly reduced up to 62% when L3 but not L1 weevils were exposed to wasps for 7 days. Following this longer exposure period, significantly greater weevil suppression occurred in small (71.7%) versus large fruit (62.5%), with an additional 15.1% weevil suppression resulting from attached relative to abscised fruit. These results highlight the potential for J. hunteri as a biological control agent of A. eugenii on pepper crops, with particular value in commercial greenhouse production where wasp retention rates can be high.
Climate changes and food-borne pathogens: the impact on human health and mitigation strategy
Authors: Awad Dina A. , Masoud Hazem A. , Hamad Ahmed
Published: June 2024

Climate change has emerged as a major pressing global issue with far-reaching implications for human health, such as the emerging and spread of food-borne pathogens. Food-borne pathogens are microorganisms that can cause illness in humans, from mild discomfort to life-threatening diseases, through the consumption of contaminated food or water. The impact of climate change on food-borne pathogens is multifaceted and includes changes in the environment, agriculture, and human behavior. This review article examines the effect of climate change on food-borne pathogens, explores the connection between climate change and food-borne illness, records the current evidence on the effects of climate change on food-borne pathogens and potential consequences for human health, highlights knowledge gaps and areas for further research, and summarizes the strategies for mitigation and adaptation. Understanding the delicate relationship between climate change and food-borne infections makes it possible to maintain food systems and defend the health and well-being of populations worldwide.
Resistance to powdery mildew in Irish oat heritage lines
Authors: Reilly Aisling , Okoń Sylwia , Cieplak Magdalena , Finnan John , Kildea Steven , Feechan Angela
Published: June 2024

Cereal breeding in Ireland took place in the early twentieth Century to establish native Irish oat cultivars with higher grain yields. However, the production of Irish cultivars declined rapidly from 1973 as the import of international, commercially bred oat cultivars increased which were tested for suitability to Irish climatic conditions. A collection of these heritage oat accessions has been maintained by the Department of Agriculture, Food and the Marine (DAFM). In this study, we investigated a collection of heritage oats for possible resistance to powdery mildew caused by the fungal pathogen Blumeria graminis f. sp. avenae (Bga). We identified Bga resistance in two historical oat accessions (Tyrone 1994 and Sandy). This resistance was mediated by high levels of papillae formation and/or encasements to an Irish single spore isolate. However, intermediate resistance was observed when using a range of Bga isolates.
Carbon financial trading risk based on multidimensional analysis of data flow from the perspective of low-carbon economy
Authors: Su Qing , Chen Lifeng
Published: June 2024

Currently, carbon trading provides financial incentives for buying and selling savings to generate a certain quantity of energy gases with a market-based mechanism. Trade in renewable energy and breakthroughs in energy efficiency can be enhanced by managing either the obstacles to the business or economic risks associated with trade facilitation, making is challenging to implement a low-carbon economy in developing financial systems. Reducing greenhouse gas emissions is likely perceived as in contradiction with the combat for poverty in developing nations, and rising real incomes are often connected with better-increased energy production. To maintain carbon option trading, the analysis begins to predict future carbon option prices using the generalized auto-regressive conditional heteroskedasticity model and fractional brownian motion. Predicting carbon option prices using fractional brownian motion makes sense, given their fractal nature. Data envelopment analysis to better understand the countermeasures for utilizing a low-carbon economy need to further analytical and economic improvement of the marketing function and development. Hence, this research GARCH-DEA has been designed to strengthen carbon financial trading using multidimensional data flow analysis from the perspective of the varying nature of returns and the implications for a low-carbon economy; distribution features are enormous theoretical and practical relevance for the monitoring and management of financial risks. Reducing greenhouse gas emissions, resulting in carbon dioxide is vital in the battle against climate change. Products and services that require carbon-intensive inputs, like electricity and transportation, can be more expensive due to the rising cost of burning fossil fuels.
A perspective on the significance of reporting climate change adaptation information to the united nations framework convention on climate change
Authors: Mantlana K. B. , Ndiitwani M. , Ndhleve S.
Published: June 2024

The United Nations Framework Convention on Climate Change (UNFCCC) is the centre of the global policy response to climate change. The Paris Agreement, a legally binding treaty under the UNFCCC, has located climate change adaptation as a critical component of the global response to climate change. The Paris Agreement also establishes an enhanced transparency framework to track progress towards Parties climate change commitments. However, the UNFCCC has consistently maintained a marked difference in provisions for reporting climate change adaptation and climate change mitigation. Consequently, reporting on climate change adaptation lags far behind in detail when compared to that for reporting on climate change mitigation. Using literature review as the main method of analysis, this paper aims to understand the underlying factors that have resulted in the lack of specificity of reporting provisions of the UNFCCC for climate change adaptation and the consequences of non-mandatory provisions for reporting on climate change adaptation on national and global adaptation practice. The paper then highlights the benefits of regular reporting of climate change adaptation to the UNFCCC. It makes important contribution to the growing literature on global environmental governance, especially on national reporting of adaptation information, an under-studied and a poorly understood field.
Adynamism in Physics: The Block Universe vs Barbour’s Relational Strategy
Authors: Margoni Emilia
Published: June 2024

The block universe is generally considered as the metaphysical position that best accommodates the outcomes of relativistic physics. Its most consistent formulation postulates a static universe where change is not admitted. However, some of its advocates try to reconcile its basic adynamical commitments as to the nature of physical reality with certain aspects of dynamism that arise, for instance, within human experience. In this article, I first examine how some block viewers try to reconcile dynamism and adynamism. I then go on to discuss the problems that emerge while trying to make room for dynamism within the block. To this end, I clarify the meaning of adynamism and explains what it takes to eradicate dynamism through the implementation of the so-called “Langrangian schema”. Based on this analysis, I juxtapose two resolute attempts at the formulation of a thoroughly adynamical project. The first, named relational block world, aims to make the block view totally static. The second, Barbour’s early works on the implementation of a neo-Machian relational strategy, makes the point that a complete adynamism cannot be achieved within relativity and its metaphysical counterpart. The article in conclusion argues that, by dismissing four-dimensionalism and embracing three-dimensionalism, Barbour proves better at promoting an utterly static physics.
Review of OpenFOAM applications in the computational wind engineering: from wind environment to wind structural engineering
Authors: Ricci Alessio
Published: June 2024

The use of computational fluid dynamics (CFD) in the wind engineering (WE) is generally defined as computational wind engineering (CWE). Since its foundation in 2004, the use of OpenFOAM in CWE has been increasing progressively and covers nowadays a wide range of topics, from wind environment to wind structural engineering. This paper was drafted in response to the invitation from the organizers of the 18th OpenFOAM workshop held in Genoa (Italy) on 11–14 July 2023, when a technical session on Civil Engineering and Wind Engineering was organized. In this paper the author briefly reviews the history of WE and surveys the evolution, methods, and future challenges of OpenFOAM in the CWE. Topics are here regrouped into three main research areas and discussed from a physical, engineering and purely computational perspective. The study does not cover the Wind Energy and related topics, since this can be considered nowadays as a stand-alone subfield of the WE. This review confirms that OpenFOAM is a versatile tool widely used for WE applications that often require new models to be developed ad hoc by CFD users. It can be coupled easily with numerical weather prediction models for mesoscale-microscale wind and thermal studies, with building energy simulation models to determine the energy demand, with finite element method for structural engineering design. OpenFOAM represents an extraordinary opportunity for all CFD users worldwide to share codes and case studies, to explore the potential of new functionalities and strengthen the network within the CFD community.
Profile mit Bedacht schärfen, statt überhastete Maßnahmen umzusetzen
Authors: Breuer-Nyhsen Julia , Klomann Verena
Published: June 2024

In nahezu allen Handlungs- und Tätigkeitsfeldern der Sozialen Arbeit ist der Bedarf und Mangel an gut qualifizierten Fachkräften immanent. Der oft große Handlungsdruck führt zu pragmatischen Lösungen, mit denen nicht selten erhebliche Abstriche in der Qualität der (dann mitunter nicht mehr) professionellen Leistungserbringung einhergehen. Anstatt diese Entwicklungen weiter zu befördern, braucht es – ganz besonders in dieser oft als Fachkräfte-Krise bezeichneten Situation – ein Innehalten, um langfristig wirkungsvolle und professionalitätserhaltende Bewältigungsmöglichkeiten zu entwickeln.
Maternal Gene Delivery for the Prevention and Treatment of Obstetric Conditions
Authors: D’Alessandro Sophia C. , Boyle Ashley K.
Published: June 2024

Purpose of Review

There is an urgent unmet clinical need for effective therapies for obstetric complications such as fetal growth restriction, pre-eclampsia and preterm birth. These global health conditions have long term consequences for the health of survivors and the families affected, as well as societal and economic implications. Despite decades of research, prevention and treatment options are limited and often demonstrate suboptimal efficacy. Thus, cutting-edge approaches, such as gene delivery, may offer a novel solution. With the advancement of prenatal gene therapy preclinical research for the treatment of inherited diseases, the safe delivery of these technologies in utero is becoming a reality.

Recent Findings

We will give insights into the current preclinical research specifically relating to maternal gene delivery to improve the outcomes of pregnancy-related conditions.

Obstetric disorders lack effective prevention and treatment options. Innovative approaches, such as gene transfer, may provide a promising alternative. This review summarises potential gene therapies in preclinical development for fetal growth restriction, pre-eclampsia and preterm birth.
Parameter study of an Al–Cr–Mo–Sc–Zr alloy processed by laser powder bed fusion reaching high build rates
Authors: Agricola Barbara , Bierwisch Claas , Palm Frank , Jägle Eric A. , Kerschenlohr Annegret
Published: June 2024

Market availability of aluminum alloys for laser powder bed fusion (L-PBF) is still highly limited in comparison to conventional manufacturing processes. The demand for high-strength but inexpensive alloys specifically designed for L-PBF is high. This demand has led to research on a variety of adapted conventional alloys which are still limited to utilize the full potential of L-PBF. Scalmalloy® (Al–Mg–Sc–Zr) satisfies the demand for high-strength L-PBF-alloys but needs a high energy input and has troubles with evaporation of Mg. Scancromal® (Al–Cr–Sc–Zr) is a novel alloying system for L-PBF and was first introduced in 2019 with the possibility of higher build rates and comparable strengths to Scalmalloy®. In this paper, a more economic low Sc-containing version of Scancromal® is presented. A parameter study was performed for $${100}\,\upmu \hbox {m}$$

100

μ
m

layer thickness reaching high build rates of about $${47}\,\hbox {cm}^{3}\,\hbox {h}^{-1}$$

47

cm
3

h

-
1

. Hardness tests for different parameters were carried out and showed a stable process window with a hardness comparable to AlSi10Mg. Additionally, two-dimensional multilayer process simulations showed a potential for increasing the layer thickness to $${150}\,\upmu \hbox {m}$$

150

μ
m

and therefore a significant increase in build rate of up to $${70}\,\hbox {cm}^{3}\,\hbox {h}^{-1}$$

70

cm
3

h

-
1

highlighting the high productivity potential of Al–Cr alloys for L-PBF.
Optimized CO2 photoreduction using cuprous oxide (Cu2O) nanoparticles synthesized using Psidium guajava extract
Authors: Torres-Arellano S. , Luevano-Hipolito E. , Fabela-Cedillo Mayte G. , Aleman-Ramirez J. L. , Torres-Martínez Leticia M. , Sebastian P. J.
Published: June 2024

This work aims to propose earth-abundant materials for CO2 photoreduction to generate renewable solar fuels to provide practical solutions to global warming. The selected material in this case is cuprous oxide (Cu2O), one of the most promising photocatalysts for CO2 photoreduction due to its high affinity to solar radiation and electronic properties. Cu2O nanoparticles (NPs) were synthesized using Psidium guajava residue for the photocatalytic CO2 reduction. The aqueous residue of the Psidium guajava fruit proved to be suitable for stabilizing and acting as a reducing agent for the synthesis of Cu2O NPs. The XRD analysis confirmed the formation of the cubic structure of Cu2O. The nanoparticles absorb light from 430 nm with a direct bandgap value of around 1.8 eV. Cu2O NPs exhibited activity for CO2 photoreduction, whose efficiency was optimized by an orthogonal Taguchi L9 design. The factors studied were catalyst loading, air flow, and temperature. During the use of Cu2O NPs in the CO2 photoreduction HCOOH was identified as the main product, with an optimized production of 103.4 µmol h− 1 under visible light. Also, it was demonstrated the photocatalytic activity of the Cu2O NPs for H2 evolution by water splitting.

Graphical Abstract
Monitoring Creeping Landslides with InSAR in a Loess-covered Mountainous Area in the Ili Valley, Central Asia
Authors: Fan Binbin , Luo Geping , Hellwich Olaf , Shi Xuguo , Yuan Xiuliang , Ma Xiaofei , Shang Ming , Wang Yuangang
Published: June 2024

Loess landslides in mountainous regions of the Ili Valley have resulted in numerous casualties as well as huge economic losses. However, the characteristics and driving mechanisms of surface deformation related to loess landslides in mountainous areas remain unclear, thus limiting our ability to identify, monitor, and warn populations of potential catastrophic events. This study was conducted in a typical mountainous area of the Ili Valley, where landslides have been documented by field investigations, unmanned aerial vehicle images, and light detection and ranging data. With ascending and descending Sentinel‑1 time series synthetic aperture radar images, acquired using the small baselines subset method, surface deformation was observed for the period from October 2014 to October 2021, and loess landslides were concurrently mapped to delineate hazardous areas. Using the methods of this study, we were able to identify 74.4% of previously documented landslides. Additionally, we observed a seasonal time-series of deformation that had a time delay of less than one month and was responsive to rainfall. Our analysis of the characteristics and driving mechanisms of creeping landslides in the Ili Valley led to the compilation of a new inventory of active slopes that will offer valuable guidance for land managers tasked with implementing disaster prevention measures.
Navigating Waste Oil Re-refinery Paths with Consideration of Uncertainty: A Sequential Optimization Approach
Authors: Yeo Lip Siang , Hie Jonathan Yung Chuan , Andiappan Viknesh , How Bing Shen
Published: June 2024

Circular economy (CE) appears to be a plausible strategy to reduce emissions and enhance sustainability for the heavy-emitting sector including the oil and gas sector. This works explores the feasibility of waste oil (WO) re-refinery pathways. Given that the moisture content of the WO plays an important role in the production cost and product quality, the impact of the moisture content variation is considered in this work. The influence of feed uncertainty on the decision-making model is modelling through a sequential optimization approach by employing Aspen HYSYS and Monte-Carlo simulation to synthesize the optimal re-refining solution in terms of (i) economic domain, (ii) environmental domain, and (iii) product quality domain. This work is essentially a step-by-step guide that helps project engineers in making informed decisions in selecting and designing the waste oil re-refinery facilities.
Between Picasso and Researcher: Introducing Cubistic Perspectives to the Social Sciences
Authors: Campill Marc Antoine
Published: June 2024

The following article introduces the notions of Cubism and fragmental insights into Picassos’ life as crucial material for the future of social sciences. In particular the use of subjectivity as the tool is underlined, by the notion of artifact construction and the interplay between being an artist and being a human with everyday life struggles. Cultural psychological perspectives intermingled with the awareness of the fluid and dynamic sense of material reality are used to underline where and how social sciences can improve their cultivation while respecting reality in its whole interconnectivity. In particular the notions between linearity and curvature in the context of Picasso’s perspective and art expressions of women are explored and used to understand the instability of generalization.
Steady Periodic Hydroelastic Waves in Polar Regions
Authors: Matioc Bogdan-Vasile , Părău Emilian I.
Published: June 2024

We construct two-dimensional steady periodic hydroelastic waves with vorticity that propagate on water of finite depth under a deformable floating elastic plate which is modeled by using the special Cosserat theory of hyperelastic shells satisfying Kirchhoff’s hypothesis. This is achieved by providing a necessary and sufficient condition for local bifurcation from the trivial branch of laminar flow solutions.
Ethnobotany, pharmacology and phytochemistry of medicinal plants used for management of Diabetes mellitus in Uganda, Kenya, Tanzania and the Democratic Republic of Congo
Authors: Dangana Reuben Samson , Abubakar Ibrahim Babangida , Shinkafi Tijjani Salihu , Aja Patrick Maduabuchi , Nwozo Sarah Onyenibe
Published: June 2024

Ethnopharmacological relevance

For decades, patients in East Africa have used herbal medicine as an alternative and affordable therapeutic option for the treatment of diseases such as Diabetes Mellitus (DM). As a result, the primary objective of this research was to thoroughly investigate the plants employed for treating DM in Uganda, Tanzania, Kenya, and the Democratic Republic of Congo and gather pertinent ethnopharmacological and ethnomedicinal knowledge that could be applied in the development of therapies for DM.

Aim of the study

The study is aimed at critically reviewing the phytochemistry, pharmacology, and toxicology of medicinal plants used for treating diabetes in East African countries, including Uganda, Tanzania, Kenya, and the Democratic Republic of Congo.

Method

A search for relevant articles was conducted on PubMed, ISI Web of Science, Open Theses, Google Scholar, Science Direct, and Scopus, and the identified articles were evaluated for quality, relevance, and taxonomical accuracy before undergoing review.

Results

The study identified 140 plant species used by local communities for the management of DM in East Africa, with decoction and infusion being the most common preparation methods. The leaves, root, and stem bark were the most frequently used parts. The in vitro and in vivo studies demonstrated the antidiabetic effect of medicinal plants such as Kigelia Africana, Hagenia abyssinica (Rosaceae), Physalis peruviana among other plants used for herbal treatment elicited via stimulation of insulin release, and gluconeogenesis. Phytochemicals present were mainly flavonoids, alkaloids, saponins, tannins, and glycosides.

Conclusion

The local communities in Uganda, Tanzania, Kenya and the Democratic Republic of Congo use herbal medicine for managing DM, but only a fraction of these species have scientific evidence. This necessitates additional research to establish the effectiveness, safety and potentially develop novel therapeutics for the management of DM.
Straightforward microwave-assisted synthesis of ultrasmall gold nanoparticles acnhored on reduced graphene oxide for enhanced antibacterial application
Authors: Fathurrohman Pandji Zamzami , Kunarti Eko Sri , Wijayanti Nastiti , Santosa Sri Juari
Published: June 2024

Graphene derivative materials, such as graphene oxide (GO) and reduced graphene oxide (rGO), have garnered significant attention from scientists for over two decades due to their distinctive characteristics and versatile applications across various fields, particularly in biomedical applications. Incorporating gold nanoparticles (AuNPs) into rGO sheets as rGO-Au nanocomposites further enhances its performance in biomedical applications. This study presents a rapid and efficient method for synthesizing ultrasmall AuNPs anchored on reduced graphene oxide (rGO-Au) using microwave irradiation and ascorbic acid. The optimum microwave treatment was 4 min, ensuring the highest GO reduction degree. Structural characterization by TEM reveals a distinctive architecture with ultrasmall AuNPs (average size of 2.2 nm) distributed on the rGO sheets. Interestingly, while the synthesized rGO-Au did not exhibit any antibacterial activities against both Escherichia coli and Staphylococcus aureus in disk diffusion assays, it demonstrated bacteriostatic effect at remarkably low concentrations when assessed by optical density measurement. The effective concentration of rGO-Au to inhibit E. coli growth was determined to be 2.5 ppm, while for S. aureus, it was 5 ppm, resulting in growth inhibition of 53.1% and 50.0%, respectively. These findings provide a straightforward synthesis route for rGO-Au nanocomposites and underscore the importance of AuNPs’ size and quantity in modulating antibacterial properties.
Sub-global equilibrium method for identification of elastic parameters based on digital image correlation results
Authors: Nowak Marcin , Szeptyński Paweł , Musiał Sandra , Maj Michał
Published: June 2024

In this work, a new, simple method is presented, which enables identification of material properties of solids basing on the digital image correlation (DIC) measurements. It may be considered as a simplified alternative of low computational complexity for the well-known finite element model updating (FEMU) method and virtual fields method (VFM). The idea of the introduced sub-global equilibrium (SGE) method is to utilize the fundamental concept and definition of internal forces and its equilibrium with appropriate set of external forces. This makes the method universal for the use in the description of a great variety of continua. The objective function is the measure of imbalance, namely the sum of squares of residua of equilibrium equations of external forces and internal forces determined for finite-sized part of the sample. It is then minimized with the use of the Nelder–Mead downhill simplex algorithm. The efficiency of the proposed SGE method is shown for two types of materials: 310 S austenitic steel and carbon-fiber-reinforced polymer (CFRP). The proposed method was also verified based on FE analysis showing error estimation.
Exploiting the margin: How capitalism fuels AI at the expense of minoritized groups
Authors: Colón Vargas Nelson
Published: June 2024

This paper explores the intricate relationship between capitalism, racial injustice, and artificial intelligence (AI), arguing that AI acts as a contemporary vehicle for age-old forms of exploitation. By linking historical patterns of racial and economic oppression with current AI practices, this study illustrates how modern technology perpetuates and deepens societal inequalities. It specifically examines how AI is implicated in the exploitation of marginalized communities through underpaid labor in the gig economy, the perpetuation of biases in algorithmic decision-making, and the reinforcement of systemic barriers that prevent these groups from benefiting equitably from technological advances. Furthermore, the paper discusses the role of AI in extending and intensifying the social, economic, and psychological burdens faced by these communities, highlighting the problematic use of AI in surveillance, law enforcement, and mental health contexts. The analysis concludes with a call for transformative changes in how AI is developed and deployed. Advocating for a reevaluation of the values driving AI innovation, the paper promotes an approach that integrates social justice and equity into the core of technological design and policy. This shift is crucial for ensuring that AI serves as a tool for societal improvement, fostering empowerment and healing rather than deepening existing divides.
How do short-term and long-term factors impact the aboveground biomass of grassland in Northern China?
Authors: Zhu Xiaoyu , An Yi , Qin Yifei , Li Yutong , Shao Changliang , Xu Dawei , Yan Ruirui , Zhou Wenneng , Xin Xiaoping
Published: June 2024

The aboveground biomass (AGB) of grassland, a crucial indicator of productivity, is anticipated to widespread changes in key ecosystem attributes, functions and dynamics. Variations in grassland AGB have been extensively documented across various spatial and temporal scales. However, a precise method to disentangle long-term effects from short-term effects on grassland AGB and assess the attribution of explanatory factors for AGB change remains elusive. This study aimed to quantify the impact of key climatic factors, soil properties, and grazing intensity on grassland AGB changes, utilizing data spanning the 1980s and the 2000s in Northern China. The Co-regression model was explored to separate the long-term effects and short-term effects of grassland AGB, while the Generalized Linear Model (GLM) was utilized to analyze the contributions of key variables to AGB. This approach effectively avoids issues related to regression to the mean and mathematical coupling. The results revealed that the influence of climatic variables, soil texture and grazing intensity on grassland AGB changes could be decomposed into long-term, short-term and random effects. Long-term effects explained 73.6% of AGB variation, whereas short-term effect only accounted for 5.9% of AGB change. Additionally, the short-term effect was divided into direct and indirect effects, with the direct effect explaining 1.3% of AGB variation, and the indirect effect explained 4.6% of AGB dynamics. The relative importance of key variables in grassland AGB was assessed, identifying soil parameters and precipitation as the main driving factors in the study area. This study introduces a robust methodology to enhance model performance in distinguishing long-term and short-term effects on grassland AGB, contributing to the sustainable development of grassland ecology in similar regions.
Using dialogue-centered approaches to community-engaged research: an application of dialectical inquiry
Authors: Sanders Catherine E. , Borron Abigail , Lamm Alexa J. , Harrell Ellen , Worley Barbara
Published: June 2024

Rural communities across the United States experience increased risk and prevalence of chronic diseases associated with both individual and community-based factors. Thus, there is a need for rural capacity development for chronic disease prevention. Traditional health promotion and intervention approaches often focus on diet-related health disparities from a positivist, evidence-based paradigm. To counter positivist bias within health promotion research, a hybridized approach is proposed using a critical-constructivist paradigm incorporating dialectical thinking, appreciative inquiry, and dialectical inquiry to address cultural and structural barriers, as well as community-based social norms, through evaluation of community-based health promotion interventions. Three dialectical models were identified through interviews with community coalition members: social ties, infrastructure, and worldviews, examining underlying assumptions and counter assumptions. By revealing the dialectic assumptions and counter assumptions within project implementation, practitioners can engage in constructive dialogue with communities to determine more effective and culturally responsive pathways for project development.
Changes in the spatial variability of extreme precipitation characteristics across Peninsular India
Authors: Dash Subhasmita , Sreya T. U. , Maity Rajib
Published: June 2024

Through a comprehensive analysis, this study portrays the changing spatial variability of extreme precipitation characteristics as a consequence of a gradually warming climate in peninsular India. In particular, it emphasizes the coastal areas that are under increased exposure to frequent extreme events in the recent past. Different extreme precipitation characteristics are considered, and the change points are identified based on their trend, mean and standard deviation. Changes in the spatiotemporal variability of extreme precipitation characteristics are identified through empirical orthogonal functions (EOFs). Our findings illustrate the occurrence of discernible changes almost all over the region with varying time points (1970 to 2011), and the extremes with higher thresholds exhibit more prominent changes. More importantly, a notable disparity in extreme indices expressing intensity is observed between the eastern and western coastal regions: change points for the eastern coastal areas (the Bay of Bengal side) predominantly emerged in the post-1980s, in contrast to the pre-1980s points across the western coastal (the Arabian Sea side) regions. Furthermore, after 2001, the spatial coverage of the western region notably expanded, as indicated by a significant increase in wet extremes, including those at the southernmost tip of India. Concurrently, extreme dry events significantly decreased across most of southern India during this period. On the other hand, the intensification of precipitation has become more prominent towards the Bay of Bengal side than towards the Arabian Sea side. This may be attributed to the increased cyclonic activity in the Bay of Bengal. Overall, the findings of this study will aid in understanding the evolving spatial pattern of extreme precipitation indices and will contribute to better management of extreme events and related hazards across peninsular India.
Contemporary Quantum Mechanics in Practice
Authors: Woods Lilia M. , Rodríguez López Pablo
Published: June 2024

This helpful and pedagogical book offers problems and solutions in quantum mechanics from areas of current research, rarely addressed in introductory courses or textbooks. It is based on the authors' own experience of teaching undergraduate and graduate courses in quantum mechanics, and adapts problems from contemporary research publications to be accessible to students. Each section introduces key quantum mechanical concepts, which are followed by exercises that grow progressively more challenging throughout the chapter. The step-by-step solutions provide detailed mathematical derivations, and explore their application to wider research topics. This is an indispensable resource for undergraduate and graduate students alike, expanding the range of topics usually covered in the classroom, as well as for instructors and early-career researchers in quantum mechanics, quantum computation and communication, and quantum information.
Helping Friends and Harming Enemies
Authors: Blondell Ruby , Konstan David
Published: June 2024

Sophocles is often considered the least philosophical of the three great Greek tragedians. However, Ruby Blondell offers a vital examination of the ethical content of the plays by focusing on the pervasive Greek popular moral code of 'helping friends and harming enemies'. Five of the extant plays are discussed in detail from both a dramatic and an ethical standpoint, and the author concludes that ethical themes are not only integral to each drama, but are subjected to an implicit critique through the tragic consequences to which they give rise. Greek scholars and students of Greek drama and Greek thought will welcome this book, which is presented in such a way as to be accessible to specialists and non-specialists alike. No knowledge of Greek is required. This revised edition includes a contextualising new Foreword which engages with critical and scholarly developments in Greek drama since the original publication.
Correspondence Primarily on Pamela and Clarissa (1732–1749)
Authors: Richardson Samuel
Published: June 2024

Samuel Richardson was one of the great letter-writers in English. His three great novels, Pamela, Clarissa, and Sir Charles Grandison were written in epistolary form, and Richardson himself was known in his time for the way he used his letters for both professional and personal purposes. As a printer, Richardson corresponded with authors, readers, and other printers and publishers. As a friend, he supported his correspondents when they were personally struggling. As a novelist, he engaged readers both before and after the publication of his works, soliciting their opinion and defending his own methods. Correspondence Primarily on Pamela and Clarissa (1732–1749) gives us Richardson the printer, the friend, and the novelist in the crucial early years of his unexpected success and fame as a literary writer, providing insight into how and why he created innovative works that changed the course of literary history.
Buffering of Aerosol‐Cloud Adjustments by Coupling Between Radiative Susceptibility and Precipitation Efficiency
Authors: Song Ci , McCoy Daniel T. , Eidhammer Trude , Gettelman Andrew , McCoy Isabel L. , Watson‐Parris Duncan , Wall Casey J. , Elsaesser Gregory , Wood Robert
Published: June 2024

Aerosol‐cloud interactions (ACI) in warm clouds are the primary source of uncertainty in effective radiative forcing (ERF) during the historical period and, by extension, inferred climate sensitivity. The ERF due to ACI (ERFaci) is composed of the radiative forcing due to changes in cloud microphysics and cloud adjustments to microphysics. Here, we examine the processes that drive ERFaci using a perturbed parameter ensemble (PPE) hosted in CAM6. Observational constraints on the PPE result in substantial constraints in the response of cloud microphysics and macrophysics to anthropogenic aerosol, but only minimal constraint on ERFaci. Examination of cloud and radiation processes in the PPE reveal buffering of ERFaci by the interaction of precipitation efficiency and radiative susceptibility.
Donor human milk versus infant formula for low-risk infants: a systematic review
Authors: McClintock Thomas , Fiddes Catherine , Harris Shalee , Embleton Nicholas , Lin Luling , Bloomfield Frank H. , Muelbert Mariana
Published: June 2024

Background

There is an increasing acceptance and use of donor human milk (DHM) in healthy infants. This review investigates the benefits and risks of mothers’ own milk (MOM) supplementation with DHM compared to infant formula (IF) in moderate-late preterm (MLP) and early term (ET) infants.

Methods

MEDLINE, EMBASE, CINAHL, Scopus, Cochrane CENTRAL and clinical trial registries were searched for studies published up to September 2023. The primary outcome was rates of exclusive breastfeeding (EBF). Certainty of evidence was assessed using GRADE framework. RoB1 and EPHPP were used to assess risk of bias for controlled trials and observational studies, respectively.

Results

Eleven studies involving total of 10,147 infants and six ongoing trials were identified. Studies were of low quality, and the certainty of evidence was assessed as very low. Three studies suggested benefits of DHM compared to IF on EBF at discharge, while two suggested no difference. No clear effect was observed on EBF duration, any breastfeeding, hypoglycemia and morbidity. No health risks were reported.

Conclusion

The effect of supplementing MOM with DHM instead of IF on EBF and other health outcomes is unclear. High-quality studies are required to determine the potential benefits or risks of DHM supplementation in this population.

Impact

We identified 11 relevant studies reporting on supplementation of mothers’ own milk (MOM) with donor human milk (DHM) compared to infant formula (IF). Studies were of low quality, had heterogeneous outcome definitions and were geographically limited; all except two were observational studies.

Limited evidence showed no clear difference on rates of exclusive breastfeeding and other health outcomes. No potential risks were reported.

The increasing acceptance and use of DHM in healthy infants highlights the need for future high-quality studies.