Entire Ecosystem Research Articles

Security and privacy challenges, issues, and enhancing techniques for Internet of Medical Things: A systematic review

AbstractThe Internet of Things (IoT) is a rapidly expanding network of interconnected things that use embedded sensors to gather and share data in real‐time. IoT technologies have given rise to many networking applications in our everyday life such as smart homes, smart cities, smart transport, and so forth. Smart healthcare is one such application that has been revolutionized by the IoT, introducing a new branch of IoT known as the Internet of Medical Things (IoMT). IoMT encompasses an entire ecosystem consisting of smart wearable, implantable sensing equipment's or devices, transmitters that are critical for monitoring the patients remotely and continuing the real‐time and has opened the door to new innovative smart healthcare approaches while improving patient care outcomes. IoMT wearable and embedded sensing devices are commonly utilized in smart healthcare to capture medical data and transmit the medical data in a communication network stored in the cloud. The large volume of data generated and transmitted by these IoMT devices is rising at an exponential rate, resulting in an increase in security and privacy vulnerabilities of healthcare data. To ensure the Confidentiality and integrity of the IoMT devices and the sensitive medical data, there should be proper security and privacy measures such as access control, passwords, multifactor authentication, and encryption of data generated, transmitted, or processed in the IoMT framework. In this paper, we identified the internet of things and its applications in smart healthcare systems. Additionally, the paper focuses on the architecture of IoMT, and several challenges, including the IoMT security and privacy requirements, and attack taxonomy. Furthermore, the paper thoroughly investigates both cryptographic and non‐cryptographic based security and privacy‐enhancing techniques for IoMT or healthcare systems with particular emphasis on advancements in key areas such as Homomorphic Encryption, Differential Privacy, and Federated Learning.

Quantum Sensing Can Already Make a Difference. But Where?

Quantum sensing technology is already providing value and potential use cases can shape multiple industries. There are four core technologies with promising applications - solid state spins, neutral atoms, superconducting circuits, and trapped ions - which have sensing capabilities across a broad range of physical properties, including magnetic fields, electric fields, rotation, temperature, gravity, time, and pressure. Which quantum sensing technology and implementation type to use depends on the use case, as they measure different properties and are best suited to specific environments. This paper discusses the potential and current applications of the abovementioned quantum sensing technologies through varied use-case studies. They are presented around four specific industries and along different time horizons, emphasizing the need for further development and enhancement that can be achieved over the conventional counterparts. The full power of quantum sensors is not yet known, but they have the potential to create an entire new ecosystem if researchers, start-ups, and industry leaders work together to explore their applications and get them out of the lab. Here, the paper outlines necessary actions and possible avenues for the stakeholders to realize this potential across academia and industry.

A Comprehensive Review on AI-Driven Healthcare Transformation

Introduction: In the dynamic landscape of healthcare, the intersection of cutting-edge technology and patient-centric solutions has sparked a paradigm shift, and at the forefront of this transformative wave is Artificial Intelligence (AI). Problem: Focusing on the multifaceted integration of AI technologies, the narrative explores their pivotal role in enhancing patient outcomes. Objective: This paper delves into the dynamic realm of healthcare transformation over the strategic exploitation of artificial intelligence (AI). Methodology: This paper explores the profound impact of AI on healthcare, examining the current state of the field and envisioning its future. From machine learning algorithms for early disease detection to personalized treatment plans, the paper delves into the diverse applications of AI in healthcare and the potential it holds for revolutionizing the entire ecosystem. From customized treatment strategies to streamlined healthcare processes, the paper unravels the diverse ways AI is reshaping the healthcare landscape. Results: The imperative to improve patient outcomes, characterized by enhanced efficiency, precision, and personalized care, finds a promising ally in AI. The paper has illuminated how AI technologies, including machine learning and advanced analytics, offer tangible solutions to longstanding challenges in diagnostics, treatment planning, and patient engagement. Conclusions: A comprehensive examination of emerging trends illuminates the potential for substantial improvements in patient well-being facilitated by the synergistic partnership of AI and healthcare practices. Originality: The exploration of AI’s applications throughout this paper underscores its transformative potential in reshaping traditional healthcare practices. Restrictions: The prospect of continually improving patient outcomes through innovative technologies remains at the forefront of this transformation.

Dispersal and connectivity in increasingly extreme climatic conditions.

While climate change has been shown to impact several life-history traits of wild-living animal populations, little is known about its effects on dispersal and connectivity. Here, we capitalize on the highly variable flooding regime of the Okavango Delta to investigate the impacts of changing environmental conditions on the dispersal and connectivity of the endangered African wild dog (Lycaon pictus). Based on remote sensed flood extents observed over 20 years, we derive two extreme flood scenarios: a minimum and a maximum flood extent, representative of very dry and very wet environmental periods. These conditions are akin to those anticipated under increased climatic variability, as it is expected under climate change. Using a movement model parameterized with GPS data from dispersing individuals, we simulate 12,000 individual dispersal trajectories across the ecosystem under both scenarios and investigate patterns of connectivity. Across the entire ecosystem, surface water coverage during maximum flood extent reduces dispersal success (i.e., the propensity of individuals to disperse between adjacent subpopulations) by 12% and increases dispersal durations by 17%. Locally, however, dispersal success diminishes by as much as 78%. Depending on the flood extent, alternative dispersal corridors emerge, some of which in the immediate vicinity of human-dominated landscapes. Notably, under maximum flood extent, the number of dispersing trajectories moving into human-dominated landscapes decreases by 41% at the Okavango Delta's inflow, but increases by 126% at the Delta's distal end. This may drive the amplification of human-wildlife conflict. While predicting the impacts of climate change on environmental conditions on the ground remains challenging, our results highlight that environmental change may have significant consequences for dispersal patterns and connectivity, and ultimately, population viability. Acknowledging and anticipating such impacts will be key to effective conservation strategies and to preserve vital dispersal corridors in light of climate change and other human-related landscape alterations.

An Empirical Study on Downstream Dependency Package Groups in Software Packaging Ecosystems

The role of focal packages in packaging ecosystems is crucial for the development of the entire ecosystem, as they are the packages on which other packages depend. However, the evolution of dependency groups in packaging ecosystems has not been systematically investigated. In this study, we examine the downstream dependency package groups (DDGs) in three typical packaging ecosystems—Cargo for Rust, Comprehensive Perl Archive Network for Perl, and RubyGems for Ruby—to identify their features and evolution. We also identify and analyze a special type of DDG, the collaborative downstream dependency package group (CDDG), which requires shared contributors. Our findings show that the overall development of DDGs, particularly CDDGs, is consistent with the status of the whole ecosystem, and the size of DDGs and CDDGs follows a power law distribution. Furthermore, the interaction mechanisms between focal packages and downstream packages differ between ecosystems, but focal packages always play a leading role in the development of DDGs and CDDGs. Finally, we investigate predictive models for the development of CDDGs in the next stage based on their features, and our results show that random forest and Gradient Boosting Regression Tree achieve acceptable prediction accuracy. We provide the raw data and scripts used for our analysis at https://github.com/onion616/DDG.

Histomorphological changes in gills and hepatopancreas of crabs: an integrative review

Crustaceans are a biological model of ecotoxicology and consequently are widely used to assess the impact of xenobiotics. The toxicity of xenobiotics starts in the interaction of biomolecules and can even cause changes in an entire ecosystem. This paper aims to compile the histomorphological changes caused by xenobiotics in the gills and hepatopancreas of crabs through an integrative review. The literature search was done using PubMed with keywords in English and the filters "full text" and "publications from 2001-21". A total of 299 studies were found and 35 of them were selected based on the title and abstract. Later, 17 were included in the study. There were 18 categories of histomorphological changes in the gills and 22 in the hepatopancreas. We noticed that there is no consensus on the terminology used for histomorphological changes in this group. Furthermore, it was observed that the quality of the histological slides was quite diverse, making the identification and comparison of changes in the images problematic. The aforementioned considerations justify the need for this study to address a gap and encourage future research to develop a guide of histomorphological changes in crabs and its use as a tool for environmental biomonitoring.

Ecological implications of unintentional aquaculture escapees: an overview of risks, remediation strategies and knowledge gaps in the aquaculture sector of India and riparian East African countries

The global expansion of aquaculture has driven significant technological advancements, including raceways, Integrated Multitrophic Aquaculture (IMTA) and marine offshore cages. However, unregulated aquaculture escapees pose a severe threat to aquatic biodiversity, acting as a potential time bomb for the entire ecosystem. Addressing this issue requires a comprehensive understanding of the impact of unintentional escapees on aquatic ecosystems, particularly in India and riparian East African countries (Kenya, Uganda, and Tanzania). Using an exploratory research design drawing from various peer-reviewed sources, this study outlines the dynamic growth of aquaculture in these countries, identifies high-impact escape incidents, and correlates risks and remedies with global cases, especially in regions such as Norway, Scotland, Ireland, Colombia, and the United States, where serious incidents of aquaculture escapees have been reported. The research categorizes aquaculture development trends, discusses mechanisms of escapee impact, proposes remedies, assesses methods and inferential strength, and highlights gaps in the existing literature. The study revealed complex ecological shifts caused by aquaculture escapees from invasive non-native fish species, affecting predation, competition, and genetic diversity. Escaped fish from aquaculture facilities pose a significant threat to aquatic biodiversity, especially in the study regions. The escalating risk of unintentional escapes was highlighted in India and three East African countries. To mitigate this, the study proposes integrating escapee management into national fisheries systems, amending fisheries laws, holding fish farmers accountable for aquaculture system failure, and developing comprehensive regulations for non-native species in aquaculture within the study regions. It is recommended to standardize the planning for aquaculture facilities and implement emergency plans, training, local mobilization and further research on the impact thresholds of aquaculture escapees in the study regions. Ecological education in aquaculture communities and the recognition of the role of translational scientists are crucial for the dissemination of knowledge. Urgent government action is needed to address unreported aquaculture escapes, preventing further ecosystem degradation and ensuring global aquaculture sustainability.

Effects of predation risk on parasite-host interactions and wildlife diseases.

Landscapes of fear can determine the dynamics of entire ecosystems. In response to perceived predation risk, prey can show physiological, behavioral, or morphological trait changes to avoid predation. This in turn can indirectly affect other species by modifying species interactions (e.g., altered feeding), with knock-on effects, such as trophic cascades, on the wider ecosystem. While such indirect effects stemming from the fear of predation have received extensive attention for herbivore-plant and predator-prey interactions, much less is known about how they alter parasite-host interactions and wildlife diseases. In this synthesis, we present a conceptual framework for how predation risk-as perceived by organisms that serve as hosts-can affect parasite-host interactions, with implications for infectious disease dynamics. By basing our approach on recent conceptual advances with respect to predation risk effects, we aim to expand this general framework to include parasite-host interactions and diseases. We further identify pathways through which parasite-host interactions can be affected, for example, through altered parasite avoidance behavior or tolerance of hosts to infections, and discuss the wider relevance of predation risk for parasite and host populations, including heuristic projections to population-level dynamics. Finally, we highlight the current unknowns, specifically the quantitative links from individual-level processes to population dynamics and community structure, and emphasize approaches to address these knowledge gaps.

Подготовка и развитие стратегических талантов в Кузбассе

Talent development is a strategic priority of talent management. It engages youth in the implementation of various strategies at different levels. The Kemerovo Region (Kuzbass) has created an entire ecosystem that raises young strategists. Kuzbass Governor Sergei Tsivilev set up the Department or Regional and Industrial Strategizing. Its goal is to promote the strategic guidelines stipulated in long-term plans of regional development, as well as to provide the region with strategic human resources. The Department is a joint project between the Lomonosov Moscow State University and the Kemerovo State University. The cooperation agreement was signed on November 7, 2019. In 2020, the Kemerovo State University launched a Master’s Degree program in Economic and Financial Strategy. Along with the Moscow State University and The National University of Science and Technology (MISiS), the Kemerovo State University became one of the three domestic institutions that were the first to start training specialists in strategizing. Professor Vladimir Kvint’s School of Young Strategists is an international academic project that identifies strategically gifted children and develops their strategic thinking. The Kemerovo Region is the current leader in the number of schools that participate in Vladimir Kvint’s School of Young Strategists.

Blockchain for Healthcare Management Systems : A Survey on Interoperability and Security

In recent years it has been shown that the secure exchange of medical information significantly benefits people’s life quality, improving their care and treatment. The interoperability of the entire healthcare ecosystem is a constant challenge, and even more, with all the risks posed to the security of healthcare information. Blockchain technology is emerging as one of the main alternatives when it comes to finding a balance in the healthcare ecosystem. However, the constant development of new Blockchain technologies and the evolution of healthcare systems make it difficult to find established proposals. From an architectural point of view, the design of blockchain-based solutions requires trade-offs e.g., security and interoperability. This paper focuses on two main objectives, in the first one, it was carried out a Systematic Literature Review for exploring architectural mechanisms used to support the interoperability and security of Blockchain-based Health Management Systems. Taking into account of results, a series of scenarios were generated where these mechanisms can be used along with their context, issues, and various architectural concerns (interoperability and security). In the second objective, a high-level architecture and its validation were proposed through an experiment for the whole process of developing a Domain Specific Language, using the Model Driven Engineering methodology for specific Smart Contracts.

Reducing DNS Traffic to Enhance Home IoT Device Privacy.

The deployment of Internet of Things (IoT) devices is widespread in different environments, including homes. Although security is incorporated, homes can become targets for cyberattacks because of their vulnerabilities. IoT devices generate Domain Name Server (DNS) traffic primarily for communication with Internet servers. In this paper, we present a detailed analysis of DNS traffic from IoT devices. The queried domains are highly distinctive, enabling attackers to easily identify the IoT device. In addition, we observed an unexpectedly high volume of queries. The analysis reveals that the same domains are repeatedly queried, DNS queries are transmitted in plain text over User Datagram Protocol (UDP) port 53 (Do53), and the excessive generation of traffic poses a security risk by amplifying an attacker's ability to identify IoT devices and execute more precise, targeted attacks, consequently escalating the potential compromise of the entire IoT ecosystem. We propose a simple measure that can be taken to reduce DNS traffic generated by IoT devices, thus preventing it from being used as a vector to identify the types of devices present in the network. This measure is based on the implementation of the DNS cache in the devices; caching few resources increases privacy considerably.

Increased harvested carbon of cropland in China

Crop harvested carbon (HC) is one of the most important components of the carbon cycle in cropland ecosystems, with a significant impact on the carbon budget of croplands. China is one of the most important crop producers, however, it is still unknown on the spatial and temporal variations of HC. This study collected statistical data on crop production at the province and county levels in China for all ten crop types from 1981 to 2020 and analyzed the magnitude and long-term trend of harvested crop carbon. Our results found a substantial increase of HC in cropland from 0.185 Gt C yr−1 in 1981 to 0.423 Gt C yr−1 in 2020 at a rate of 0.006 Gt C yr−1. The results also highlighted that the average annual carbon sink removal from crop harvesting in China from 1981 to 2020 was 0.32 Gt C yr−1, which was comparable to the net carbon sink of the entire terrestrial ecosystems in China. This study further generated a gridded dataset of HC from 2001 to 2019 in China by using jointly the statistical crop production and distribution maps of cropland. In addition, a model-data comparison was carried out using the dataset and results from seven state-of-the-art terrestrial ecosystem models, revealing substantial disparities in HC simulations in China compared to the dataset generated in the study. This study emphasized the increased importance of HC for estimating cropland carbon budget, and the produced dataset is expected to contribute to carbon budget estimation for cropland ecosystems and the entire China.

Effects of warming conditions on plant nitrogen–phosphorus stoichiometry and resorption of three plant species in alpine meadows on the Tibetan Plateau

Abstract Global climate change is expected to have a significant impact on ecosystems worldwide, especially for alpine meadows which are considered as one of the most vulnerable components. However, the effects of global warming on the plant nitrogen–phosphorus stoichiometry and resorption in alpine meadows remain unclear. Therefore, to investigate the plant nitrogen–phosphorus stoichiometry and resorption in alpine meadows on the Qinghai-Tibet Plateau, we conducted an artificial warming study using open-top chambers (OTCs) over the 3 years of warming period. We selected three dominant species, four height types of OTCs (0.4, 0.6, 0.8 and 1 m) and four warming methods (year-round warming, winter warming, summer–autumn–winter warming and spring–summer–autumn warming in the experiment) in this experiment. In our study, soil temperature significantly increased with increasing the height of OCTs under the different warming methods. Kobresia pygmaea presented an increase in nitrogen (N) limitation and Kobresia humilis presented an increase in phosphorus (P) limitation with increasing temperature, while Potentilla saundersiana was insensitive to temperature changes in terms of nitrogen and phosphorus limitations. Both nitrogen resorption efficiency:phosphorus resorption efficiency and N:P trends in response to rising temperatures were in the same direction. The differential responses of the chemical stoichiometry of the three species to warming were observed, reflecting that the responses of nitrogen and phosphorus limitations to warming are multifaceted, and the grassland ecosystems may exhibit a certain degree of self-regulatory capability. Our results show that using chemical dosage indicators of a single dominant species to represent the nitrogen and phosphorus limitations of the entire ecosystem is inaccurate, and using N:P to reflect the nutritional limitations might have been somewhat misjudged in the context of global warming.

Contaminants of Emerging Concern: Antibiotics Research in Mussels from the Coasts of the Tyrrhenian Sea (Sardinia, Italy).

Contaminants of emerging concern (CECs) are compounds found in several environmental compartments whose ubiquitous presence can cause toxicity for the entire ecosystem. Several personal care products, including antibiotics, have entered this group of compounds, constituting a major global threat. It is essential to develop simple and reliable methods by which to quantify these contaminants in several matrices. In this work, mussels were chosen as sentinel organisms to assess environmental pollution and the safety of bivalve mollusk consumption according to the "One Health perspective". A liquid chromatographic tandem mass spectrometry method (LC-MS/MS) was developed for the quantification of two macrolides, erythromycin (ERY) and azithromycin (AZI), in mussels. This new method was validated according to international guidelines, showing high selectivity, good recoveries (>60% for both of them), sensitivity, and precision. The method was successfully applied for ERY and AZI research in mussels farmed along the Sardinian coasts (Italy), demonstrating itself to be useful for routine analysis by competent authorities. The tested macrolides were not determined in the analyzed sites at concentrations above the limits of detection (LODs). These results demonstrate the food safety of mussels (as concerns the studied antibiotics) and a negligible amount of pollution derived from these drugs in the studied area.

A meta-research analysis on the biological impact of plastic litter in the marine biota

Marine litter and more specifically plastic marine litter is nowadays considered a global issue with unprecedented impact and consequences to the entire marine ecosystem and biota. The current situation that has been created worldwide due to the abundance of plastic litter in the Earth's Seas has been characterized as alarming, necessitating the immediate action for an overall reduction of plastic waste, better collection and recycling schemes and beach-shoreline clean-ups. In this article we attempt to delve into the details of the magnitude of the impact that plastic litter have caused to marine biota via a meta-research analysis, by compiling, combining, analysing and presenting data from various relative works, using primarily scientific and, secondarily, grey literature. Apart from the threats that plastic marine litter pose to the marine ecosystem, they present potential threats to humans, as well, via food chain. Aside from understating the risks and uncertainties contained in the hereby collected and presenting information, this study can provide an evidence base for decision and policy makers into implementing the appropriate action plans for reducing and, in time, mitigating this immense problem.

Investigating a manufacturing ecosystem in transition toward electric vehicles – a business model perspective

PurposeBusiness Model Innovation is increasingly created by an ecosystem of related companies. This paper aims to investigate the transition of a manufacturing ecosystem toward electric vehicles from a business model perspective.Design/methodology/approachThe authors investigate an automotive manufacturing ecosystem that is in transition toward electric and electrified vehicles, conducting semi-structured interviews with 46 informants from 27 ecosystem members.FindingsThe results reveal that the actions of several ecosystem members are driven by regulations relating to emissions. Novel requirements regarding components and complementary offers necessitate the entry of actors from other industries and the formation of new ecosystem members. While the newly emerged ecosystem has roots in an established ecosystem, it relies on new value offers. Further, the findings highlight the importance of ecosystem governance, while the necessary degree of change in the members' business models depends on their roles and positions in the ecosystem. Therefore, upstream suppliers of components must perform business model adaptation, whereas downstream providers must perform more complex business model innovation.Originality/valueThe paper is among the first to investigate an entire manufacturing ecosystem and analyze its transition toward electric vehicles and the implications for business model innovation.

Microclimate, an important part of ecology and biogeography

AbstractBrief introduction: What are microclimates and why are they important?Microclimate science has developed into a global discipline. Microclimate science is increasingly used to understand and mitigate climate and biodiversity shifts. Here, we provide an overview of the current status of microclimate ecology and biogeography in terrestrial ecosystems, and where this field is heading next.Microclimate investigations in ecology and biogeographyWe highlight the latest research on interactions between microclimates and organisms, including how microclimates influence individuals, and through them populations, communities and entire ecosystems and their processes. We also briefly discuss recent research on how organisms shape microclimates from the tropics to the poles.Microclimate applications in ecosystem managementMicroclimates are also important in ecosystem management under climate change. We showcase new research in microclimate management with examples from biodiversity conservation, forestry and urban ecology. We discuss the importance of microrefugia in conservation and how to promote microclimate heterogeneity.Methods for microclimate scienceWe showcase the recent advances in data acquisition, such as novel field sensors and remote sensing methods. We discuss microclimate modelling, mapping and data processing, including accessibility of modelling tools, advantages of mechanistic and statistical modelling and solutions for computational challenges that have pushed the state‐of‐the‐art of the field.What's next?We identify major knowledge gaps that need to be filled for further advancing microclimate investigations, applications and methods. These gaps include spatiotemporal scaling of microclimate data, mismatches between macroclimate and microclimate in predicting responses of organisms to climate change, and the need for more evidence on the outcomes of microclimate management.

Corporate governance mishap in a startup: a case of GoMechanic

Learning outcomes The case study is designed to help students understand how the “growth at all costs” attitude can lead to compromised corporate governance in a start-up leading to disastrous implications for all the stakeholders. This case study aims to make students understand the components of the fraud triangle, the impact of financial fraud on various stakeholders, the role of venture capitalist (VC) investors and the importance of good corporate governance in start-ups. The case study presents an excellent opportunity for students to discuss the consequences of ignoring good governance in the pursuit of growth in a start-up. After analyzing the case study, the students shall be able to explain the concept of the fraud triangle and to be able to identify the motivation, opportunity and rationalization of financial irregularities in a start-up; analyze the impact of financial irregularities on various stakeholders; comprehend the business model of VCs and evaluate its influence on VC-funded start-ups; and appraise the importance of good corporate governance in start-ups. Case overview/synopsis The case study revolves around the confession of financial irregularities made by one of the cofounders of GoMechanic, a start-up headquartered in Gurugram, India. On January 18, 2023, Amit Bhasin confessed to financial irregularities in the company’s financial statements, leading to laying off 70% of the workforce of the company. GoMechanic had earlier raised close to US$62m [1] from maverick global investors including Sequoia Capital, Tiger Global, Orios Venture Partners and Chiratae Ventures, and was negotiating to raise Series D financing from the Japanese multinational SoftBank with aspirations to be a unicorn (start-up with a valuation of over $1bn). The confession led to a debate about the consequences of the “growth at all cost” culture being followed by start-ups as well as VCs. GoMechanic was not an isolated instance of a lack of governance in the start-ups. The confession had consequences not only for the GoMechanic but for the entire start-up ecosystem of India, which was the third largest in the world. Bhasin stated that the founders take full responsibility for the situation, and they were working on a plan which was most viable under the circumstances. However, it was not going to be easy to regain the confidence of the investors. Complexity academic level The case study is best suited for senior undergraduate- and graduate-level business school students and in executive education programs in courses such as corporate governance and ethics, private equity and entrepreneurial finance. Supplementary material Teaching notes are available for educators only. Subject code CSS 1: Accounting and finance

Climate-Affected Australian Tropical Montane Cloud Forest Plants: Metabolomic Profiles, Isolated Phytochemicals, and Bioactivities.

The Australian Wet Tropics World Heritage Area (WTWHA) in northeast Queensland is home to approximately 18 percent of the nation's total vascular plant species. Over the past century, human activity and industrial development have caused global climate changes, posing a severe and irreversible danger to the entire land-based ecosystem, and the WTWHA is no exception. The current average annual temperature of WTWHA in northeast Queensland is 24 °C. However, in the coming years (by 2030), the average annual temperature increase is estimated to be between 0.5 and 1.4 °C compared to the climate observed between 1986 and 2005. Looking further ahead to 2070, the anticipated temperature rise is projected to be between 1.0 and 3.2 °C, with the exact range depending on future emissions. We identified 84 plant species, endemic to tropical montane cloud forests (TMCF) within the WTWHA, which are already experiencing climate change threats. Some of these plants are used in herbal medicines. This study comprehensively reviewed the metabolomics studies conducted on these 84 plant species until now toward understanding their physiological and metabolomics responses to global climate change. This review also discusses the following: (i) recent developments in plant metabolomics studies that can be applied to study and better understand the interactions of wet tropics plants with climatic stress, (ii) medicinal plants and isolated phytochemicals with structural diversity, and (iii) reported biological activities of crude extracts and isolated compounds.

Popularity Bias in Correlation Graph based API Recommendation for Mashup Creation

The explosive growth of the API economy in recent years has led to a dramatic increase in available APIs. Mashup development, a dominant approach for creating data-centric applications based on APIs, has experienced a surge in popularity. However, the vast array of choices poses a challenge for mashup developers when selecting appropriate API compositions to meet specific business requirements. Correlation graph-based recommendation approaches have been designed to assist developers in discovering related and compatible API compositions for mashup creation. Unfortunately, these approaches often suffer from popularity bias issues, leading to an inequality in API usage and potential disruptions to the entire API ecosystem. To address these challenges, our research begins with a theoretical analysis of the popularity bias introduced by correlation graph-based API recommendation approaches. Subsequently, we empirically validate the presence of popularity bias in API recommendations through a data-driven study. Finally, we introduce the p opularity b ias aware w eb A PI r ecommendation ( PB-WAR ) approach to mitigate popularity bias in correlation graph-based API recommendations. Experimental results over a real world dataset demonstrate that PB-WAR offers the optimal trade-off between accuracy and debiasing performance compared to other competitive methods.