Managing a portfolio whose value closely tracks an index by trading only in a subset of the index constituents involves an NP-hard optimisation problem. In the prior literature, it has been suggested to solve this problem using sequential Monte Carlo (SMC, a.k.a. particle filter) methods. However, this literature does not take transaction costs into account, although transaction costs are the primary motivation for attempting to replicate the index by trading in a subset, rather than the full set of index constituents. This paper extends the SMC approach to index tracking to allow for proportional transaction costs and implements this extended method on empirical data for a variety stock indices. In addition to providing a more practically useful tracking strategy by allowing for transaction costs, we find that including a penalty for transaction costs in the optimisation objective can actually lead to better tracking performance.

We seek to examine effective collaboration when engaging in value cocreation with the customer for mobile financial services in EMs. Our study is a motivation for multi-stakeholder engagement in value cocreation with financial inclusion of all consumers including marginalized populations. Furthermore, our work discovers that social sustainability is a product of social creativity and results in financial inclusion. We apply a qualitative descriptive methodology utilizing exemplars from extant research and phenomenology to analyze the data from the contexts of Ghana and Kenya. The results show that value is co-created for mobile financial services in networks in emerging markets when actors integrate resources for social and environmental sustainability as essential elements for mobile financial inclusion.

This study explores the critical role of supply chain management (SCM) in shaping marketing strategies in emerging markets, where rapid economic growth and dynamic consumer behavior present both challenges and opportunities. Utilizing a qualitative approach, the research synthesizes insights from industry professionals across various sectors to elucidate how effective SCM practices can enhance marketing outcomes. Findings reveal that flexibility in supply chain operations is indispensable for adapting to the volatile market conditions typical of emerging economies, enabling companies to respond swiftly to changes in consumer demand and market trends. Advanced technologies such as predictive analytics, artificial intelligence, and the Internet of Things are identified as key enablers, providing real-time insights that align supply chain processes with marketing objectives. The ability to customize products and services to local preferences, supported by agile supply chains, emerges as a crucial factor in gaining a competitive edge. Cost management and optimization through efficient supply chain practices allow for competitive pricing and effective promotions, essential in price-sensitive markets. The study also highlights the importance of managing regulatory compliance, mitigating risks, and enhancing customer experience through integrated supply chain strategies. Cultural considerations and local market knowledge are critical for aligning product features and marketing messages with consumer expectations. Additionally, sustainability is becoming increasingly relevant, with environmentally conscious supply chains enhancing brand image and supporting marketing strategies. The impact of e-commerce and the role of supplier relationships further underscore the need for cohesive SCM and marketing integration. This research provides a comprehensive framework for understanding how supply chain capabilities can be leveraged to achieve marketing success in emerging markets, offering practical insights for companies seeking to navigate these complex environments effectively.

Enterococcus faecalis is a growing cause of nosocomial and antibiotic-resistant infections. Treating drug-resistant E. faecalis requires novel approaches. The use of bacteriophages (phages) against multidrug-resistant (MDR) bacteria has recently garnered global attention. Biofilms play a vital role in E. faecalis pathogenesis as they enhance antibiotic resistance. Phages eliminate biofilms by producing lytic enzymes, including depolymerases. In this study, Enterococcus phage vB_Efs8_KEN04 (ФKEN04), isolated from a sewage treatment plant in Nairobi, Kenya, was tested against clinical strains of MDR Enterococcus faecalis. This phage had a broad host range against 100% (26/26) of MDR E. faecalis clinical isolates and cross-species activity against Enterococcus faecium. It was able to withstand acidic and alkaline conditions, from pH 3 to 11, as well as temperatures between -80ᵒC and 37ᵒC. It could inhibit and disrupt the biofilms of MDR E. faecalis. Its linear double-stranded DNA genome of 142,402 bp contains 238 coding sequences with a G+C content and coding gene density of 36.01% and 91.46%, respectively. Genomic analyses showed that ФKEN04 belongs to the genus Kochikohdavirus in the family Herelleviridae. It lacked antimicrobial resistance, virulence, and lysogeny genes, and its stability, broad host range, and cross-species lysis indicate strong potential for the treatment of Enterococcus infections.

The West Pomeranian Anti-Crisis Package was an effective tool of the Voivodeship Executive Board of West Pomeranian self-government, which was used to combat and counteract and counteract the social and economic effects of the COVID-19 pandemic. The Anti-Crisis Package was prepared with a view to undertaking systemic, but also point-based measures in order to save and protect regional health care, entrepreneurs, the labour market, but also to use the existing social capital of non-governmental organizations involved in measures reducing the effects of the COVID-19 pandemic. The West Pomeranian Anti-Crisis Package, as evidenced not only by the financial outlays incurred, but also by the outcome of implementing measures achieved in 2020 and their continuation in 2021.

This paper draws upon the theory of turbulence in physics to explore the similarities and differences between volatility in financial markets and turbulent phenomena at a statistical physics level. By analogizing the dynamics of financial markets with fluid turbulence, we have developed an innovative analytical framework aimed at deepening the understanding of the complexity inherent in financial markets. Our research methodology includes a comparative analysis of simulated time series data for the Standard & Poor's 500 Index and fully developed turbulent velocity time series. We focus on the similarities in key statistical physical properties, including probability distributions, correlation structures, and power spectral densities. Furthermore, we have established a model of capital flow in financial markets and proposed corresponding solutions. Through computational simulations and data analysis, we find that while financial market volatility shares some statistical characteristics with turbulent phenomena, significant differences exist in the shapes of probability distributions and the temporal scales of correlations. This suggests that although financial markets exhibit patterns akin to turbulence, the behavior of this complex, multi-variable driven system does not completely correspond to natural turbulence, highlighting the limitations of directly applying turbulence theory to financial market analysis. Our study offers a new perspective for the theories of financial markets and the field of econophysics, providing fresh insights into the complexity of financial markets and contributing to the prevention and management of financial risk.

Digitalization have force emerging markets (EMs) firms operating in resource-constrained environment to adopt market-driven strategies, particularly frugal innovation, to provide affordable, optimize processes and high-value solutions. However, understanding the mechanisms behind developing frugal innovation capability (FIC) at the firm level in diverse EMs remains limited. From the perspective of the Resource-based View, this study added to the existing body of knowledge by exploring how strategic orientation (entrepreneurial orientation (EO) and market orientation (MO)) and organizational ambidexterity (OA) impact the development of FIC in EMs. The study utilized a cross-sectional survey in data collection with 386 valid respondents from Tanzanian manufacturing firms to empirically validate our theoretical predictions. The results show that both EO and MO have strong and positive association with OA and the development of FIC in EMs. In addition, OA partially mediate the relationship of both EO and MO with the development of FIC. Furthermore, our results indicate that MO exerts a more significant impact on the development of FIC than EO in EMs. Managers of manufacturing firms in EMs can use these findings to review their strategic decisions and their exploitative and exploratory approaches to enhance supply chains, develop cost-effective technologies, and produce affordable offerings that cater to the preferences of price-conscious consumers in the digitalization age.

Sustainable food consumption has attracted a widespread attention during last decades by scholars, policy makers and consumers. In line with this, farmers’ markets (FMs) have the potential to encourage sustainable agricultural production and consumption. By reducing the number of actors and distances along the food chain, these alternative food systems foster the reconnection between farmers and consumers and contribute to different social, economic and environmental sustainable goals. This paper provides insights on the role of consumers' sustainability concerns related to their motivation for shopping at FMs. By means of a choice experiment, we analyze the determinants of consumers’ WTP for buying apples at FMs. We are particularly interested in understanding how attitudes towards the three sustainability dimensions are related to consumer preferences in this context. We find that consumer attitudes towards direct contact with producers, contributing to farmers’ income, and environmental benefits can be directly related to product characteristics that are specific to FMs.

Fish is a very healthy food that is a great source of proteins, vitamins, minerals, and essential fatty acids. For many people in developing nations, like Nigeria, smoked and dried fish serves as a significant source of protein and is an integral part of the traditional diet. However, smoked and dried fish are susceptible to fungal growth, especially when the storage conditions are not right, and it has been discovered that many products contain fungi that are easily visible on the surface. This study investigated the diversity and occurrence frequency of contaminated fungi species in dried fishes sold in local markets of Awka in Anambra state, Nigeria. Twenty-four fungal isolates with varying colony morphology were isolated from the various dried fish products. The contaminating species were further subjected to lactophenol cotton blue (LPCB) staining and Ten (10) Unique fungal morphology was detected. Carbohydrate utilization test was used to select unique isolates and further molecular identification showed successful PCR amplification of the isolates. The current study identified various fungal groups contaminating dried fish products sold in Awka Markets. The three dominant genera were the fungal genera' Aspergillus, Penicillium and Fusarium. These results suggest that most dried fish being sold in the Markets are contaminated with multiple fungi, posing a critical health risk to individuals. Thus, there is a need for attention to be paid to the packaging and storage of dried fish to lessen the likelihood of air exposure, lower water activity, and prevent microbiological growth on the dried fish.

The present study discusses how adopting cryptos affects financial inclusion in developing economies. Primary constructs like Financial Inclusion (FI), Perceived Economic Empowerment (PEE), Trust in Financial Institutions (TFI), User Satisfaction (US), and Cryptocurrency Adoption (CA) were tested through Structural Equation Modeling (SEM). The results indicated that CA significantly and positively influenced FI, US, TFI, and PEE. These relationships extend to the interaction effects: US, TFI, and PEE all positively related to FI. It is a reflection of cryptocurrencies as an opportunity to redress most of the afflictions characteristic of traditional finance systems and to promote financial inclusion and economic empowerment in developing countries. Future research should also investigate whether digital literacy and regulatory environments support cryptocurrency access.

Aflatoxin M1 (AFM1), a carcinogenic toxin, mainly contaminates cow milk and poses significant challenges to the dairy industry and, thereby, public health in most tropical countries, including Ethiopia. This study aimed to investigate the prevalence and level of AFM1 in raw cow milk collected from Specialized Dairy Farms and Milk Vendors in three Urban Centers: Chiro town, Dire Dawa, and Harar cities in Eastern Ethiopia. 180 milk samples were collected from two major Milk Sources: Specialized Dairy Farms (N=90) and Milk Vendors (N=90) using a simple random sampling technique. AFM1 was analyzed using high-performance liquid chromatography. The study revealed that, 63.9% (115/810) prevalence and 0.179±0.48µg/L mean of AFM1 level in milk, which 39.13% and 26.08% exceed the tolerable limits of EU and FDA respectively. A 40.0% prevalence and 0.344±0.72µg/L average of AFM1 in the milk from Dire Dawa city was significantly higher than the samples from other Urban Centers. However, 30.43% and 29.57% prevalence as well as 0.055±±0.13µg/L and 0.140±0.33µg/L average of AFM1 in milk from Chiro town and Harar city, respectively were not significant. Furthermore, milk samples from Dairy Farms revealed a higher prevalence (57.39%) and level (0.252±0.64µg/L) of AFM1 than Milk Vendors, with a prevalence of 42.61% and an average of 0.107±0.21µg/L. Therefore, this study concludes that substantial milk samples were contaminated by AFM1 and urges the need to enhance farmers' awareness on mitigation.

The enterprise software sector is experiencing a significant transformation driven by rapid technological progress and evolving business needs. An effective release management process is crucial for the solution providers and customers, as they incorporate the principles of Industry 4.0 and 5.0. With the growing dependence on software solutions and services, ensuring their quality, reliability, and security is highly necessary. The release management process incorporates strategic planning, regression testing, accurate deployment, and continuous monitoring of software updates. However, the rapid advancements in artificial intelligence (AI) and machine learning (ML) create a unique challenge to the release management process. The existing research needs to address the necessary adaptations for Industry 5.0, mainly in product-based software organizations, to the release management process without a change management system. After working with several software solution stakeholders, based on the responses from SaaS project stakeholders, we have presented a revised software release readiness workflow by incorporating a change management module. The preliminary result based on data over a limited period of time indicates that the revised workflow has the potential to improve the management of slippage, particularly by more effectively addressing NFR-related issues, thereby resulting in more predictable and reliable release cycles.

This study challenges the prevailing belief in the necessity of complex models for accurate forecasting by demonstrating the effectiveness of parsimonious econometric models, namely ARCH(1) and GARCH(1,1), over deep learning robust approaches, such as LSTM and 1D-CNN neural networks, in modeling historical volatility within pre-emerging stock markets, specifically the Moroccan and Bahraini stock markets. The findings suggest reevaluating the balance between model complexity and predictive accuracy. Future research directions include investigating the potential existence of threshold effects in market capitalization for optimal model performance. This research contributes to a deeper understanding of volatility dynamics and enhances forecasting models’ effectiveness in diverse market conditions.

The literature study delves into novel therapeutic strategies in transplantation medicine, highlighting unique approaches that could revolutionize current practices. It emphasizes novel patterns that have the potential to transform the current landscape. Commencing with a thorough examination of the historical environment, it traces crucial advances and changes in transplantation. The focus is on traditional techniques, emphasizing their limitations and challenges. An extensive examination of contemporary transplantation therapies offers valuable insights into the most recent progress in organ, cellular, and tissue transplantation. This analysis examines the immunological obstacles and outlines the current advancements and constraints. The exploration further navigates into novel models and concepts such as immunomodulation, developments in organ preservation techniques, gene editing, biomaterials, scaffold-based solutions, and the integration of precision medicine, artificial intelligence, and machine learning applications. Each section analyzes the potential for change and current progress in various areas, backed by relevant scientific sources up to 2023. The subsequent sections delve into innovative cellular therapies, biotechnological progress, current clinical trials, and case studies demonstrating effective applications. This examination delves into the ethical and regulatory considerations of innovative therapeutic procedures, emphasizing ethical implications, regulatory frameworks, and the importance of balancing innovation with patient welfare. The literature review concludes by offering a forward-looking perspective, emphasizing potential progress, anticipated challenges, and advocating for collaboration and interdisciplinary approaches to overcome limitations in the field of transplantation medicine.

Since the Paris Agreement in 2016, the goals of limiting climate change and moving toward climate resilience stand. With a share of about \SI{80}{\percent} of global \ce{CO2} emissions, the energy sector is an essential driver for these goals. A shift to low-carbon energy production and a decentralized system for more efficient energy transmission distribution is necessary. In this paper, we present our work on Modeling of Power Exchanges, Algorithms for \ac{LEM}, Competitiveness of \ac{CHP} and Energy Feedback Devices. The study was conducted considering technical, economic, social and regulatory framework. For easy integration into energy simulations or a \ac{DEMS}, a model for power exchanges was created that allows flexible input or deterministic price patterns. The algorithm handles the clearing of an \ac{LEM} by a district aggregator using limit orders with the goal of increasing the share of locally consumed electricity using economic incentives. An investigation was conducted into the operation of flexible \acp{CHP} in low-carbon power systems to balance the volatility of renewable energy. An energy signal light was developed as an energy feedback device, which is integrated into the \ac{DEMS} in a living lab and allows individual configuration. In summary, the results presented should be compared with those of other research approaches in the future and require qualitative and quantitative evaluation.

The study explores how different dimensions of perfectionism influence psychological well-being (PWB) in emerging adults. Literature has deepened the relationships between maladaptive perfectionism (e.g., excessive self-criticism, perceived discrepancy from the standards) and low PWB. Less is known about whether and how adaptive perfectionism (e.g., pursuing personal standards) relates to PWB. Secondly, the study has investigated whether self-compassion (i.e., self-benevolence, seeing personal imperfections as a common condition) may mediate the relationships between adaptive/maladaptive perfectionism and PWB. Participants (N=217, 18-35 y. o.) completed self-report questionnaires measuring: adaptive/maladaptive perfectionism (Almost Perfect Scale-Revised, APS-R: high standards and order/discrepancy, respectively), PWB, and self-compassion (SCS). Adaptive perfectionism resulted associated with PWB, particularly higher presence of purpose in life, environmental mastery, self-acceptance, and personal growth. Conversely, discrepancy resulted the most robust predictor of low PWB (β=-.68), followed by high standards with a positive direction (β=.23; Rc²=.514, p<.001). A strong negative association emerged between discrepancy and SCS (r=-.67, p<.001). A mediation analysis shows that self-compassion has an indirect effect reducing the strength of the relationship between discrepancy and low PWB. Results suggest focusing on self-compassion as a buffer factor that reduces the negative impact of maladaptive perfectionism on psychological well-being. Implications for education and health psychology are discussed.

The rapid increase in metabolic diseases occurred in the last three decades in both industrialized and developing countries has been related to the rise in sugar-added foods and sweetened beverages consumption. An emerging topic in the pathogenesis of metabolic diseases related to modern nutrition is the role of Advanced Glycation Endproducts (AGEs). AGEs can be ingested with high temperature processed foods, but also endogenously formed as consequence of a high dietary sugars intake. Animal models of high sugars consumption, in particular fructose, have reported AGEs accumulation in different tissues in association with peripheral insulin resistance and lipid metabolism alterations. The in vitro observation that fructose is one of the most rapid and effective glycating agent when compared to other sugars has prompted the investigation of the in vivo fructose-induced glycation. In particular, the widespread employment of fructose as sweetener has been ascribed by many experimental and observational studies for the enhancement of lipogenesis and intracellular lipid deposition. Indeed, diet-derived AGEs have been demonstrated to interfere with many cell functions such as lipid synthesis, inflammation, antioxidant defences, and mitochondrial metabolism. Moreover, emerging evidences also in humans suggest that this impact of dietary AGEs on different signalling pathways can contribute to the onset of organ damage in liver, skeletal and cardiac muscle, and brain, affecting not only metabolic control, but global health. Indeed, the here reviewed most recent reports on the effects of high sugars consumption and diet-derived AGEs on human health suggest the need to limit the dietary sources of AGEs, including added sugars, to prevent the development of metabolic diseases and related comorbidities.

Background: The rise of new SARS-CoV-2 mutations brought challenges and progress in the global fight against COVID-19. Mutations in spike and accessory genes affect transmission, vaccine efficacy, treatments, testing, and public health strategies. Monitoring emerging variants is crucial to halt virus spread. Methods: 44 nasopharyngeal/oropharyngeal swabs from Kenyan patients were sequenced with the Illumina platform. Galaxy's bioinformatic tools were used for genomic analysis. SARS-CoV-2 genome classification was done using PANGOLIN and mutation annotation with the COVID-19 Annotator tool. Results: The study showed 5 clades to be circulating in the region. 38(86%) were BA.1.1; 2(5%) were BA.1.1.1; 1(2%) was BA.1; 1(2%) was BA.1.14 and 2(5%) were AY.46. These clades had a cumulative of 173 mutations among them with 50 novel mutations. Forty-eight of these novel mutations occurred in a low frequency of 2.3% of the sequences tested while the other two, S:R214R, and NSP2:A555A, were for 43.2% and 18.2% of the cases respectively. Conclusions: The high-frequency novel mutations were synonymous mutations, a phenomenon that was previously viewed as phenotypically silent but recent studies indicate they can affect viral fitness with potential functional associations. These findings add to the understanding of the SARS-CoV-2 virus future evolutionary and immunological dynamics in the region

We present an open-source Star Tracker (ST) for Attitude Determination. Our implementation makes use of open source software of common usage in astronomy running on a Raspberry Pi 2 platform. The developed platform is open and available for parties interested in further development. Evaluation of the system showed that the ST is suitable for Cubesats of 2U or larger. The ST platform is capable of solving the Lost-In-Space (LIS) problem. Currently, the average accuracy reached of the algorithm is close to 5 seconds of arc with an average processing time of 75 seconds. However, a average accuracy of about 3 minutes of arc can be reached with 35 seconds in average processing time. We also describe the evaluation procedure, the found conclusions of this procedure, in particular the section of the algorithm where most of the processing time is spent and other possible source of errors not included in this study.

The emergence of SARS-CoV-2 mutations pose significant challenges to diagnostic tests as these mutations can reduce the sensitivity of commonly used RT-PCR assays. Therefore, there is a need to design diagnostic assays with multiple targets to enhance sensitivity. In this study, we identified a novel diagnostic target, nsp10 gene, using nanopore sequencing. Firstly, we determined the analytical sensitivity and specificity of our COVID-19-nsp10 assay. The COVID-19-nsp10 assay had the limit of detection of 74 copies/mL (95% confidence interval: 48-299 copies/mL) and did not show cross-reactivity with other respiratory viruses. Next, we determined the diagnostic performance of the COVID-19-nsp10 assay using 261 respiratory specimens, including 147 SARS-CoV-2-positive specimens belonging to ancestral strain and Alpha, Beta, Gamma, Delta, Mu, Eta, Kappa, Theta and Omicron lineages. Using the LightMix E-gene RT-PCR assay as the reference method, the diagnostic sensitivity and specificity of the COVID-19-nsp10 assay were 100%. The median Cp values for the LightMix E-gene RT-PCR and our COVID-19-nsp10 RT-PCR were 22.48 (range: 12.95-36.60) and 25.94 (range 16.37-36.87), respectively. The Cp values of the COVID-19-nsp10 RT-PCR assay correlated well with those of the LightMix E-gene RT-PCR assay (Spearman’s ρ = 0.968; P<0.0001). In conclusion, nsp10 is a suitable target for SARS-CoV-2 RT-PCR assay.

Digital transformation provides new drivers for economic performance growth in enterprises, but can it further improve ESG performance and support sustainable development? Based on the perspective of resources and capabilities, this paper uses the relevant data of 1588 listed companies in strategic emerging industries from 2011 to 2021 to study the differential impact of digital transformation on enterprise ESG performance, and further tests the mediating role of absorptive capacity and the moderating role of regional digital level. The research results indicate that: firstly, digital transformation of enterprises can positively promote the improvement of ESG performance; Secondly, there are significant differences in the impact of digital transformation on the ESG performance of enterprises in terms of property rights and industry characteristics. The ESG performance of state-owned enterprises and high-end equipment manufacturing enterprises is more sensitive to digital transformation; Thirdly, absorptive capacity plays a mediating role between digital transformation and corporate ESG performance; Fourthly, the positive regulation of regional digital level promotes the ESG performance of enterprises through digital transformation. The research conclusion is based on a digital perspective, providing relevant insights for improving the ESG performance of strategic emerging industry enterprises and expanding their ESG development paths.

The flux of opinions in newsletters and social media platforms significantly influences market perceptions regarding corporate entities and their financial instruments. This paper introduces an advanced model, the Sentiment Dynamics Analyzer (SDA), which leverages a refined hierarchical architecture of Transformers to effectively discern the sentiment embedded within financial texts, ranging from news headlines to microblogs. We have enhanced a RoBERTa model with specialized sentiment lexicons and an augmented layer of Transformer models tailored for nuanced sentence-level sentiment detection, aiming to assign a sentiment score from -1 to +1. Our evaluations demonstrate that SDA not only surpasses the previous best methods but also exhibits superior performance over robust baseline models. This success underscores the utility of integrating tailored contextual analyses with sector-specific sentiment insights in improving predictive accuracy.

Real assets in the energy market are subject to ecological uncertainty due to the penetration of renewables. We illustrate this point by analysing electrolysers, a class of assets which recently became the subject of a large interest, as they lead to the production of the desirable green hydrogen and green ammonia. The latter has the advantage of being easily stored and has huge potential in decarbonising both the fertilizer and shipping industries. We consider the optimization of green ammonia production with different types of electricity procurement in the context of stochastic power and ammonia markets, a necessary assumption to translate the features of renewable, hence intermittent, electricity. We emphasise the importance of using stochastic prices to effectively model the volatile nature of the price dynamics, illustrating the project risks that can be mitigated by hedging activities. This study shows the pivotal role of flexibility when dealing with fluctuating renewable production and volatile electricity prices to maximise profits and better manage risks.

We select four important waves of new entrants that knocked on the door of European electricity markets to illustrate how market rules need to be continuously adapted to allow new entrants to come in and push innovation forward. The new entrants that we selected are utilities venturing into neighbouring markets after establishing a strong position in their home market, utility-scale renewables project developers, asset-light software companies aggregating the assets of smaller consumers and producers, and different types of communities. We show that well-intentioned rules designed for certain types of market participants can (unintentionally) become obstacles for new entrants. We conclude that the evolution of market rules illustrates the importance of dynamic regulation. At the start of the liberalisation process the view was that we would deregulate or re-regulate the sector after which the role of regulators could be reduced. But their role has only increased. New players might also present new risks that require intervention by regulators.

Complex models have received significant interest in recent years and are being increasingly used to explain the stochastic phenomenon with upward and downward fluctuation such as the stock market. Different from existing semi-variance methods in traditional integer dimension construction for two variables, this paper proposes a simplified multi-factorized fractional dimension derivation with the exact Excel tool algorithm involving the fractional center moment extension to covariance, which is a complex parameter average that is a multi-factorized extension to Pearson covariance. By examining the peaks and troughs of gold price averages, the proposed algorithm provides more insight into revealing underlying stock market trends to see who is the financial market leader during good economic times. The calculation results demonstrate that the complex covariance is able to distinguish subtle differences among stock market performances and gold prices for the same field that the two variable covariance may overlook. We take the London, Tokyo, Shanghai, Toronto and Nasdaq as the representative examples.

Contaminants of Emerging Concern (CECs), a new category of contaminants in limelight, is a major issue of global concern. The pervasive nature of CECs and its harmful effects, such as cancer, reproductive disorders, neurotoxicity etc., make the situation alarming. The perilous nature of CECs lies in the fact that even very small concentration of CECs can cause great impacts on living beings. It also has a nature of bioaccumulation. Thus, it is the ultimate need to have efficient sensors for detection of CECs to ensure safe living environment. Electrochemical sensors are an efficient platform for CEC detection as they are highly selective, sensitive, stable, reproducible, and prompt, and can detect very low concentrations of analyte. Major classes of CECs are pharmaceuticals, illicit drugs, personal care products, endocrine disruptors, newly registered pesticides, and disinfection by-products. This review focusses on CECs, including their sources and pathways, health effects caused by them, and electrochemical sensors as reported in literatures under each category for the detection of major CECs.

The editorial explores the realm of emerging translational research in neurology and psychiatry, providing insights into various subjects, including neurodegenerative disorders and innovative therapeutic methods. The main objective is to study neurological and psychiatric disorders, such as Alzheimer's disease, with a particular emphasis on techniques for neural regeneration and repair. The editorial highlights the importance of taking a comprehensive approach to studying and treating complex neurological and psychiatric conditions. This involves thoroughly reviewing research papers on the causes, development, biomarkers, and treatment options for these conditions. The editorial emphasizes the necessity of using multidisciplinary and integrative methods to gain a deeper understanding and improve the treatment of these conditions. The development of an animal model for recurrent primary dysmenorrhea showcases the innovative techniques employed in the investigation of gynecological disorders, illustrating the capacity of translational research to connect fundamental scientific knowledge with practical medical applications. The cooperative endeavors of researchers and clinicians emphasized in this editorial lay the foundation for revolutionary advancements that hold the potential to enhance outcomes for individuals afflicted with neurological and psychiatric disorders globally.

Pediatric onset multiple sclerosis (POMS), characterized by the onset of multiple sclerosis before the age of 18, is gaining increased recognition. Approximately 5 percent of MS cases manifest before the age of 18, with less than 1 percent occurring before the age of 10. Despite its rarity, pediatric MS exhibits distinct characteristics, with an association between younger age at onset and a comparatively slower disease progression. Despite this slower progression, individuals with POMS historically reach disability milestones at earlier ages than those with adult-onset multiple sclerosis. While various immunomodulatory agents demonstrate significant benefits in MS treatment, such as reduced relapse rates and slower accumulation of brain lesions on magnetic resonance imaging (MRI), the majority of disease-modifying therapies (DMTs) commonly used in adult MS lack evaluation through pediatric clinical trials. Current evidence is predominantly derived from observational studies. This comprehensive review aims to consolidate existing knowledge on the mechanisms of action, efficacy, safety profiles, and recommended dosages of available DMTs specifically in the context of pediatric MS. Furthermore, the review outlines recent advancements and explores potential medications still in developmental stages, providing a thorough overview of the current landscape and future prospects for treating POMS.

The widespread integration and growing dependence among currency, stock and commodity markets make these markets often very vulnerable to shocks and at risk of collapse at the same time. As a result, these trends threaten the sustainability of the entire financial system. In this study, we explore the spillovers and nonlinear dependences between the seven major foreign ex-change rates, crude oil and gold prices, a global stock price index, and oil and stock implied volatility indices as proxy variables for global risk factors by em-ploying directional spillover network approach. We also use multi-scale de-composition method and nonlinear causality test between these variables to capture multi-level relationships at short and long horizons. Major findings are summarized as follows. First, from the multi-scale decomposition analysis, we identify that Granger causality test results and the direction and strength of return spillovers change with the level of decomposition. Second, the results of nonlinear causality tests show variation in both the significance and direction of Granger causality relationships between the decomposed currency and other series at different timescales, especially for the decomposed oil, gold, and OVX series. Third, the measured directional spillover indices identify the EUR as the largest contributor of connectedness to the other series. The central role of the EUR is a net transmitter of connectedness to gold, oil, the GBP, JPY, and CHF.

We use the Maximum Overlap Discrete Wavelet Transform (MODWT) to analyse the interconnectedness between the stock markets of continental Europe and the BRICS countries. More specifically, we examine the presence of financial contagion in the stock markets of Brazil, Russia, India, China, and South Africa subsequent to the EuroZone Sovereign Debt Crisis (EZDC). We find evidence of co-movement and volatility spillover at short time scales between the Eurozone equity market and the equity markets in South Africa and Russia, with the Eurozone stock market leading the BRICS markets. The presence of co-movement in short time scales is indicative of the presence of financial contagion. No financial contagion is observed in the Brazilian, Chinese, and Indian stock markets during the European sovereign debt crisis. The absence of financial contagion observed in these three BRICS countries during the European sovereign debt crisis suggests that policymakers in these countries should prioritise addressing idiosyncratic shock channels.

Prediction markets are heralded as powerful forecasting tools, but models to describe them often fail to capture the full complexity of the underlying mechanisms that drive price dynamics. To address this issue, we propose a model in which agents belong in a social network, have an opinion about the probability of a particular event to occur, and bet on the prediction market accordingly. Agents update their opinions about the event by interacting with their neighbours in the network, following the Deffuant model of opinion dynamics. Our results suggest that a simple market model that takes into account opinion formation dynamics is capable to replicate the empirical properties of historical prediction market time series, including volatility clustering and fat-tailed distribution of returns. Interestingly, the best results are obtained when there is the right level of variance in the opinions of the agents. Moreover, this paper provides a new way to indirectly validate opinion dynamics models against real data by using historical data obtained from PredictIt, an exchange platform whose data has never been used before to validate models of opinion diffusion.

This paper aims to analyze the effects of Covid-19 on financial markets from the perspective of Complexity. The Covid-19 pandemic has caused turmoil in financial markets and is already one of the most important financial crises in history, causing a fall in several stock markets as well as great volatility. Unlike past crises, which in most cases were caused either by problems of fiscal deficits or in the financial system, this crisis has its origin in an epidemic disease, which occurred in Wuhan, China, and quickly spread across the globe affecting transport networks, commerce and finance, and will affect the public debt of many countries. This is a systemic situation in which there is a need to consider several interconnected systems, financial instability and high financial risk, something that econophysics and some complexity theorists already do. Therefore, this paper intends to show, theoretically, the systemic and complex character of the recent financial crisis caused by Covid-19.

This paper investigates portfolio optimization methodologies and short-term investment strate-gies in the context of the cryptocurrency market, focusing on ten major cryptocurrencies from January 2020 to November 2023. We employ high frequency data and utilize the Kurtosis Mini-mization methodology, alongside other optimization strategies, to construct and evaluate port-folios under different rebalancing frequencies. The empirical analysis reveals that cryptocurren-cies exhibit significant volatility, skewness, and kurtosis, necessitating sophisticated portfolio management techniques. We find that the Kurtosis Minimization methodology consistently outperforms other optimization strategies, delivering optimal returns to investors, particularly in shorter-term investment horizons. We demonstrate the diversification benefits of integrating cryptocurrencies into multi-asset portfolios and emphasize the importance of regular portfolio rebalancing in the volatile cryptocurrency market. Our findings offer insights for portfolio man-agers and investors seeking to optimize their investment outcomes in the cryptocurrency market, highlighting the importance of risk management, diversification, and dynamic portfolio man-agement strategies.

As technology advances, people are increasingly adopting IoT (Internet of Things) devices in smart cities. These devices are equipped with sensors that are connected everywhere, from energy infrastructure and transportation to telecommunications, health and human services, waste management, public safety, and more. People want to access sensor data anytime and everywhere to make better decisions and improve their lives. To achieve this, a sensor integration model has been developed where multiple sensors are integrated under one gateway to manage and share sensor data. However, effective sharing of sensor data requires data security, privacy, confidentiality, fairness, effective architectural design, and low communication and computation costs. These issues need to be addressed in the sensor integration model in smart cities. Existing storage models face challenges in managing sensor data effectively. To overcome these issues and hurdles, a data storage framework leveraging blockchain technology has been proposed within the sensor integration model. This framework ensures data security, privacy, confidentiality, and a fair and transparent financial model, all while minimizing communication and computation costs.

This article provides a comprehensive analysis of the global gas and liquefied natural gas (LNG) markets. The article begins by discussing the increasing demand for gas and LNG, particularly in Asia, as countries aim to transition to cleaner sources of energy. It explores how market volatility has led to energy security interventions and lasting economic and emissions impacts. It then explores the global supply and demand dynamics, highlighting the structural change expected in the market and the competition between Europe and Asia for limited new LNG supply. The article also focuses on Europe's increased flexibility and dependence on LNG imports, which have risen by 60% to 121 million tonnes, offsetting lower Russian pipeline imports. Furthermore, the article delves into global supply and demand dynamics, how the market is expected to remain tight, and how record gas and LNG prices have led to demand reductions. Additionally, it analyzes the future outlook and investment needs, highlighting the continued uptake of gas in heavy-duty transport and the need for further investment to avoid a supply-demand gap. The article concludes with an analysis of the implications for the future of the global gas and LNG markets.

We are currently witnessing a strong global transition towards new, more sustainable models of development and consumption. This transition is both activating and highlighting a series of discrepancies between the various actors in agri-food marketing systems, including the institutions that govern regulatory and trade aspects. These discrepancies highlight how, on a global level, the agri-marketing system is unable to provide adequate responses to the new principles of sustainability that are increasingly expected by civil society. This inability is often linked to the phenomena of opportunism, information asymmetries and lock-in effects (resulting from the slowness or inability of some sectors or actors to evolve technologically and culturally). The control of all these factors is often ineffective and often involves high transaction costs . These conditions lead the global market into an ongoing state of failure, creating structural holes, in which new forms of exchange are born, in response to the principles mentioned above. We identify these as nested markets: hybrid market forms that, thanks to new information technologies, create a new form of proximity that transcends the classical concept of physical proximity. This new proximity directly links producers and consumers in relationships based mainly on two substantial values: reciprocity and reputation. Both of these values significantly reduce transaction costs which today play an increasing role in determining the form and repetition of exchanges. This condition is difficult to interpret from classical and neo-classical economic theories. To do so it is necessary to use a multidisciplinary approach. In this article we combine neo-institutional theory and network analysis to interpret phenomena that are increasingly emerging in all areas of the world. Using these two theories, and case studies from Brasil, North America, China and Europe, we try to highlight how the market is not only the place where the price of products and of their quantities are formed. Market is a complex social spaces, where more-or-less stable relationships are formed, based on the values of reciprocity and reputation that determine behaviour and codes of conduct aimed at containing opportunism and encouraging sustainable, autonomous, forms of self-regulation. These relationships are also the driving forces that ensure the ongoing continuation of the exchanges and the diffusion and preservation of knowledge over time and space. These relationships represent the beating heart of nested markets and their dynamic ability to adapt to the continuous changes that occur in the socio-economic and environmental context in which they are rooted. In this way sustainability is built as an expression of the co-production between different actors (producers, consumers, institutions) and natural resources.

This study estimates the effects of double long memory and structural breaks on the persistence level of six major cryptocurrency markets. We apply the Bai and Perron’s structural break test, Inclán and Tiao’s iterated cumulative sum of squares (ICSS) algorithm, and the fractionally integrated generalized autoregressive conditional heteroscedasticity (FIGARCH) model with different distributions. The results show that long memory and structural breaks characterize the conditional volatility of cryptocurrency markets and confirm our hypothesis that ignoring structural breaks leads to an underestimation of the persistence of volatility modelling. The ARFIMA-FIGARCH model with structural breaks and a skewed Student–t distribution fits the cryptocurrency market’s price dynamics well.

The questions on who is entitled to benefit from REDD+ transactions remains one of the most controversially debated issues around cooperative efforts to reduce deforestation in developing countries. REDD+ has been conceived as international framework for voluntary efforts of developing countries to reduce greenhouse gas emissions and enhance carbon removals from forest activities. Designed as international framework under the UNFCCC that calculates emission reductions and removals (ERRs) at the national -and as an interim step on the subnational level – REDD+ is primarily a creature of international law. However, in defining forest-carbon ERRs the international framework competes with national emission trading systems and domestic REDD+ legislation as well as private standards that define units traded on the voluntary carbon market. The definition of various carbon units is closely linked to the question on who is entitled to participate in REDD+ and benefit from the sale of ERRs under results-based payment schemes or carbon market transactions. This paper applies a legal lens to the various claims to participate in REDD+ transactions. It tries to disentangle the various rights to ERRs, various carbon credits, and payments that come with REDD+ and that almost always create confusion and not seldom conflict around REDD+ implementation. The definition of carbon rights and the legal nature of carbon credits depends on local law and differs between countries. However, there are a number of legal considerations that apply and certain underlying concepts are relevant for the understanding of REDD+ transactions and the allocation of benefits and burdens of conservation activities.

In this work, the Pumped Storage Unit (PSU) is proposed to be integrated with wind-thermal generation system owned by a company whose aim is to maximize its profit through optimal self-scheduling of its units by finding the best bidding strategy in day-ahead energy market. The mathematical formulation of the scenario based unit-commitment and coordinated trading problem is formulated as a stochastic mixed integer linear program. The formulation takes into account several uncertain parameters; such as the wind power generation, energy market prices, and imbalances up/down prices. The expected total profit obtained from energy trading with and without coordinating pumped storage system (PSS) with thermal-wind generation system were compared, and a significant improvement in profit resulted from coordination was observed. Furthermore, coordination between PSS and wind-thermal generation improve the conditional value at risk (CVaR) which monitors the biding risk level.

Nanovaccines are able to deliver antigen(s) and immunomodulator(s) directly to antigen-presenting cells (APCs) of the lymphatic system. Positive charges improve their uptake by APCs and often drive a Th-1 biased immune response so necessary against infectious diseases caused by intracellular pathogens and cancers. However, cationic compounds often display a dose-dependent toxicity so that systematic evaluation of cytotoxicity against cells in culture is required. After the rapid evolution of nanovaccines against SARS-Covid 2, mRNA vaccines gained much strength and have been designed against several infectious diseases, often relying on cationic components for mRNA protection. Once established the limiting concentration for the cationic compound, cationic nanovaccines perform well eliciting the desired Th-1 improved immune response in most cases. Other valuable approach found in the literature has been the construction of biocompatible nanoparticles (NPs) carrying cationic lipids, polymers or surfactants so that minimization of the concentration for the cationic component led to absence of toxicity. Against cancer, recent constructions of nanovaccines in situ after cancer cell disruption in the presence of adjuvants led to systemic presentation of multiple tumoral antigens yielding, in many instances, prevention of metastasis appearance and conversion of tumors non treatable by immunotherapy into treatable ones.

SARS-CoV-2 can induce vascular dysfunction and thrombotic events in patients with severe COVID-19; however, the cellular and molecular mechanisms behind these effects remain largely unknown. In this study, we used a combination of experimental and in silico approaches to explore the mechanisms behind the vascular and thrombotic events in COVID-19. Single-cell RNA-sequencing data from patients with COVID-19 and healthy subjects were obtained from the publicly available Gene Expression Omnibus (GEO) repository. In addition, HUVECs were treated with inactivated protein C before exposure to SARS-CoV-2 infection or a severe COVID-19 serum. An RT-qPCR array containing 84 related genes was used, and the candidate genes obtained were evaluated. Activated protein C levels were measured using an ELISA kit. We identified at the single-cell level the expression of several pro-inflammatory and pro-coagulation genes in endothelial cells from the patients with COVID-19. Furthermore, we demonstrated that exposure to SARS-CoV-2 promoted transcriptional changes in HUVECs that were partly reversed by the activated protein C pretreatment. We also observed that the serum of severe COVID-19 had a significant amount of activated protein C that could protect endothelial cells from serum-induced activation. In conclusion, activated protein C protects endothelial cells from pro-inflammatory and pro-coagulant effects during exposure to the SARS-CoV-2 virus.

This paper attempts to assess the economic significance and implications of collateralization in different financial markets, which is essentially a matter of theoretical justification and empirical verification. We present a comprehensive theoretical framework that allows for collateralization adhering to bankruptcy laws. As such, the model can back out differences in asset prices due to collateralized counterparty risk. This framework is very useful for pricing outstanding defaultable financial contracts. By using a unique data set, we are able to achieve a clean decomposition of prices into their credit risk factors. We find empirical evidence that counterparty risk is not overly important in credit-related spreads. Only the joint effects of collateralization and credit risk can sufficiently explain unsecured credit costs. This finding suggests that failure to properly account for collateralization may result in significant mispricing of financial contracts. We also analyze the difference between cleared and OTC markets.

Cancer develops from abnormal cell growth in the body, causing significant mortalities every year. To date, potent therapeutic approaches have been developed to eradicate tumor cells, but intolerable toxicity and drug resistance can occur in treated patients, limiting the efficiency of existing treatment strategies. Therefore, searching for novel genes critical for cancer progression and therapeutic response is urgently needed for successful cancer therapy. Recent advances in bioinformatic and proteomic techniques have allowed the identification of a novel category of peptides encoded by non-canonical open reading frames (ncORFs) from historically non-coding genomic regions. Surprisingly, many of the ncORFs express functional microproteins that play a vital role in human cancers. In this review, we provide a comprehensive description of different ncORF types with coding capacity and technological methods in discovering ncORFs among human genomes. We also summarize the diagnostic and prognostic value of these ncORF as well as their carcinogenic role in cancer progression and underestimated potential as therapeutic targets.

Immunosuppressed individuals, such as people living with HIV (PLWH), remain vulnerable to severe COVID-19. We analyzed the persistence of specific SARS-CoV-2 humoral and cellular immune responses in a retrospective, cross-sectional study in PLWH on antiretroviral therapy. Among 104 participants, 70.2% had anti-S IgG antibodies and 55.8% had significant neutralizing activity against the Omicron variant in a surrogate virus neutralization test. Only 38.5% were vaccinated (8.76 ± 4.1 months prior), all displaying anti-S IgG, 75% with neutralizing antibodies and anti-S IgA. 29.8% had no SARS-CoV-2 serologic markers and displayed significantly lower CD4 counts and HIV viral load. Severe immunosuppression (present in 12.5% of participants) was linked to lower levels of detectable anti-S IgG (0.0003), anti-S IgA (p < 0.0001) and lack of neutralizing activity against Omicron variant (p < 0.0001). T-cell responses were present in 86.7% of tested participants, even in those lacking serological markers. In PLWH without severe immunosuppression, neutralizing antibodies and T-cell responses persisted for up to 9 months post-infection or vaccination. Advanced immunosuppression led to diminished humoral immunity, but retained cellular immune.

Peer-to-peer (P2P) energy trading is a new data-driven business model currently being trialed within the energy sector. Introducing P2P transactions to an essential service such as energy could have far-reaching implications for individuals and the grid. This paper raises considerations and questions from social, economic/markets and regulatory points of view, that should be understood and addressed by societies and policymakers. It does this by considering under what circumstances it is reasonable to conceptualize P2P energy trading as part of the sharing economy, and drawing parallels to the sharing economy experience in other sectors. In order to reap the full societal benefits, while avoiding considerable risks to infrastructure and individuals, a policy approach promoting dialogue and innovation is necessary. We suggest the regulatory sandbox is the most appropriate tool to achieve this and would help avoid the breakdown of trust between policymakers and platform companies observed in other sectors.

This paper employs the two-step procedure developed by Cheung and Ng (1996) to analyze the causality-in-mean and causality-in-variance between the housing and stock markets of the UK. The empirical findings make two key contributions. First, although previous studies have indicated a one-way causal relation from the housing market to the stock market in the UK, this paper discovered a two-way causal relation between them. Second, a causality-in-variance as well as a causality-in-mean was detected from the housing market to the stock market.

Taeniid infections pose a significant threat to both animal and public health, as certain tapeworms within this group can also infect humans, potentially leading to severe health conditions. Therefore, this calls for preventive and control measures, such as regular deworming of dogs. While the efficacy of deworming has been established in developed countries, there is a paucity of reported deworming practices in rural areas of developing countries, such as Tanzania. This study aims to understand determinants for helminth treatment-seeking behavior towards control of cestodial taeniid infections in rural settings in northern Tanzania. A cross-sectional study was undertaken in agropastoral and pastoral areas of northern Tanzania. Comprehensive data from household surveys in selected sub-villages were collected to ascertain dog ownership, dog deworming practices, and the availability of dewormers. Analytical methods were employed to discern how various determinants influenced deworming practices among dog owners in these communities. Awareness of dewormers specifically formulated for dogs emerged as a pivotal factor affecting dog deworming practices. Dog owners who were informed about appropriate dog dewormers were nearly two times more likely to engage in deworming compared to those with limited knowledge (OR = 1.78, 95% CI 1.77 - 4.18, p < 0.001). A majority 32(51.6%, 95% CI: 38.7-64.3) acknowledged accessing dewormers for livestock within their wards. Praziquantel a potent dewormer on cestodes was least known to most dog owners 2(13%, 95% CI: 2.3-41.6). Praziquantel, a potent and common dewormer effective against cestodial taeniid infections, remains relatively unknown in rural areas of northern Tanzania. Therefore, more awareness on appropriate deworming agents against taeniids in dogs should be raised in rural dog-keeping communities.

The cattle sector is strategic sector for both the economic development and food security of Africa. However, the low availability and quality of forage puts the most vulnerable population in the field at risk. Hybrid forages have proven to be a real alternative for enhancing both the food security and sustainability of the sector. They are the product of genetic improvements and combine the superior traits of different materials. In 1987, the International Center for Tropical Agriculture (CIAT) began this line of research and together with the private sector various ma-terials have been released until today. Despite their beforementioned potential, forage hybrid adoption levels are still low in Africa, which is related to various factors among which the availability of seeds and planting material stands out. This document analyzes the potential markets for new forage hybrids adapted to the environmental conditions of eastern Africa and partially western Africa. Likewise, an estimation of the commercial value of these markets is provided. The results show a potential market of 414,388 ha for new interspecific hybrids of Urochloa and 528,409 ha for potential hybrids of Megathyrsus maximus, with approximate values of 73.5 and 101.1 million dollars, respectively. Ethiopia, Tanzania, and Kenya have a 70% market share for Urochloa, while South Sudan, Ethiopia, and Tanzania have a 67% share for Megathyrsus maximus. The results will help different actors in decision-making, i.e., regarding private sector investments in forage seed commercialization or public sector incentives supporting adoption processes, and thus contribute to increasing food security and sustainability in the region.

Markets from Maputo City have been reported to have poorly hygienic premises and sellers unaware of the safest food handling practices. This study aimed to analyze associations between some lettuce (Lactuca sativa) handling features and the levels of total, fecal coliforms and Escherichia coli. Cabbage was purchased in the 3 markets. Just before the acquisition, the researchers recorded the source of water used to wash it, if they were sold in booths or on the floor, if they were sold mixed with other products, and if the sellers were wearing gloves. The cabbage heads (42) were analyzed through the technique of multiple tubes to estimate the most probable number. Average levels were 14.43 MPN/g for total coliforms, 13.9 MPN/g for fecal coliforms and 12.02 MPN/g for E. coli. The highest levels of contamination seemed associated with the use of well water, the food sold on the floor, the lettuce sold together with other products and the lack of gloves. The mixture of products only showed a major impact on the level of E. coli (p < 0.001). Yet, all levels were within the satisfactory range, according to the guidelines from The International Commission on Microbiological Specifications for Foods (ICMSF).

Poxviridae have been successful pathogens throughout recorded history, infecting humans among a variety of other hosts. Although eradication of the notorious smallpox has been a globally successful healthcare phenomenon, the recent emergence of Monkeypox virus, also belonging to the Orthopoxvirus genus and causing human disease, albeit milder than smallpox, is a cause of significant public health concern. The ongoing outbreak of monkeypox, demonstrating human-human transmission, in previously non-endemic countries, calls for critical need into further research in the areas of viral biology, ecology and epidemiology to better understand, prevent and treat human infections. In the wake of these recent events, it becomes important to revisit poxviral infections, their pathogenesis and ability to cause infection across multiple non-human hosts and leap to a human host. The poxviruses that cause human diseases include Monkeypox virus, Molluscum contagiosum virus and Orf virus. In this review we summarize the current understanding of various poxviruses causing human diseases, provide insights into their replication and pathogenicity, disease progression and symptoms, preventive and treatment options and their importance in shaping modern medicine through application in gene therapy, oncolytic viral therapies for human cancers or as poxvirus vectors for vaccines.

Metabolomics is now considered to be a wide-ranging, sensitive and practical approach to acquire useful information on the composition of a metabolite pool present in any organism, including plants. Investigating metabolomic regulation in plants is essential to understand their adaptation, acclimation and defense response to environmental stresses through the production of numerous metabolites. Moreover, metabolomics can be easily applied for the phenotyping of plants; and thus, it has great potential to be used in molecular breeding and genome editing programs to develop superior next generation crops. This review describes the recent analytical tools and techniques available to study plants metabolome, along with their significance of sample preparation using targeted and non-targeted method. Advanced analytical tools, like gas chromatography-mass spectrometry (GC-MS), liquid chromatography mass-spectroscopy (LC-MS), capillary electrophoresis-mass spectrometry (CE-MS), fourier transform ion cyclotron resonance-mass spectrometry (FTICR-MS) and matrix-assisted laser desorption/ionization (MALDI) have speed up metabolic profiling in plants. Further, we deliver a complete overview of bioinformatics tools and plant metabolome database that can be utilized to advance our knowledge to plant biology.

This paper explores the suppression of ideas within academic scholarship by academics, either by self-suppression or because of the efforts of other academics. Legal, moral, and social issues distinguishing freedom of speech, freedom of inquiry, and academic freedom are reviewed. How these freedoms and protections can come into tension is then explored by an analysis of denunciation mobs who exercise their legal free speech rights to call for punishing scholars who express ideas they disapprove of and condemn. When successful, these efforts, which constitute legally protected speech, will suppress certain ideas. Real-world examples over the past five years of academics who have been sanctioned or terminated for scholarship targeted by a denunciation mob are then explored.

With the interest and attention that the Blockchain and the Distributed Ledger Technologies (DLT) have recently demanded, the technology is advancing at a very high rate. With investors and applications in a wide variety of fields, a lot of funding and efforts are being driven into bringing the technology to everyday use. The community and companies are coming up with new ways to collaborate, which makes the blockchain ecosystem evolve at full tilt. Consequently, this paper’s aim is to review the academic and grey literature and to provide readers with information about the evolution, benefits and challenges of the Public Distributed Ledger Technologies and to discuss the latest solutions, which are being developed for bringing decentralization closer to the mainstream. The paper reviews the Directed Acyclic Graph (DAG) structured distributed ledgers with focus on the Hedera Hashgraph, a novelty DLT bringing a unique consensus algorithm with new use-cases enabled by new cryptoeconomic mechanisms as well as vital services, such as Solidity smart contracts and distributed file storage. Lastly, we are going to explore second-layer network protocols, a major topic for solving the scalability issues, empower Bitcoin and DLTs and also disrupt cryptocurrency exchanges focusing on the Lightning Network.

Metastasis is the leading cause of cancer-related death and drives patient morbidity as well as healthcare costs. For several cancers, breast and prostate in particular, bone is the primary site of metastasis. Efforts to treat bone metastases have been stymied by a lack of models to study the progression and cellular players and signaling pathways driving bone metastasis. In this review, we examine the newly described and classic models of bone metastasis. Through the use of current in vivo, microfluidic and in silico computational models bone metastasis models we may eventually understand how cells escape the primary tumor and how these circulating tumor cells then home to and colonize the bone marrow. Further, future models may uncover how cell enter and escape dormancy to develop into overt metastases. Recreating the metastatic process will lead to the discovery of therapeutic targets for disrupting and treating bone metastasis.

Neisseria meningitidis causes most cases of bacterial meningitis. Meningococcal meningitis is a public health burden to both developed and developing countries throughout the world. There are a number of vaccines (polysaccharide-based, glycoconjugate, protein-based and combined conjugate vaccines) that are approved to target five of the six disease-causing serogroups of the pathogen. Immunization strategies have been effective at helping to decrease the global incidence of meningococcal meningitis. Researchers continue to enhance these efforts through discovery of new antigen targets that may lead to a broadly protective vaccine and development of new methods of homogenous vaccine production. This review describes current meningococcal vaccines and discusses some recent research discoveries that may transform vaccine development against N. meningitidis in the future.

The dawn of quasispecies theory revolutionized our understanding of viral evolution and pathogenesis. This theory conceptualises viruses as dynamic populations of closely related but genetically diverse variants that constantly mutate and adapt to environmental pressures. Quasispecies dynamics govern key aspects of virus-host interactions, such as adaptive evolution, immune evasion, drug resistance, and host tropism. In this article, we discuss the fundamental role of quasispecies theory in elucidating viral fitness landscapes, shaping antiviral strategies, and predicting viral emergence and evolution. We provide a concise overview of the original quasispecies model and its latest advancements, which enable the study of the connection between viral dynamics and the significant genetic diversity exhibited by viruses. We then point out some key features of virus dynamics that need to be incorporated into quasispecies theory. We continue with examples of convergence between theory and real viruses by discussing theoretical results supported by RNA virus data and vice versa. Next, we discuss the need to extend the concept of sequence space beyond the classical hypercube towards more complex, multidimensional connected sequence spaces that we have called ultracubes. Finally, we highlight the necessity of developing multi-scale models to understand how viral evolutionary dynamics within a host can affect epidemiological patterns. We also examine the limitations of quasispecies theory in predicting virus evolution and emergence.

We introduce tools to capture the dynamics of three different pathways, in which the synchronization of human decision making could lead to turbulent periods and contagion phenomena in financial markets. The first pathway is caused when stock market indices, seen as a set of coupled integrate-and-fire oscillators, synchronize in frequency. The integrate-and-fire dynamics happens due to "change blindness", a trait in human decision making where people have the tendency to ignore small changes, but take action when a large change happens. The second pathway happens due to feedback mechanisms between market performance and the use of certain (decoupled) trading strategies. The third pathway occurs through the effects of communication and its impact on human decision making. A model is introduced in which financial market performance has an impact on decision making through communication between people. Conversely, the sentiment created via communication has an impact on financial market performance.

This literature review explores the relationship between energy markets development, environmental concerns, and geopolitics. By presenting a range of sources, we delve into essential themes like carbon emissions, the impact of energy markets deregulation on energy prices, trust in energy-related exchange-traded funds, the correlation between greenhouse gas emissions and global temperature anomalies, and how renewables regulations affect CO2 emissions. We also discuss relevant research on the impact of social and governance (ESG) considerations on financial performance. The geopolitical aspect is thoroughly presented under the energy framework. Topics, such as energy transitions in the Eastern Mediterranean and MENA regions and maritime oil piracy, are discussed. This review highlights the interdependent nature of these subjects and stresses the need for their interdisciplinary examination.

Pearl millet is an important food crop in the arid and semi-arid tropical regions of Africa and Asia. These regions are home to millions of poor smallholder’s households living in harsh agro ecology and reported higher prevalence of malnutrition. Such poor households have few options in terms of food crops, besides the limited markets. Indeed, pearl millet is one of the food crops they continue to grow for their food and nutritional security. Pearl millet is important sources of dietary carbohydrates, energy, protein, and important minerals such as calcium, iron and zinc. Considering inherent high nutritional values and climate resilient nature (drought and heat), demand for pearl millet as food, beside valued for its Stover as a source of livestock fodder, is projected to grow strongly in Asia (India) and Africa (West and Central Africa). Iron (cause anemia) and zinc (cause stunting) deficiencies are widespread and serious public health problems worldwide, including India and Africa. Biofortification is a cost-effective and sustainable agricultural strategy to address this problem. Research on pearl millet has shown that large genetic variability (30-140 mg/kg Fe and 20-90 mg/kg Zn) available in this crop can be effectively utilized to develop high-yielding cultivars with high iron and zinc densities. Both Open –pollinated varieties (Dhanshakti and Chakti) and hybrids (ICMH 1202, ICMH 1203 and ICMH 1301) of pearl millet with high grain yield (>3.5 tons/ha in hybrids) and high levels of iron (70-75 mg/kg) and zinc (35-40 mg/kg) densities have been developed and released. Currently, India growing >70,000 ha of biofortified pearl millet, besides more pipeline hybrids and varieties are under various stage of testing at the national (India) and international (west Africa) trials for possible release. Genomic tools will be an integral part of breeding program particularly for nutritional traits to use diagnostic markers and genomic selection. Clinical studies showed that 200g grains from biofortified cultivar would provide bioavailable Fe to meet full recommended daily allowance (RDA) in children, adult men and 80% of the RDA in women. Till today, no markets to promote biofortified cultivars/grains/products as no incentive price and such products aims to address food and nutritional security challenges simultaneously. The demand is likely to increase only after investment and integration into modern public distribution system, nutritional intervention schemes, private seed and food companies with strong mainstreaming nutritional policies. In the non-traditional regions, this will contribute to livestock and poultry feed industry as spill-over benefits to improve nutrition.

The past few decades have been characterized by a growing body of profit-seeking public service areas with the understanding that profit-seeking organizations will deliver public services more efficiently than government can. These sectors include, but are not limited to, health care, corrections and education. Governments have created quasi markets to attract private providers of services in these sectors, with varying results. Organizational economics has provided the primary explanation for quasi markets, but questions about the sought-for efficiencies actually realized through these markets persist. We integrate resource dependence theory and organizational economics to provide a more comprehensive explanation of the persistence of quasi markets.

The ongoing global pandemic caused by coronavirus disease 2019 (COVID-19) has once again demonstrated the significance of the Coronaviridae family in causing human disease outbreaks. As SARS-CoV-2 was first detected in December 2019, information on its tropism, host range, and clinical presentation in animals is limited. Given the limited information, data from other coronaviruses may be useful to inform scientific inquiry, risk assessment and decision-making. We review the endemic and emerging alpha- and betacoronavirus infections of wildlife, livestock, and companion animals, and provide information on the receptor usage, known hosts, and clinical signs associated with each host for 15 coronaviruses discovered in people and animals. This information can be used to guide implementation of a One Health approach that involves human health, animal health, environmental, and other relevant partners in developing strategies for preparedness, response, and control to current and future coronavirus disease threats.

GCN1 is recognized as a factor that is essential for the activation of GCN2, which is a sensor of amino acid starvation. This function is evolutionarily conserved from yeast to higher eukaryotes. However, recent studies have revealed non-canonical functions of GCN1 that are independent of GCN2, such as its participation in cell proliferation, apoptosis, and the immune response, beyond the borders of species. Although it is known that GCN1 and GCN2 interact with ribosomes to accomplish amino acid starvation sensing, recent studies have reported that GCN1 binds to disomes (i.e. ribosomes that collide each other), thereby regulating both the co-translational quality control and stress response. We propose that GCN1 regulates ribosome-mediated signaling by dynamically changing its partners among RWD domain-possessing proteins via unknown mechanisms. We recently demonstrated that GCN1 is essential for cell proliferation and whole-body energy regulation in mice. However, the manner in which ribosome-initiated signaling via GCN1 is related to various physiological functions warrants clarification. GCN1-mediated mechanisms and its interaction with other quality control and stress response signals should be important for proteostasis during aging and neurodegenerative diseases, and may be targeted for drug development.

Three decades have now passed since the first papers linking climate change to issues in human disease and healthcare. One of the most active topics in this area has been the implication of climate change events, particularly temperature and humidity fluctuations, in the northward spread of vector-borne viruses from more tropical regions into Europe and North America. However, some detailed studies of one such emerging disease, tick-borne encephalitis virus (TBEv), have called the connection into question, concentrating the debate on the investigation of precise mechanisms for the spread of viral disease. More recently, firmer statistical correlations have been made between climate variables, the presence of insect vectors and the prevalence of viral disease, particularly for West Nile Virus (WNV). These insights suggest avenues for mechanistic confirmation of the involvement of climate change in other diseases where the connection remains conjectural.

Membrane remodeling and phospholipid biosynthesis are normally tightly regulated to maintain the shape and function of cells. Indeed, different physiological mechanisms ensure a precise coordination between de novo phospholipid biosynthesis and modulation of membrane morphology. Interestingly, the overproduction of certain membrane proteins hijack these regulation networks, leading to the formation of impressive intracellular membrane structures in both prokaryotic and eukaryotic cells. The proteins triggering an abnormal accumulation of membrane structures inside the cells (or membrane proliferation) share two major common features: 1) they promote the formation of highly curved membrane domains and 2) they lead to an enrichment in anionic, cone-shaped phospholipids (cardiolipin or phosphatidic acid) in the newly formed membranes. Taking into account the available examples of membrane proliferation upon protein overproduction, together with the latest biochemical, biophysical and structural data, we explore the relationship between protein synthesis and membrane biogenesis. We propose a mechanism for the formation of these non-physiological intracellular membranes that shares similarities with natural inner membrane structures found in α-proteobacteria, mitochondria and some viruses-infected cells, pointing towards a conserved feature through evolution. We hope that the information discussed in this review will give a better grasp of the biophysical mechanisms behind physiological and induced intracellular membrane proliferation, and inspire new applications, either for academia (high-yield membrane protein production and nanovesicle production) or industry (biofuel production and vaccine preparation).

We examined the causality between growth opportunities and earnings quality of the emerging economies. We develop an argument that growth opportunities should lead to high-quality earnings of the developing economies against the prior studies from developed economies which posit that growth opportunities result in a lower quality of earnings. The findings revealed that Growth Opportunities had a positive significant effect on Earnings Quality (EQ). Thus, the study concluded that growth opportunities are useful in determining the earnings quality of firms. The study recommends that managers should take advantage of their firms' growth opportunities to provide quality accounting information which will directly provide expanded opportunities for business growth. Though literature confirms that generally, earnings management might not be necessarily bad in business operational practice, managers are advised to avoid extreme aggressiveness in managing earnings as this may culminate in the negative manipulation of accounting information.

Our paper deals with an advanced statistical tool for the volatility prediction problem in financial (crypto) markets. Firstly, we consider the conventional GARCH involved volatility models. We next extend the corresponding GARCH based forecasting and calculate a specific probability associated with the predicted volatility levels. Since the probability evaluation is based on a stochastic model, we next develop an additional data-driven estimation of this probability. The proposed advanced statistical estimation uses real (historical) market data. The obtained theoretic results for the statistical probability of levels are also discussed in the framework of the integrated volatility concept. A possible application of the established probability estimation to the volatility clustering problem is also mentioned. Our paper includes a concrete practical implementation of the resulting (combined) volatility prediction tool and considers a novel module for trading and volatility estimation in crypto markets recently developed by 1ex Trading Board group in collaboration with the GoldenGate Venture. Moreover, we discuss shortly a possible using of the proposed model based and data-driven volatility prediction methodology in the financial risk management.

Hybrid Classical-Quantum computing has already arrived at several commercial quantum computers, offered to researchers and businesses. Here, applications are made to a model of financial options, Statistical Mechanics of Financial Markets (SMFM). These applications were published in many papers since the 1980's. This project only uses Classical (super-)computers to include quantum features of these models. Since 1989, an optimization code, Adaptive Simulated Annealing (ASA), has been to fit parameters in such models. Since 2015, a path-integral algorithm, PATHINT, used previously to accurately describe several systems in several disciplines, has been generalized from 1 dimension to N dimensions, and from classical to quantum systems, qPATHINT. Published papers have described the use of qPATHINT to neocortical interactions and financial options. The classical space by SMFM applies nonlinear nonequilibrium multivariate statistical mechanics to fit parameters in conditional short-time probability distributions, while the quantum space described by qPATHINT deals specifically with quantum systems, e.g., quantum money. This project thereby demonstrates how some hybrid classical-quantum systems may be calculated quite well using only classical (super-)computers.

This paper investigates the dynamic tail dependence risk between BRICS economies and world energy market in the context of the COVID-19 financial crisis of 2020, to determine optimal investment decisions based on risk metrics. For this purpose, the study employs a combination of novel statistical techniques ranging from Markov Switching, GARCH and Vine copula. Using a dataset consisting of daily stock and world crude oil prices; we find high probability of transition between lower and higher volatility regimes. Furthermore, our results based on the C-Vine copula confirm the existence of two types of tail dependence: - symmetric tail dependence between South Africa and China; South Africa and Russia; and lower tail dependence between South Africa and India; South Africa and Brazil; South Africa and Oil. For the purpose of diversification in these markets, we formulate an asset allocation problem using C-vine copula-based returns and optimize it using Particle Swarm algorithm with a rebalancing strategy. The results show an inverse relationship between the risk contribution and asset allocation of South Africa and oil market supporting the existence of lower tail dependence between them. This suggests that when South African stocks are in distress, investors tend to shift their holdings in oil market. Similar results are found between China and oil. In the upper tail, South African asset allocation is found to have an inverse relationship with that of Brazil, Russia and India suggesting that these three markets might be good investment destinations when things are not good in South Africa and vice-versa.

This paper examines the dynamic dependence structure of crude oil and East Asian stock markets at multiple frequencies using wavelet and copulas. We also investigate risk management implications and diversification benefits of oil-stock portfolios by calculating and comparing risk and tail risk hedging performance. Our results provide strong evidence of time-varying dependence and asymmetric tail dependence between crude oil and East Asian stock markets at different frequencies. The level and fluctuation of their dependencies increase as time scale increases. Furthermore, we find the time-varying hedging benefits differ at investment horizons and reduced over the long run.

It is well known that that there is an intrinsic link between the financial and energy sectors, which can be analyzed through their spillover effects, which are measures of how the shocks to returns in different assets affect each other’s subsequent volatility in both spot and futures markets. Financial derivatives, which are not only highly representative of the underlying indices, but can also be traded on both the spot and futures markets, include Exchange Traded Funds (ETFs), a tradable spot index whose aim is to replicate the return of an underlying benchmark index. When ETF futures are not available to examine spillover effects, “generated regressors” are useful for constructing both Financial ETF futures and Energy ETF futures. The purpose of the paper is to investigate the co-volatility spillovers within and across the US energy and financial sectors in both spot and futures markets, by using “generated regressors” and a multivariate conditional volatility model, namely Diagonal BEKK. The daily data used are from 1998/12/23 to 2016/4/22. The data set is analyzed in its entirety, and are also subdivided into three distinct subsets. The empirical results show there is a significant relationship between the Financial ETF and Energy ETF in the spot and futures markets. Therefore, financial and energy ETFs are suitable for constructing a financial portfolio from an optimal risk management perspective, and also for dynamic hedging purposes.

The Automatic Dependent Surveillance-Broadcast (ADS-B) protocol stands as a cornerstone in modern aviation surveillance and Aircraft Traffic Control systems, integral to the NextGen project initiated by the US Department of Federal Aviation Administration (FAA) in 2005. ADS-B utilizes data links to autonomously broadcast Aircraft navigational and identification information to augment air transportation system capacity and safety. However, concerns regarding the protocol's security persist, particularly as its adoption expands. The unencrypted nature of transmitted aircraft data and the absence of robust authentication mechanisms render the protocol susceptible to exploitation by malicious actors. In this study, we comprehensively reviewed existing research in the field, identifying a crucial gap necessitating a holistic survey. Prior surveys have predominantly focused on specific aspects, such as vulnerabilities, attacks, or critiques of existing solutions. Our survey addresses this gap by thoroughly exploring the aviation system, providing readers with a nuanced understanding of ADS-B security. Utilizing a detailed SWOT analysis diagram, our paper delves into the vulnerabilities inherent in the ADS-B protocol outlines potential threats and scrutinizes various attack scenarios. We systematically categorize and analyze existing security solutions, considering cost-effectiveness, scalability, implementation complexity, and coverage against diverse attack vectors. Furthermore, we critically evaluate these solutions, elucidate ADS-B security requirements, discuss current challenges, and propose future research directions. This survey serves as a comprehensive resource for researchers and practitioners alike, shedding light on the multifaceted landscape of ADS-B security and paving the way for enhanced aviation system resilience in the face of evolving cybersecurity threats.

In Southeast Asia, the incidence of Leukemia, a malignant blood cancer originating from hema-topoietic progenitor cells, is on the rise, marked by a concerning 54% mortality rate. This study focuses on enhancing early-stage prediction to improve patient recovery prospects significantly. Leveraging Machine Learning and Data Science, we employ protein sequential data from frequently mutated genes such as BCL2, HSP90, PARP, and RB to predict Chronic Myeloid Leukemia (CML). Our approach relies on robust feature extraction techniques, namely Di-peptide Composition (DPC), Amino Acid Composition (AAC), and Pseudo amino acid composition (Pse-AAC), with prior attention to addressing outliers and validating feature selection through the Pearson Corre-lation Coefficient. Data augmentation ensures a well-rounded dataset for analysis. Employing a range of Machine Learning models, including Support Vector Machine (SVM), XGBoost, Random Forest (RF), K Nearest Neighbor (KNN), Decision Tree (DT), and Logistic Regression (LR), we achieve accuracy rates spanning from 66% to 94%. These classifiers undergo comprehensive as-sessment using performance metrics such as accuracy, sensitivity, specificity, F1-score, and the confusion matrix. Our proposed solution, encompassing a user-friendly web application dashboard, presents an invaluable tool for early CML diagnosis with profound implications for practitioners, offering a deploy-able asset within healthcare institutions and hospitals.

This study delves into the complex interplay among attachment styles, hypersexuality, and sexual and couple satisfaction in emerging adults within the Mexican population. Despite established correlations between anxious and avoidant attachment styles and emotion dysregulation and hypersexuality, the roles of sexual and couple satisfaction in this context have remained relatively unexplored. This study aims to fill this gap by examining how these variables interact and contribute to psychological distress and sexual risk behaviors. Data analysis on 214 emerging adults, utilizing the Experiences in Close Relationships scale, Couple Satisfaction Index, Sexual Satisfaction Scale, SCL-90, and SAST-R, revealed a significant association between risky sexual behaviors—such as a higher number of sexual partners, drug and alcohol use during sexual activity, and lack of sexual protection—and heightened levels of hypersexuality. Notably, individuals with hypersexual tendencies reported diminished levels of sexual satisfaction but elevated levels of couple satisfaction. Avoidant attachment emerged as the attachment style most strongly correlated with early sexual experiences. Moreover, the abbreviated versions of the Couple Satisfaction Index (CSI-4) and the Experiences in Close Relationships scale (ECR-8) exhibited commendable psychometric properties, enhancing their applicability for research and clinical purposes. This research furnishes valuable insights into the complex dynamics among attachment styles, hypersexuality, and couple and sexual satisfaction, emphasizing the need for targeted interventions to foster healthier sexual and relationship behaviors among emerging adults.

Currently, in a context of biorefinery and bioeconomy, lignocellulosic biomass is increasingly used to produce biofuels, biochemicals and value-added products. Microwaves and ultrasounds are emerging techniques that enable efficient and environmentally more sustainable routes in the transformation of lignocellulosic biomass. This review is divided into four parts. In Part I, the theoretical foundations of microwaves and ultrasounds are revisited. Dielectric constants for biomass, factors that influence pre-treatment, are some of the subjects addressed. In Part II, the effects that these technologies have on lignocellulosic biomass (on the size and surface area of the particle; on the content of lignin, hemicellulose and cellulose; on the crystallinity index of cellulose; on the effect of solubilization of organic matter; on hydrolysis and reduction of sugars) are analyzed. In Part III, emphasis is given on how microwaves and ultrasounds can contribute to the achievement of value-added products. In this context, several examples of liquefactions and extractions are presented. Part IV describes examples of sonocatalysis of lignocellulosic biomass to obtain value-added products such as for instancethe production of furfural, which is significantly reduced by ultrasound.

World Health Organization (WHO) has announced the monkeypox (MPX) epidemic a global public health emergency due to its re-emergence, remarkable increase in the number of MPX cases worldwide, and its potential spread. This paper introduces the symptoms, complications, and features of MPX; its transmission, diagnosis and testing, vaccines and treatment; MPX and sexually transmitted diseases, especially the human immunodeficiency virus (HIV); possible natural hosts or reservoirs of the monkeypox virus (MPXV). A useful tool for MPX and surgical safety recommendations are presented. The challenges in fighting the MPX epidemic, One Health strategy, and future research are discussed.

Alzheimer’s and Parkinson's diseases (AD and PD) are amongst top of the prevalent neurodegenerative disease. One-third of PD patients are diagnosed with dementia, a pre-symptom of AD, but the underlying mechanism is elusive. Amyloid beta (Aβ) and α-synuclein are two of the most investigated proteins, whose pathological aggregation and spreading are crucial to the pathogenesis of AD and PD, respectively. Transcriptomic studies of the mammalian central nervous system shed light on gene expression profiles at molecular levels, regarding the complexity of neuronal morphologies and electrophysiological inputs/outputs. In the last decade, the booming of the single-cell RNA sequencing technique helped to understand gene expression patterns, alternative splicing, novel transcripts, and signal pathways in the nervous system at single-cell levels, providing insight for molecular taxonomy and mechanistic targets of the degenerative nervous system. Here, we re-visited the cell-cell transmission mechanisms of Aβ and α-synuclein in medi-ating disease propagation, and summarized recent single-cell transcriptome sequencing from different perspectives and discussed its understanding of neurodegenerative diseases.

Deforestation and associated changing landscapes are major components of environmental changes, with important implications for ecosystem functioning and biodiversity conservation. Tropical forests are hot spots of biodiversity and provide multiple goods and ecosystem services which benefit people in many ways Forest also play an important role in health-related legends, myths, and fairy tales from all over the world, and are important sources of new potential emerging microbial threats to human. Although plausibly numerous abundant microbial forms with a forest origin may exist, our systematic literature review shows that forest-derived infection studies are relatively unexplored, and both taxonomically and geographically biased. Since biodiversity has been associated with emergence of novel infectious diseases at macro-scale, we describe the main biogeographical patterns in the emerging infection-biodiversity-forest loss nexus. Then, we illustrate four fine-scale case studies to decipher the underlying processes of increased infection risk in changing forest clearing landscapes. Finally, we identify scientific challenges and regional management measures required to mitigate these important new emerging threats.

With Internet of Things (IoT) gaining presence throughout different industries a lot of new technologies have been introduced to support this undertaking. Implications on one such technology, wireless systems allowed for the use of different communication methods to achieve the goal of transferring data reliably, with more cost efficiency and over longer distances. Anywhere from a single house with only a few IoT devices such as a smart light bulb or a smart thermostat connected to the network, all the way to a complex system that can control power grids throughout countries, IoT has been becoming a necessity in everyday lives. This paper presents an overview of the devices, systems and wireless technologies used in different IoT architectures (Healthcare, Vehicular Networks, Mining, Learning, Energy, Smart Cities, Behaviors and Decision Making), their upbringings and challenges to this date and some foreseen in the future.

We analyze the upside and downside risk connectedness among international stock markets. We characterize the connectedness among international stock returns using the Diebold and Yilmaz spillover index approach and compute the upside and downside value-at-risk. We document that the connectedness level of the downside risk is higher than that of the upside risk and that stock markets are more sensitive when the stock market declines. We also find that specific periods (e.g., the global financial crisis, the European debt crisis, and the COVID-19 turmoil) intensify the spillover effects across international stock markets. Our results demonstrate that the EU, Ger-many, and the US acted as net transmitters of dynamic connectedness; however, Japan (JP), China (CH), and India (IN) acted as net receivers of dynamic connectedness during the sample period. These findings provide significant new information to policymakers and market participants.

We study in this paper the time evolution of stock markets using a statistical physics approach. We consider an ensemble of agents who sell or buy a good according to several factors acting on them: the majority of the neighbors, the market ambiance, the variation of the price and some specific measure applied at a given time. Each agent is represented by a spin having a number of discrete states q or continuous states, describing the tendency of the agent for buying or selling. The market ambiance is represented by a parameter T which plays the role of the temperature in physics: low T corresponds to a calm market, high T to a turbulent one. We show that there is a critical value of T, say T_c, where strong fluctuations between individual states lead to a disordered situation in which there is no majority: the numbers of sellers and buyers are equal, namely the market clearing. The specific measure, by the government or by economic organisms, is parameterized by $H$ applied on the market at the time t₁ and removed at the time t₂. We have used Monte Carlo simulations to study the time evolution of the price as functions of those parameters. In particular we show that the price strongly fluctuates near T_c and there exists a critical value H_c above which the boosting effect remains after H is removed. Our model replicates the stylized facts in finance (time-independent price variation), volatility clustering (time-dependent dynamics) and persistent effect of a temporary shock. The second party of the paper deals with the price variation using a time-dependent mean-field theory. By supposing that the sellers and the buyers belong to two distinct communities with their characteristics different in both intra-group and inter-group interactions, we find the price oscillation with time. Results are shown and discussed.

We examine the use of deep learning (neural networks) to predict the movement of the S&P 500 Index using past returns of all the stocks in the index. Our analysis finds that the future direction of the S&P 500 index can be weakly predicted by the prior movements of the underlying stocks in the index. Decomposition of the prediction error indicates that most of the lack of predictability comes from randomness and only a little from nonstationarity. We believe this is the first test of S&P500 market efficiency that uses a very large information set, and it extends the domain of weak-form market efficiency tests.

This study aimed to assess the microbiological quality of set of fruits and vegetables sold on stalls in the streets and open markets of Dakar, the capital city of Senegal. Samples purchased in seven sites were analyzed to isolate E. coli, Salmonella spp. and Vibrio spp. Some primary samples with a positive microbiological culture were subjected to DNA extraction and subsequent metagenomic analysis. A total of 240 fresh fruit and vegetable samples were collected including lettuce (n=40), tomatoes (n=40), mango slices (n=40), onion slices (n=40), mint leaves (n=40), strawberries and grapes (respectively n=20). Of these samples, 50.83% (122/240) and 60.41% (145/240) was contaminated with Escherichia coli and Vibrio spp. respectively, while Salmonella was not isolated in any product. Levels of contamination with both E. coli (1.3 103 to 6 106 CFU/g) and Vibrio spp. (4.55 102 to 8.73 106 CFU/g) were significantly above the thresholds acceptable for human consumption. The most contaminated samples were lettuce with a prevalence of 98% (39/40) for E. coli and for Vibrio spp. followed by mint leaves with 100% (40/40) and 93% (37/40) of the samples containing for E. coli and Vibrio spp. respectively. Out of 46 samples sequenced, metagenomic analysis revealed high contamination rates for E. coli, Vibrio spp. and Salmonella spp. with 100%, 67.39% and 93.47% of prevalence respectively. On the other hand, the alpha diversity analysis shows a high bacterial diversity in lettuce and mint leaf samples while beta diversity analysis highlighted the presence of two major clusters. Our results stress the need of a surveillance system that extends this investigation to a national scale while increasing the number of sampling sites and products analyzed.

Uncertain price movement in staple food commodities puts agrarian economies at risk if not monitored and managed consistently. Hence, an attempt has been made to analyze the price behavior and integration across major wholesale and retail markets for rice and wheat in India. Monthly data (July 2000 to June 2022) on prices viz., wholesale and retail were sourced from the Food and Agriculture Organization and analyzed using growth rate, instability index, seasonal price index, Johansen’s test on cointegration, Granger causality test, and impulse response function. Findings indicated strong evidence of price dynamics in the selected markets in terms of spatial and temporal variation, clear-cut seasonality linking to production, and price divergence between wholesale and retail markets. Johansen’s test indicated a strong integration between wholesale and retail markets exhibiting unidirectional-, bidirectional- and no-causality. Impulse response analysis revealed that the selected wheat and rice markets are efficient in terms of ‘price discovery’ which takes place initially in the wholesale market, and then transmitted to the retail market. The study advocates decision-making information to the producers, traders, and consumers who have a potential interest in getting advantage of the price movement. It is concluded that strengthening market intelligence and reducing the distortion in markets will improve the existing overall performance.

Socially Responsible Investments, also referred to as ethical or sustainable investments, have experienced rapid global growth in recent years. They represent an investment approach that incorporates social, environmental, and ethical considerations into decision-making processes. Consequently, the significance of SRIs has captured the attention of academics, prompting inquiries into the impact of integrating social criteria on portfolio performance. The primary objective of this work was to conduct a comparative study of the performance between socially responsible and non-socially responsible investment funds, using funds domiciled in Portugal and Spain. Various multi-factor models, including the three-factor model of Fama and French (1992), the four-factor model of Carhart (1997), and the five-factor model of Fama and French (2015), were employed to assess performance. The sample comprised 125 investment funds, with 43 identified as socially responsible and 82 as non-socially responsible. The study's findings indicate that there are no significant differences between socially responsible funds and their conventional counterparts. The majority of funds experience performance alterations during periods of crisis compared to crisis-free periods. Additionally, when comparing non-conditional models with conditional models, an improvement in the explanatory power of the latter is observed. This suggests that the inclusion of the dummy variable enhances the quality of fit for the models.

Finnish government's carbon neutrality goal by 2035 requires integration of renewable energy sources into the power grid. Considering the stochasticity of these resources, additional sources of flexibility are necessary to balance supply and demand in the power grid. District heating network (DHN) operators in Finland plan to shut down fossil-fuel-based combined heat and power plants and electrify heating systems by deploying heat pumps (HPs) and electric boilers. Techno-economic analysis and optimal operation of DHN-connected HPs and electric boilers in providing ancillary balancing services considering the 15-minute granularity in the balancing markets in Finland were investigated. The objective was to maximize the potential revenue for DHN operators gained from the day-ahead electricity market and frequency containment reserve (FCR) balancing markets. Three inter-connected DHNs in the Helsinki metropolitan area were optimized considering the reference year 2019 and each operator's decarbonization strategies for 2025. HPs could gain the highest profit from FCR-D up-regulation market, while the electric boiler may gain considerable profits from FCR-D down-regulation market. Compared to other balancing markets studied, the FCR-N market had a limited profit margin. Sensitivity analysis indicated that spot electricity prices and CO2 emission allowance prices have a significant impact on the profit from balancing markets.

We have implemented quantum modeling mainly based on Bohmian Mechanics to study time series that contain strong coupling between their events. Compared to time series with normal densities, such time series are associated with rare events. Hence, employing Gaussian statistics drastically underestimates the occurrence of their rare events. The central objective of this study is to investigate the effects of rare events in the probability densities of time series from the point of view of quantum measurements. For this purpose, we first model the non-Gaussian behavior of time series using the multifractal random walk (MRW) approach. Then, we examine the role of the key parameter of MRW, λ, which controls the degree of non-Gaussianity, in quantum potentials derived for time series. Our Bohmian quantum analysis shows that the derived potential takes some negative values in high frequencies (its mean values), then substantially increases, and the value drops again for rare events. Thus, rare events can generate a potential barrier in the high-frequency region of the quantum potential, and the effect of such a barrier becomes prominent when the system transverses it. Finally, as an example of applying quantum potential beyond the microscopic world, we compute quantum potentials for the S&amp;P financial market time series to verify the presence of rare events in the non-Gaussian densities and demonstrate deviation from the Gaussian case.

COVID-19, a global health crisis, occurred unexpectedly and has led to global transformation over all countries in the world today. While COVID-19 had claimed lives and led to an economic crisis, the impacts on the financial market cannot be overemphasized. Considering the previous financial crisis, which occurred due to poor regulations and unchecked misconduct by financial market stakeholders, COVID-19 is the first to mention an indirect crisis that almost has no direct relationship to the financial system. Thus, this paper explores the impacts of COVID-19 from a stochastic approach on Local markets by applying the GARCH model to measure the level of volatility of two (2) US stock indexes (NASDAQ and NYSE) and a Europe index (EURONEXT). Our results show that volatility existed before COVID-19, but the volatility rate increased after COVID-19, possibly due to the COVID-19 shock. We also explore the Markov-Switching Dynamic Regression (MSDR) model to corroborate our findings. We validated that there is a very high persistent volatility for all the considered local markets at the early stage COVID-19 period.

Bacteriotherapy is emerging as a strategic and effective approach to treat infections by providing putatively harmless bacteria (i.e., probiotics) as antagonists to pathogens. A proper delivery of probiotics or their metabolites (i.e., post-biotics) can avail itself of biomaterial encapsulation by innovative manufacturing technologies. This review paper aims at providing the most recent biomaterial-assisted strategies proposed to treat infections or disbiosis using bacteriotherapy. We revised the encapsulation processes across multiscale biomaterial approaches, which could be ideal to target different tissues and suit diverse therapeutic opportunities. Hydrogels, and in particular polysaccharides, are the focus of this review, as they have been reported to better sustain vitality of the live cells incorporated. Specifically, the approaches for fabricating hydrogel-based devices with increasing dimensionality (D), namely, 0D (i.e., particles), 1D (i.e., fibers), 2D (i.e., fiber meshes), and 3D (i.e., scaffolds) endowed with probiotics, were detailed by describing their advantages and challenges, along with a future overlook in the field. Electrospinning, electrospray and 3D bioprinting were investigated as new biofabrication methods for probiotic encapsulation within multidimensional matrices. Finally, examples of biomaterial-based systems for cell and post-biotic release were reported.

A recent report in PNAS that Candidatus Nanohaloarchaeum antarcticus requires haloarchaeon Halorubrum lacusprofundi for growth expands the list of known symbiotic or parasitic associations between the members of DPANN archaea, which are relatively small cells with reduced genomes and limited metabolic capacity, and free-living archaea. In line with previous studies addressing the enigmatic mechanism(s) for the transfer of metabolites from Ignicoccus hospitalis to Nanoarchaeum equitans, this new study presents additional evidence supporting a direct cytoplasmic connection facilitated by the fusion of parasite’s membrane with that of its host. Here I show that this novel mechanism for accessing the host resources by a membrane fusing mechanism, which eliminates the need for sophisticated multivalent transport systems, is fundamentally similar to that employed by several viral lineages. These new findings support an evolutionary model on the origin of incipient viral lineages from parasitic cellular lineages that started their parasitic life cycle by fusing with their host cells.

In the rapidly evolving landscape of wireless communication, each successive generation of networks has achieved significant technological leaps, profoundly transforming the way we connect and interact. From the analog simplicity of 1G to the digital prowess of 5G, the journey of mobile networks has been marked by constant innovation and escalating demands for faster, more reliable, and more efficient communication systems. As 5G becomes a global reality, laying the foundation for an interconnected world, the quest for even more advanced networks leads us to the threshold of the sixth-generation (6G) era. This paper presents a hierarchical exploration of 6G networks, poised at the forefront of the next revolution in mobile wireless technology. This study delves into the technological advancements that underpin the need for 6G, examining its key features, benefits, and the key enabling technologies. We dissect the intricacies of cutting-edge innovations like terahertz communication, ultra-massive MIMO, artificial intelligence (AI), machine learning, quantum communication, and re-configurable intelligent surfaces. Through a meticulous analysis, we evaluate the strengths, weaknesses, and state-of-the-art research in these areas, offering a wider view of the current progress and potential applications of 6G networks. Central to our discussion is the transformative role of AI in shaping the future of 6G networks. By integrating AI and machine learning, 6G networks are expected to offer unprecedented capabilities, from enhanced mobile broadband to groundbreaking applications in areas like smart cities and autonomous systems. This integration heralds a new era of intelligent, self-optimizing networks that promise to redefine the parameters of connectivity and digital interaction. This paper also addresses critical challenges in the deployment of 6G, from technological hurdles to regulatory concerns, providing a holistic assessment of potential barriers. By highlighting the interplay between 6G and AI technologies, this study not only maps out the current landscape but also lights the path forward in this rapidly evolving domain. This paper aims to be a cornerstone resource, providing essential insights, addressing unresolved research questions, and stimulating further investigation into the multifaceted realm of 6G networks. By highlighting the synergy between 6G and AI(Artificial Intelligence) technologies, we aim to illuminate the path forward in this rapidly evolving field.

Futures markets are mainly used as a tool for price discovery and for risk management on the spot markets and enable diversification for international portfolio investments. With this study we aim (1) to investigate the causality relationship between futures markets and spot market and (2) to examine the causality relationship between futures markets and spot markets in different countries. We are interested in both the futures markets - spot market relations and the interactions between the markets at international level. For variables we used the the BIST30 spot index and BIST30 futures contract representing the Borsa Istanbul market and the Dow-Jones 30 index and Dow-Jones 30 futures contract, which are the most important indices representing the US markets. Daily closing price data for the period between 2nd January, 2009 and 18th June, 2018 were analyzed using correlation, unit root test, causality test and regression equations. The results of the study show that the futures markets continue their price discovery role for both the spot markets and futures markets and are influential on other futures and spot markets at international level. These findings are important for investors wanting to invest in Turkey and in similarly considered emerging market economies. It will help investors take informed decisions by providing them with a more efficient price estimations utilizing the futures markets.

This paper aims to measure the effects of delisting on stock returns for Vietnam stock market. The study employs a sample of 118 stocks that were compulsorily delisted from the market between January 2011 and December 2021. Using an event study methodology, the empirical findings confirm that the delisting has negative effects on stock returns in the Vietnam stock market. Specifically, results derived from tests show that the average abnormal return of delisted stocks continuously declines during three trading days following the announcement of delisting. Moreover, it is found that the differences in cumulative abnormal returns between post-delisting and pre-delisting periods are significantly negative for all tracking periods. Apart from the negative effect of delisting on stock abnormal returns, we also find that the impact of delisting on stock returns for smaller companies is greater than bigger companies.

The phenomenon of Airbnb has significantly permeated the urban housing markets of major U.S. cities. This qualitative analysis meticulously examines its wide-ranging impacts, exploring Airbnb’s operational model, regulatory challenges, and its influence on housing availability, prices, and neighborhood dynamics. The research aggregates insights from various sources, including academic articles, web-based resources, and case studies, providing a nuanced understanding of the intricate balance cities are endeavoring to achieve between the economic opportunities Airbnb presents and the potential strains it places on local housing markets. The paper also scrutinizes the evolving policy frameworks and their effectiveness in mitigating the impacts of short-term rental platforms. Furthermore, it deliberates on future trends in the short-term rental market and the wider implications for the sharing economy. Concluding, the paper acknowledges the limitations of the qualitative method and suggests areas for future in-depth research, aiming to offer insightful contributions to policymakers, researchers, and urban planners engaged in urban housing discourse in the United States.

The health emergency caused by Covid 19 highlighted the food dependency of many cities in the world and Cali and the cities of Valle del Cauca were no exception as they depended on food flows from other regions and countries, so these Entities are part of the corporate food system, to which their rulers delegated food security for their inhabitants. In the same territory and for the last nine years, a second-level organization has been consolidating that brings together 14 peasant agroecological markets and that despite strict confinement measures was able to continue supplying food to consumers in the municipalities of influence that have supported this initiative. The foregoing leads to think that said organization can become a platform for an agroecological food system of regional scope, for which the characteristics that said system should have according to a model based on the principles of agroecology were evaluated.

We examine the profitability of the momentum and contrarian strategies in three South Asian markets i.e. Bangladesh, India and Pakistan. We also analyze, whether credit risk influences momentum and contrarian return for these markets from 2008 to 2014. We use Distance-to-default (DD) of Kealhofer, McQuown and Vasicek (KMV) model as a measure of credit risk. We calculate the credit risk and form the momentum and contrarian strategies of the firms on the basis of high, medium and low risk. We find that in all three markets, the momentum and contrarian returns are significant for medium and high credit risk portfolios and no momentum and contrarian returns for low credit risk portfolios.

Chile faces a serious crisis on housing affordability given that most of the population is unable to secure a house. While housing prices between 2008 increased by 70.96%, wages only increased by 20.43%. This article presents the analysis of the housing prices configuration for the main district in the country: Santiago Centro. The assessment focuses on verticalised housing buildings constructed between 2015 and 2019. The article develops an exploratory study on the price of housing in the city of Santiago de Chile, to generate a diagnosis to identify the role played by expectations of profitability when configuring that price. Based on the information generated, we seek to contribute to the discussion on public policies that advance towards the development of affordable housing for households in central communes with high urban value, as is the case of Santiago Centro. We hypothesize that profitability expectations of real estate developers play a key role in the housing prices and an adjustment in the profit ratios may increase the affordability keeping the housing market over profitable rates. The research is relevant due to the lack of data transparency in the Chilean housing market, where access to investment costs, land values, yields and house price formation are not part of the public discussion, even though this implies that many households are facing severe difficulties in paying for access to decent housing.

The unprecedented progress in Artificial Intelligence (AI), particularly in deep learning algorithms with ubiquitous internet connected smart devices, has created a high demand for AI computing on the edge devices. This review studied commercially available edge processors, and the processors that are still in industrial research stages. We categorized state-of-the-art edge processors based on the underlying architecture, such as dataflow, neuromorphic, and Processing in-Memory (PIM) architecture. The processors are analyzed based on their performance, chip area, energy efficiency, and application domains. The supported programming frameworks, model compression, data precision, and the CMOS fabrication process technology are discussed. Currently, most of the commercial edge processors utilize dataflow architectures. However, emerging non-von Neumann computing architectures have attracted the industry in recent years. Neuromorphic processors are highly efficient for performing computation with fewer synaptic operations, and several neuromorphic processors offer online training for secured and personalized AI applications. This review found that the PIM processors show significant energy efficiency and consume less power compared to dataflow and neuromorphic processors. The future direction of the industry would be to implement state-of-the-art deep learning algorithms in emerging non-Von Neumann computing paradigms for low power computing on the edge devices.

Lassa fever, commonly known as Lassa hemorrhagic fever (LHF), is one of the progressive illnesses invading a large population of two to three million individuals in West Africa. The infection transmitted through the rodents severely impacts the local population and the medical professionals in surrounding areas, which were also the primary target of LHF. In epidemic areas, Lassa fever causes a public health threat since it poses a significant morbidity and fatality Case rate (CFR) ≥ 50%). The disease is widespread in West Africa and has developed into one of the most common and life-threatening viral hemorrhagic fevers. Monitoring and preventing persistent disease outbreaks has been challenging in affected regions due to insufficient healthcare facilities, diagnostics labs, care centers, and low socioeconomic conditions. An absence of public awareness and the emergence of an ecological niche is advantageous for the survival and multiplication of the mouse (Mastomys natalensis) inhabiting the Lassa virus serving as the disease's natural host and reservoir. The current review focuses on early diagnosis and appropriate treatment, highlighting the immediate requirement of clinically approved vaccines for LHF, causing preventative and control actions more difficult in the present era.

Epithelial-mesenchymal transition (EMT) is a cellular biological process involved in migration of primary cancer cells to secondary sites facilitating metastasis. Besides, EMT also confers properties such as stemness, drug resistance and immune evasion which can aid a successful colonization at the distant site. EMT is not a binary process; recent evidence suggests that cells in partial EMT or hybrid E/M phenotype(s) can have enhanced stemness and drug resistance as compared to those undergoing a complete EMT. Moreover, partial EMT enables collective migration of cells as clusters of circulating tumor cells or emboli, further endorsing that cells in hybrid E/M phenotypes may be the ‘fittest’ for metastasis. Here, we review mechanisms and implications of hybrid E/M phenotypes, including their reported association with hypoxia. Hypoxia-driven activation of HIF-1α can drive EMT. In addition, cyclic hypoxia, as compared to acute or chronic hypoxia, shows the highest levels of active HIF-1α and can augment cancer aggressiveness to a greater extent, including enriching for a partial EMT phenotype. We also discuss how metastasis is influenced by hypoxia, partial EMT and collective cell migration, and call for a better understanding of interconnections among these mechanisms. We discuss the known regulators of hypoxia, hybrid EMT and collective cell migration and highlight the gaps which needs to be filled for connecting these three axes which will increase our understanding of dynamics of metastasis and help control it more effectively.

Rho guanosine triphosphatases (GTPases) are key regulators in a number of cellular functions, including actin cytoskeleton remodeling and vesicle traffic. Traditionally, Rho GTPases are studied because of their function in cell migration and cancer, while their roles in metabolism are less documented. However, emerging evidence implicates Rho GTPases as regulators of processes of crucial importance for maintaining metabolic homeostasis. Thus, the time is now ripe for reviewing Rho GTPases in the context of metabolic health. Rho GTPase-mediated key processes include the release of insulin from pancreatic β-cells, glucose uptake into skeletal muscle and adipose tissue, and muscle mass regulation. Through the current review, we cast light on the important role of Rho GTPases in skeletal muscle, adipose tissue, and the pancreas and mechanisms by which Rho GTPases act to regulate glucose metabolism in health and disease. We also describe challenges and goals for future research.