Search Results (30)

The many limitations of implementing anticancer strategies under the term “precision oncology” have been extensively discussed. While some authors propose promising future directions, others are less optimistic and use phrases such as illusion, hype, and false hypotheses. The reality is revealed by practicing clinicians and cancer patients in various online publications, one of which has stated that “in the quest for the next cancer cure, few researchers bother to look back at the graveyard of failed medicines to figure out what went wrong”. The message is clear: Novel therapeutic strategies with catchy names (e.g., synthetic “lethality”) have not fulfilled their promises despite decades of extensive research and clinical trials. The main purpose of this review is to discuss key challenges in solid tumor therapy that surprisingly continue to be overlooked by the Nomenclature Committee on Cell Death (NCCD) and numerous other authors. These challenges include: The impact of chemotherapy-induced genome chaos (e.g., multinucleation) on resistance and relapse, oncogenic function of caspase 3, cancer cell anastasis (recovery from late stages of apoptosis), and pitfalls of ubiquitously used preclinical chemosensitivity assays (e.g., cell “viability” and tumor growth delay studies in live animals) that score such pro-survival responses as “lethal” events. The studies outlined herein underscore the need for new directions in the management of solid tumors. Full article

Large-scale hydrological modeling is an emerging approach in river hydrology, especially in regions with limited available data. This research focuses on evaluating the performance of two well-known large-scale hydrological models, namely E-HYPE and LISFLOOD, for the five transboundary rivers of Greece. For this purpose, discharge time series at the rivers’ outlets from both models are compared with observed datasets wherever possible. The comparison is conducted using well-established statistical measures, namely, coefficient of determination, Percent Bias, Nash–Sutcliffe Efficiency, Root-Mean-Square Error, and Kling–Gupta Efficiency. Subsequently, the hydrological models’ time series are bias corrected through scaling factor, linear regression, delta change, and quantile mapping methods, respectively. The outputs are then re-evaluated against observations using the same statistical measures. The results demonstrate that neither of the large-scale hydrological models consistently outperformed the other, as one model performed better in some of the basins while the other excelled in the remaining cases. The bias-correction process identifies linear regression and quantile mapping as the most suitable methods for the case study basins. Additionally, the research assesses the influence of upstream waters on the rivers’ water budget. The research highlights the significance of large-scale models in transboundary hydrology, presents a methodological approach for their applicability in any river basin on a global scale, and underscores the usefulness of the outputs in cooperative management of international waters. Full article

The story of the discovery of hepatitis E originated in the late 1970s with my extreme belief that there was a hidden saga in the relationship between jaundice and pregnancy in developing countries and the opportunity for a massive epidemic of viral hepatitis, which hit the Gulmarg Kashmir region in November 1978. Based on data collected from a door-to-door survey, the existence of a new disease, epidemic non-A, non-B hepatitis, caused by a hitherto unknown hepatitis virus, was announced. This news was received by the world community with hype and skepticism. In the early 1980s, the world watched in awe as an extreme example of human self-experimentation led to the identification of VLP. In 1990, a cDNA clone from the virus responsible for epidemic non-A, non-B hepatitis was isolated. Over the years, we traversed three eras of ambiguity, hope, and hype of hepatitis E research and conducted several seminal studies to understand the biology of HEV and manifestations of hepatitis E. Many milestones have been reached on the long and winding road of hepatitis E research to understand the structure, biology, and diversity of the agent, changing the behavior of the pathogen in developed countries, and the discovery of a highly effective vaccine. Full article

Hydrogenases catalyze the simple yet important redox reaction between protons and electrons and H₂, thus mediating symbiotic interactions. The contribution of hydrogenase to this symbiosis and anti-oxidative damage was investigated using the M. huakuii hypE (encoding hydrogenase maturation protein) mutant. The hypE mutant grew a little faster than its parental 7653R and displayed decreased antioxidative capacity under H₂O₂-induced oxidative damage. Real-time quantitative PCR showed that hypE gene expression is significantly up-regulated in all the detected stages of nodule development. Although the hypE mutant can form nodules, the symbiotic ability was severely impaired, which led to an abnormal nodulation phenotype coupled to a 47% reduction in nitrogen fixation capacity. This phenotype was linked to the formation of smaller abnormal nodules containing disintegrating and prematurely senescent bacteroids. Proteomics analysis allowed a total of ninety differentially expressed proteins (fold change > 1.5 or <0.67, p < 0.05) to be identified. Of these proteins, 21 are related to stress response and virulence, 21 are involved in transporter activity, and 18 are involved in energy and nitrogen metabolism. Overall, the HypE protein is essential for symbiotic nitrogen fixation, playing independent roles in supplying energy and electrons, in bacterial detoxification, and in the control of bacteroid differentiation and senescence. Full article

With their exponentially rising computational power, digital platforms are heralding a new era of hybrid intelligence. There has recently been much enthusiasm and hype that the Metaverse has the potential to unlock hybrid intelligence. This is premised on the idea that the Metaverse represents an applied convergence of Artificial Intelligence of Things (AIoT) and Extended Reality (XR) that intersects with urbanism in terms of the distinctive features of platform-mediated everyday life experiences in cities. However, social interaction and its resulting social organization in the Metaverse are mediated and governed by algorithms and thus submitted to—a dream of—complete logical ordering. This raises a plethora of concerns related to the systemic collection and algorithmic processing of users’ personal, brain, and biometric data, i.e., profound societal—and the hardest to predict ethical—implications. Therefore, this study analyzes and synthesizes a large body of scientific literature on the unfolding convergence of AIoT and XR technologies, neurotechnology, and nanobiotechnology in the realm of the Metaverse in order to derive a novel conceptual framework for the Metaverse as an envisioned virtual model of platform urbanism. Further, it examines the key challenges and risks of these converging technologies in relation to the Metaverse and beyond. This study employs thematic analysis and synthesis to cope with multidisciplinary literature. The analysis identifies seven themes: (1) Platformization, (2) platform urbanism, (3) virtual urbanism, (4) XR technologies, (5) AIoT technologies, (6) neurotechnology, and (7) nanobiotechnology. The synthesized evidence reveals that, while neurotechnology and nanobiotechnology have numerous benefits and promising prospects, they raise contentions and controversies stemming from their potential use to inflict harm to human users—if left unchecked—through the black box of the algorithmic mediation underpinning the Metaverse. The findings serve to steer the Metaverse to contribute to human flourishing and wellbeing by adhering to and upholding ethical principles as well as leveraging its underlying disruptive technologies in meaningful ways. They also aid scholars, practitioners, and policymakers in assessing the pros and cons of these technologies, especially their inevitable ramifications. Full article

Undoubtedly, we are witnessing a new era of computer networks that aspire to support modern demanding applications by providing the highest Quality of Experience (QoE) to the end user. Next Generations Networks (NGNs) ensure that characteristics such as ultra-low latency, high availability and wide service coverage can be met across the network regardless of the network infrastructure ownership. To accomplish that, beyond the necessary improvements in the radio propagation field, changes have been made in the core network functions which are now characterized as programmable, and software defined. Software Defined Networks (SDNs) and Network Function Virtualization (NFV) are the keystones of the NGNs flexibility. The high expectations of NGNs’ performance and the continuous changes in the network conditions lead to the development of new network management frameworks that add elasticity and dynamicity and minimize human intervention. ETSI (the European Standards Organization) presents the Zero-touch Service Management (ZSM) framework that uses hyped technologies such as Artificial Intelligence (AI) and Machine Learning (ML) to achieve full end-to-end automation of the network services’ management across one or many different domains. Focusing on multi-domain network service management, there are several security issues identified by the standardization team which mostly derive from the lack of trust among network providers. In the present research, we explore the suitability of blockchain technology adoption for facing these security issues. Blockchain technology inherently addresses security in trustless environments such as the infrastructures defined by the ZSM team. Our contribution is three-fold: (a) we define the architecture of a multi-domain network infrastructure that adopts the ZSM approach and integrates blockchain functionality, (b) we explore the adoption of different blockchain and distributed ledger technologies (DLT) approaches to address ZSM security needs and (c) we provide guidelines to prospective solution designers/implementers on the detailed requirements that this solution has to meet to maximize the offered value. Full article

Reconfigurable robots design based on polyominos or n-Omino is increasingly being explored in cleaning and maintenance (CnM) tasks due to their ability to change shape using intra- and inter-reconfiguration, resulting in various footprints of the robot. On one hand, reconfiguration during a CnM task in a given environment or map results in enhanced area coverage over fixed-form robots. However, it also consumes more energy due to the additional effort required to continuously change shape while covering a given map, leading to a deterioration in overall performance. This paper proposes a new strategy for n-Omino-based robots to select a range of optimal morphologies that maximizes area coverage and minimizes energy consumption. The optimal “morphology” is based on two factors: the shape or footprint obtained by varying the angles between the n-Omino blocks and the number of n-Omino blocks, i.e., “n”. The proposed approach combines a Footprint-Based Complete coverage Path planner (FBCP) with a metaheuristic optimization algorithm to identify an n-Omino-based reconfigurable robot’s optimal configuration, assuming energy consumption is proportional to the path length taken by the robot. The proposed approach is demonstrated using an n-Omino-based robot named Smorphi, which has square-shaped omino blocks with holonomic locomotion and the ability to change from monomino to tetromino. Three different simulated environments are used to find the optimal morphologies of $Smorphi$ using three metaheuristic optimization techniques, namely, MOEA/D, OMOPSO, and HypE. The results of the study show that the morphology produced by this approach is energy efficient, minimizing energy consumption and maximizing area coverage. Furthermore, the HypE algorithm is identified as more efficient for generating optimal morphology as it took less time to converge than the other two algorithms. Full article

It is increasingly important to know the water quality of a reservoir, given the prospect of an environment poor in water reserves, which are based on intense and short-lived precipitation events. In this work, vegetation indices (NDVI, EVI) and bio-physical parameters of the vegetation (LAI, FC), meteorological variables, and hydrological data are considered as possible drivers of the spatial and temporal variability of water quality (WQ) of the Banja reservoir (Albania). Sentinel-2 and Landsat 8/9 images are analyzed to derive WQ parameters and vegetation properties, while the HYPE model provides hydrological variables. Timeseries of the considered variables are examined using graphical and statistical methods and correlations among the variables are computed for a five-year period (2016–2022). The added-value of integrating earth observation derived data is demonstrated in the analysis of specific time periods or precipitation events. Significant positive correlations are found between water turbidity and hydrological parameters such as river discharge or runoff (0.55 and 0.40, respectively), while negative correlations are found between water turbidity and vegetation descriptors (−0.48 to −0.56). The possibility of having easy-to-use tools (e.g., web portal) for the analysis of multi-source data in an interactive way, facilitates the planning of hydroelectric plants management operations. Full article

Workflow scheduling is essential to simultaneously optimize the makespan and economic cost for cloud services and has attracted intensive interest. Most of the existing multi-objective cloud workflow scheduling algorithms regard the focused problems as black-boxes and design evolutionary operators to perform random searches, which are inefficient in dealing with the elasticity and heterogeneity of cloud resources as well as complex workflow structures. This study explores the characteristics of cloud resources and workflow structures to design a knowledge-based evolutionary optimization operator, named KEOO, with two novel features. First, we develop a task consolidation mechanism to reduce the number of cloud resources used, reducing the economic cost of workflow execution without delaying its finish time. Then, we develop a critical task adjustment mechanism to selectively move the critical predecessors of some tasks to the same resources to eliminate the data transmission overhead between them, striving to improve the economic cost and finish time simultaneously. At last, we embed the proposed KEOO into four classical multi-objective algorithms, i.e., NSGA-II, HypE, MOEA/D, and RVEA, forming four variants: KEOO-NSGA-II, KEOO-HypE, KEOO-MOEA/D, and KEOO-RVEA, for comparative experiments. The comparison results demonstrate the effectiveness of the KEOO in improving these four algorithms in solving cloud workflow scheduling problems. Full article

Oscillating flow is one challenge for wide-Mach-number-range flight with supersonic/hype-rsonic vehicles. Aiming to mitigate the large-amplitude forced shock-wave oscillation, a 2D wavy surface has been implemented onto the flat-bottomed wall of the Sajben diffuser under downstream pressure disturbance. The oscillating SBLI in the diffuser is captured using the finite volume method with the second-order implicit dual-time-stepping method. Impacts of the wavy surface on the forced shock-wave oscillation are numerically investigated. It is found that increasing the wavy surface’s depth benefits mitigation of the shock-wave oscillation amplitudes on the walls under the given conditions, but that decreasing the wavy surface’s length may increase or decrease the oscillation amplitudes, depending on the specific value. The mitigating mechanism is interpreted from two viewpoints, i.e., the shock-wave stability and the work performed by a moving shock-wave. The transient second shock-wave temporally appears in the flow field and can be explained by the post-shock expansion. Full article

Compared to other applications of distributed ledger technologies, for example, in decentralized finance, non-fungible tokens, and logistics, Blockchain applications in the energy industry have not found widespread dissemination and fell short of market expectations during the Blockchain hype in the late 2010s. In semi-structured qualitative interviews with leading providers in the energy industry, conducted from 2019 to 2021, hurdles in energy applications are compared with a control group of additional interviews with representatives of companies operating in IT and FinTech. The analysis uses a framework covering technical feasibility, desirability, and economic viability, as well as the role of regulatory frameworks. The interviews reveal that the first Blockchain applications suffered from a combination of technological constraints and inter-platform competition. Due to the permissionless configuration of the early energy Blockchains, they were slow in terms of transaction speed compared to existing platforms and prices per transaction were high, in addition to high degrees of complexity related to requirements from both critical-infrastructure systems and financial market regulation. The analysis further points to the slow adoption of Blockchain applications in the energy sector being related to business models rather focusing on products and platforms as well as on transactional rather than procedural use cases, with a high degree of standardization of the offering and low levels of inclusiveness concerning processes. The move from transaction platforms to innovation platforms and the emergence of Blockchain as a service provider—plus technical advances with regards to high-frequency transactions combined with the increasing importance of use cases, such as proof of origin for fuels or e-charging—may induce a shift from pilot applications to commercialization within the larger innovation ecosystem. While the involvement of Blockchain solutions in energy markets increases with pilot projects and with this, the acceptance of players and stakeholders in the energy ecosystem, a big hurdle for innovation remains the regulation of energy markets to allow for peer-to-peer trading, a usage-driven distribution of network costs, and bottom-up pricing markets. Full article

Twelve years have passed since the early outlooks of applying genomic selection (GS) to forest tree breeding, initially based on deterministic simulations, soon followed by empirical reports. Given its solid projections for causing a paradigm shift in tree breeding practice in the years to come, GS went from a hot, somewhat hyped, topic to a fast-moving area of applied research and operational implementation worldwide. The hype cycle curve of emerging technologies introduced by Gartner Inc. in 1995, models the path a technology takes in terms of expectations of its value through time. Starting with a sudden and excessively positive “peak of inflated expectations” at its introduction, a technology that survives the “valley of disappointment” moves into maturity to climb the “slope of enlightenment”, to eventually reach the “plateau of productivity”. Following the pioneering steps of GS in animal breeding, we have surpassed the initial phases of the Gartner hype cycle and we are now climbing the slope of enlightenment towards a wide application of GS in forest tree breeding. By merging modern high-throughput DNA typing, time-proven quantitative genetics and mixed-model analysis, GS moved the focus away from the questionable concept of dissecting a complex, polygenic trait in its individual components for breeding advancement. Instead of trying to find the needle in a haystack, i.e., the “magic” gene in the complex and fluid genome, GS more efficiently and humbly “buys the whole haystack” of genomic effects to predict complex phenotypes, similarly to an exchange-traded fund that more efficiently “buys the whole market”. Tens of studies have now been published in forest trees showing that GS matches or surpasses the performance of phenotypic selection for growth and wood properties traits, enhancing the rate of genetic gain per unit time by increasing selection intensity, radically reducing generation interval and improving the accuracy of breeding values. Breeder-friendly and cost-effective SNP (single nucleotide polymorphism) genotyping platforms are now available for all mainstream plantation forest trees, but methods based on low-pass whole genome sequencing with imputation might further reduce genotyping costs. In this perspective, I provide answers to why GS will soon become the most efficient and effective way to carry out advanced tree breeding, and outline a simple pilot demonstration project that tree breeders can propose in their organization. While the fundamental properties of GS in tree breeding are now solidly established, strategic, logistics and financial aspects for the optimized adoption of GS are now the focus of attentions towards the plateau of productivity in the cycle, when this new breeding method will become fully established into routine tree improvement. Full article

In the world of structural design, in most cases, there is a need to control the shape of structural elements and—at the same time—the performance that each one can achieve. With the evolution of structural analysis tools, nowadays it is possible not only to have an immediate investigation of the structure’s performance, but also to search for the best shape by imposing geometric constraints. The aim of this paper is to present an innovative methodology called the performative structural design optimization (PSDO) method, based on the use of algorithm-aided design (AAD). The proposed approach deals with an emptied voided beam; starting from the parameterization of a large-span beam, the search method for the most performing shape is accomplished by multi-objective evolutionary algorithms (MOEAs). The obtained results are characterized by a double optimization: the structure achieved by the hypervolume estimation algorithm for multi-objective optimization (HypE Reduction) (OCTOPUS) represents the starting shape for the application of form-finding, giving so the possibility to obtain different feasible solutions from a single study and to choose the best one in terms of structural behavior. Full article

The development of information and communication technology has created many positive outcomes, including convenience for people; however, cases of unsolicited communication, such as spam, also occur frequently. Spam is the indiscriminate transmission of unwanted information by anonymous users, called spammers. Spam content is indiscriminately transmitted to users in various forms, such as SMS, e-mail, and social network service posts, causing negative experiences for users of the service, while also creating costs, such as unnecessarily large amounts of network traffic. In addition, spam content includes phishing, hype or false advertising, and illegal content. Recently, spammers have also used images that contain stimulating content to effectively attract users’ curiosity and attention. Image spam contains more complex information than text, making it more difficult to analyze and to generalize its properties compared to text. Therefore, existing text-based spam detectors are vulnerable to spam image attacks, resulting in a decline in service quality. In this paper, a “hybrid features by combining visual and text information to improve spam filtering performance” method is proposed to reduce the occurrence of misclassification. The proposed method employs three sub-models to extract features from spam images and a classifier model to output the results using the features. Each sub-model extracts topic-, word-, and image-embedding-based features from spam images. In addition, the sub-models use optical character recognition, latent Dirichlet allocation, and word2Vec techniques to extract features from images. To evaluate spam image classification performance, the spam classifiers were trained using the extracted features and the results were measured using a confusion matrix. Our model achieved an accuracy of 0.9814 and a macro-F1 score of 0.9813. In addition, the application of OCR evasion techniques resulted in a decrease in recognition performance. Using the proposed model, a mean macro-F1 score of 0.9607 was obtained. Full article

For nearly two decades, mobile health or (m-Health) was hailed as the most innovative and enabling area for the digital transformation of healthcare globally. However, this profound vision became a fleeting view since the inception and domination of smart phones, and the reorientation of the concept towards the exclusivity of global smart phone application markets and services. The global consumerization of m-Health in numerous disciplines of healthcare, fitness and wellness areas is unprecedented. However, this divergence between ‘mobile health capitalism’ and the ‘science of mobile health’ led to the creation of the ‘m-Health schism’. This schism was sustained by the continued domination of the former on the expense of the latter. This also led to increased global m-Health inequality and divide between the much-perceived health and patient benefits and the markets of m-Health. This divergence was more evident in low and middle income (LMIC) countries compared to the developed world. This powerful yet misguided evolution of the m-Health was driven essentially by complex factors. These are presented in this paper as the ‘known unknowns’ or ‘the obvious but sanctioned facts’ of m-Health. These issues had surreptitiously contributed to this reorientation and the widening schism of m-Health. The collateral damage of this process was the increased shift towards understanding ‘digital health’ as a conjecture term associated with mobile health. However, to date, no clear or scientific views are discussed or analyzed on the actual differences and correlation aspects between digital and mobile health. This particular ‘known unknown’ is presented in detail in order to provide a rapprochement framework of this correlation and valid presentations between the two areas. The framework correlates digital health with the other standard ICT for the healthcare domains of telemedicine, telehealth and e-health. These are also increasingly used in conjunction with digital health, without clear distinctions between these terms and digital health. These critical issues have become timelier and more important to discuss and present, particularly after the world has been caught off guard by the COVID-19 pandemic. The much hyped and the profiteering digital health solutions developed in response of this pandemic provided a modest impact, and the benefits were mostly inadequate in mitigating the massive health, human, and economic impact of this pandemic. This largely commercial reorientation of mobile health was unable not only to predict the severity of the pandemic, but also unable to provide adequate digital tools or effective pre-emptive digital epidemiological shielding and guarding mechanisms against this devastating pandemic. There are many lessons to be learnt from the COVID-19 pandemic from the mobile and digital health perspectives, and lessons must be learnt from the past and to address the critical aspects discussed in this paper for better understanding of mobile health and effective tackling of future global healthcare challenges. Full article