When animals evolve sufficient intelligence and dexterity to be able to learn to fabricate utility products (UPs) like tools, the UP's they produce become part of an induced-reproduction system that intrinsically shares many life-like traits with biological organisms, including genome-like fabrication and operation information that is physically-encoded in the animal fabricator’s neural networks. When this set of life- like traits includes a sufficient capacity for system-improving cultural evolution (UP-evolvability), the UPs become ‘para-alive’, i.e., nearly alive, or a form of non-biological UP-paralife that is equivalent to the life- status of biological viruses, plasmids, and transposons. In the companion paper I focus on the evolution of UP-paralife in the context of modern, language-capable humans and its predicted evolution going forward in time (Rice 2022). Here I look backward in time and focus on the origin of UP-paralife and its subsequent coevolution with human intelligence. I begin by determining the pathways leading to the evolution of large brains in the rare lineages of biological life that have sufficient intelligence to learn to fabricate tools –a critical first step in the evolution of UP-paralife. The simplest forms of these learning- based UPs, made by species like chimpanzees and New Caledonian crows, represent only proto-UP- paralife because they lack sufficient UP-evolvability. Expanded UP-evolvability required a combination of three attributes that enabled continuous niche-expansion of the animal fabricator via a new and advanced form of UP-mediated teamwork (TW): i) self-domestication that facilitated TW among low-related individuals, ii) learned volitional words (protolanguage) that represent ephemeral UPs that coordinate TW, and iii) learned fabrication of simple flaked-stone tools with cutting and chopping capabilities (a UP to make other structural UPs) that expanded teammate phenotypes and TW capabilities. This specific triad of attributes is synergistic because each one acts as a TW-enhancer that can gradually erode different components of the three major constraints on TW operation and expansion: too much selfishness, insufficient coordination signals, and insufficient physical traits of teammates. The increase in UP- evolvability was transformative and marked the origin of UP-paralife and the initiation of coevolution between UP-paralife (cultural evolution) and the intelligence of its hominin/human symbiont (genetic evolution) that fostered 2.5 million years of: i) continuous brain size increase and niche-expansion within the genus Homo, and ii) parallel advances in the diversity, complexity and uses of UP-paralife. This coevolution also fostered evolutionary expansion of word-based communication, and eventually language, that acted in a catalyst-like manner to facilitate the evolution of increasingly complex forms of imagination, reasoning, mentalizing, and UP-generating technology. I next focus on the evolution of creativity in the human lineage –in the form of divergent thinking and creative imagination. I conclude that the evolution of this advanced cognitive feature required a preadaptation of sufficient intelligence and is the component of human cognition that was the major causal factor generating the greatly expanded diversity and complexity of UP-paralife currently associated with modern humans. Lastly, I apply my findings to the issue of the prevalence of extraterrestrial intelligent life. I conclude that any exoplanets with detected chemical life will very rarely (e.g., probability ~10-5 for a planet closely matching Earth’s characteristics) have evolved intelligence equalling or exceeding that of humans.

The COVID-19 pandemic has expanded globally and caused serious consequence in both health system and the economy. MPV is one of two oral antivirals for COVID-19 with mild-to-moderate symptoms that have received FDA approval so far. This study aims to evaluate the cost-utility of using MPV in COVID-19 treatment. To compare the utility costs of MPV and non-MPV treatments for COVID-19 patients, we used a decision tree model. Data on clinical efficacy, cost, and quality of life were gathered from previous studies. The incremental effective cost ratio (ICER) calculated based on costs incurred and quality-adjusted life years (QALY), was considered as primary out-come. The total cost of MPV treatment and not using MPV was 15,362,516 VND (618 USD) and 15,771,262 VND (634 USD), respectively. The estimated QALY with using-MPV was 28.33, while without MPV was 19.72. The ICER was dominant, resulting in savings of 47,453 VND/QALY (1.9 USD). MPV is a cost-effective medication used to treat COVID-19. In mild-to-moderate COVID-19 patients, MPV considerably lowers costs and improves quality of life. However, several clinical factors of the drug still need to be taken into account.

With the pressure of the increasing density of urban areas, some public infrastructures are moving to the underground to free up space above, such as utility lines, rail lines and roads. In the big data era, the three dimensional (3D) data can be beneficial to understand the complex urban area. Comparing to spatial data and information of the above ground, we lack of the precise and detailed information about underground infrastructures, such as the spatial information of underground infrastructure, the ownership of underground objects and the interdependence of infrastructures in the above and below ground. How to map reliable 3D underground utility networks and use it in the land administration? First, to explain the importance of this work and find a possible solution, this paper observes the current issues of the existing underground utility database in Singapore. A framework for utility data governance is proposed to manage the work process from the underground utility data capture to data usage. This is the backbone to support the coordination of different roles in the utility data governance and usage. Then, an initial design of the 3D underground utility data model is introduced to describe the 3D geometric and spatial information about underground utility data and connect it to the cadastral parcel for land administration. In the case study, the newly collected data from mobile Ground Penetrating Radar is integrated with the existing utility data for 3D modelling. It is expected to explore the integration of new collected 3D data, the existing 2D data and cadastral information for land administration of underground utilities.

Most pathologies in emergency departments(EDs) can be detected with using non-invasive, extremely safe magnetic resonance imaging (MRI). MRI is highly sensitive to abnormality, so when compared to Computed Tomography(CT), a negative MRI far exceeds the value of a negative CT. This was a retrospective cohort study comparing resource utilization between September 2016 and September 2017 in a university hospital ED. Descriptive statistics are presented with frequency, percentage, mean, standard deviation, minimum and maximum values. A chi-square analysis was conducted to examine the relationships. Analyses were conducted using the SPSS 22.0 package program. In the ED, MRI is available 24/7. MRI was performed on 954 (479 female, 475 male) patients. A total of 212 cranial, 604 diffusion, 57 lumbar, 40 cervical, 38 dorsal, two abdominal, and one orbital MRIs were performed. In most groups, the average age was over 40, and the age distribution was similar (p = 0.12). There was no significant sex difference except for lumbar MRI. Lumbar MRI and diffusion MRI groups were admitted to the hospital mostly in the day hours (p = 0.03); in other groups, night and day admissions were almost the same. Neuroimaging takes the majority part of MRI examinations in our ED.

With a data revolution underway for some time, there is an increasing demand for formal privacy protection mechanisms that are not so destructive. Hereof microaggregation is a popular high-utility approach designed to satisfy the popular k-anonymity criteria while applying low distortion to data. However, standard performance metrics are commonly based on mean square error, which will hardly capture the utility degradation related to a specific application domain of data. In this work, we evaluate the performance of k-anonymous microaggregation in terms of the loss in classification accuracy of the machine learned models built from perturbed data. Systematic experimentation is carried out on four microaggregation algorithms that are tested over four data sets. The empirical utility of the resulting microaggregated data is assessed using the learning algorithm that obtains the highest accuracy from original data. Validation tests are performed on a test set of non perturbed data. The results confirm k-anonymous microaggregation as a high-utility privacy mechanism in this context and distortion based on mean squared error as a poor predictor of practical utility. Finally, we corroborate the beneficial effects for empirical utility of exploiting the statistical properties of data when constructing privacy preserving algorithms.

In this study we were interested in the behaviors of individuals who preserve the social and organizational environment by ensuring sustainability. More specifically we are interested in allegiants behaviors. Numerous studies have highlighted the normative character of allegiance. To confer an object the status of social norm means to assign value (in terms of desirability and utility) to that object. Therefore we questioned the value attributed to allegiance. 170 employees were questioned on the desirability and utility they attribute to a future work colleague (future peer or future subordinate) starting from the answers the latter was supposed to have given to a questionnaire on allegiance. It was observed that desirability and utility make reference to two independent dimensions, utility being often more important. It was also noted there is greater severity assigned to endo-group targets (future peers) than to exo-group targets (future subordinates). Finally, it was noted there was not so much a valuation of allegiant targets, but rather a rejection of rebel targets, which raises the question of the bi-dimensionality of the valuation-devaluation process.

We use the theory of coherent measures to look at the problem of surplus sharing in an insurance business. The surplus share of an insured is calculated by the surplus premium in the contract. The theory of coherent risk measures and the resulting capital allocation gives a way to divide the surplus between the insured and the capital providers, i.e. the shareholders.

The power sectors in most African countries face an enduring problem of utility performance – electricity utilities have failed to deliver adequate, reliable and competitively priced electricity to support economic growth and improve the welfare of their populations. Despite more than two decades of power sector re-forms, outcomes have been varied and often disappointing. Using a case study de-sign, we explore the five key enduring power challenges. The research utilizes a more powerful analytical framework that combines power sector reform theory and principal-agent theoretical lens to explore the experience of power sector reforms in Kenya and provides a deeper understanding of drivers of utility performance and reform impacts. Empirical findings show that the structural, governance and regulatory reforms that previously created incentives for improved utility performance are increasingly threatened by political influence. Kenya Power’s financial viability has deteriorated in recent years and the regulator has been undermined. One of our major conclusions is that when the relationship between the principal (government) and agent (utility) is well understood and the agent is properly incentivized, performance improvements are possible. However, when the government undermines or muddies those incentives through conflicting political interventions, performance improvements can be reversed.

This paper presents the behavior of decision makers, the possible choices and the strategies 1 resulting from the uncertainties related to the integration of renewable energies. Its uncertainties 2 are the risks associated with the volatility of renewable sources, the dynamics of energy production 3 as well as the planning and operation of the electricity grid. The goal is to model the risk-averse 4 decision-maker’s behavior and the choice of integrating renewable energies into the electrical system. 5 Following a bibliographic approach, we expose a methodology to model the decision-maker’s 6 behavior(risk aversion and predilection for risk) to risk taking. The risk-averse decision maker may 7 adopt nonlinear utility functions. Risk aversion is a behavior that reflects the desire to avoid risk 8 decisions and thus reduces the risk of adverse consequences. A decision support tool is provided to 9 the decision-maker to choose a best-fit strategy based on his preferences. The rational and risk-averse 10 decision-maker would seek to maximize a concave utility function instead of seeking to minimize its 11 cost. Taste or aversion to risk can be modeled by a thematic function of utility.

Utility perceived by individuals is believed to be different from the utility experienced by that individual. System dynamicists implicitly categorize this phenomenon as a form of bounded rationality and traditionally employ a simple smoothing function to capture it. We challenge this generalization by testing it against an alternative formulation of utility perception that is suggested by modern theories of behavioral economics. In particular, the traditional smoothing formulation is compared with the peak-end rule in a simple theoretical model as well as in a medium-size model of electronic health record implementation. Experimentation with the models reveals that the way utility perception is formulated is important and might affect behavior and policy implications of system dynamics models.

The article proposes a mathematical model for creating playlists based on the emotional utility of the consumer, aiming to maintain high user interest in music streaming platforms (Lugos Abarca, 2024). The proposed method follows these steps: first, it calculates the MEEPE of Songs on the digital platform; subsequently, it quantitatively defines the Consumer's MEEPI before exposure to music; then, it operates the MEEPE of the Songs with the consumer's initial MEEPI using the known resolution process (Lugos Abarca, 2023); finally, it employs an emotional utility function to determine possible levels of emotional satisfaction and establish the playback order. This results in the creation of an efficient theoretical model. It is recommended to expand the computational study of the model in future research by using a broader set of songs and considering various initial MEEPI of the consumer. The article's originality lies in its ability to predict, quantitatively and theoretically, the level of satisfaction a music consumer could experience when listening to a playlist.

The objective of this research is to propose a utility function that calculates the possible level of satisfaction that a music consumer would achieve after listening to a song according to the emotional response obtained from a song. Assuming that the consumer's musical preferences are satisfied if the predominant emotions of his current emotional state increase; the probabilistic emotional-musical model (Lugos Abarca, 2023) is used to construct such a utility function. By posing a hypothetical scenario, realistic results were obtained, which guarantee, in principle, its use in theoretical practice. The originality of this research lies in proposing a mathematical tool to study, quantitatively, the decision making of a musical consumer influenced by his emotions. The main conclusion being its importance in the music industry, since the function could predict, theoretically, whether a song will be accepted or rejected by the consumer in comparison with the competition.

Osteoarthritis (OA) is the degeneration of cartilage in joints that results in bones rubbing against each other, it causes uncomfortable symptoms such as pain, swelling, stiffness, and can lead to disability. It usually occurs in the elderly and causes a large medical burden. The aim of this study is to evaluate cost-effectiveness between the standard treatment for osteoarthritis and the standard treatment with added crystalline glucosamine sulfate at various stages. The Markov analysis modelling was applied to evaluate the effect of both adding glucosamine compared to standard treatment from a societal perspective during whole patients’ lifetimes. Data input was collected by review in previous studies. The outcome was measured in quality-adjusted life years (QALYs), and the incremental cost-effectiveness ratio (ICER) from a societal perspective was ap-plied with 3%-discounted for all cost and outcome. One-way analysis via Tornado diagram was performed to investigate the change of factors in model. In general, the adding glucosamine into the standard treatment proved to be more cost-effectiveness compared to the standard treatment. Particularly, early stage adding glucosamine in the treatment was cost-effectiveness then post-stage adding glucosamine. The addition of supplementing crystalline glucosamine sulfate to whole regimen at any stage was cost-effectiveness at willingness-to-pay (WTP) threshold

Background: Stoma closure is a widely performed surgical procedure, with 6295 undertaken in England in 2018 alone. This procedure is associated with significant complications; incisional hernias are the most severe, occurring in 30% of patients. Complications place considerable financial burden on the NHS; hernia costs are estimated at GBP 114 million annually. As recent evidence (ROCSS, 2020) found that prophylactic meshes significantly reduce rates of incisional hernias following stoma closure surgery, an evaluation of this intervention vs. standard procedure is essential. Methods: A cost-utility analysis (CUA) was conducted using data from the ROCSS prospective multi-centre trial, which followed 790 patients, randomly assigned to mesh closure (n=394) and standard closure (n=396). Quality of life was assessed using mean EQ-5D-5L scores from the trial, and costs in GBP using UK-based sources over a 2-year time horizon. Results: The CUA yielded an incremental cost-effectiveness ratio (ICER) of GBP 128,356.25 per QALY. Additionally, two univariate sensitivity analyses were performed to test the robustness of the model. Conclusion: The results demonstrate an increased benefit with mesh prophylaxis, but at an increased cost. Although the intervention is cost-ineffective and greater than the ICER threshold of GBP 30,000/QALY (NICE), further investigation into mesh prophylaxis for at risk population groups is needed.

Existing literature on the relationship between ride-hailing (RH) and transit services is limited to empirical studies that lack real-time spatial contexts. To fill this gap, we took a novel real-time geospatial analysis approach. With source data on ride-hailing trips in Chicago, Illinois, we computed real-time transit-equivalent trips for all 7,949,902 ride-hailing trips in June 2019; the sheer size of our sample is incomparable to the samples studied in existing literature. An existing Multinomial Nested Logit Model was used to determine the probability of a ride-hailer selecting a transit alternative to serve the specific O-D pair, P(Transit|CTA)[1]. We find that 31% of ride-hailing trips are replaceable, whereas 61% of trips are not replaceable. The remaining 8% lie within a buffer zone. We measured the robustness of this probability using a parametric sensitivity analysis and performed a two-tailed t-test. Our results indicate that of the four sensitivity parameters, the probability was most sensitive to the total travel time of a transit trip. The main contribution of our research is our thorough approach and fine-tuned series of real-time spatiotemporal analyses that investigate the replaceability of ride-hailing trips for public transit. The results and discussion intend to provide perspective derived from real trips and we anticipate that this paper will demonstrate the research benefits associated with the recording and release of ride-hailing data. [1] This value defines the replaceability of the trip, where a value ranging from 0 to 0.45 is considered not-replaceable (NR), and a value ranging from 0.55 to 1.0 is considered replaceable (R).

The target of an antibody plays a significant role in the success of antibody-based therapeutics and diagnostics, and to an extent, that of vaccine development. This importance is focussed on the target binding site – epitope, where epitope selection as a part of design thinking beyond traditional antigen selection using whole cell or whole protein immunisation can positively impact success. With purified recombinant protein production and peptide synthesis to display limited/selected epitopes, intrinsic factors that can affect the functioning of resulting antibodies can be more easily selected for. Many of these factors stem from the location of the epitope that can affect accessibility of the antibody to the epitope at a cellular or molecular level, direct inhibition of target antigen activity, conservation of function despite escape mutations, and even non-competitive inhibition sites. Through the incorporation of novel computational methods for predicting antigen changes to model-informed drug discovery and development, superior vaccines and antibody-based therapeutics or diagnostics can now be more easily designed to mitigate failures. With detailed examples, this review highlights the new opportunities, factors and methods of predicting antigenic changes for consideration in sagacious epitope selection.

Dairy cattle predicted by genomic breeding values to be heat tolerant are known to have less milk production decline and lower core body temperature increases in response to elevated temperatures. In a study conducted at the University of Melbourne’s Dookie Robotic Dairy Farm during summer, we identified the most 20 heat-susceptible and 20 heat-tolerant cows in a herd of 150 Holstein Friesian lactating cows based on their phenotypic responses (changes in respiration rate, surface body temperature, panting score, and milk production). Hair samples were collected from the tip of the cows' tail following standard genotyping protocols. The results indicated variation in feed saved and HT genomic estimated breeding values (GEBVs) (P≤0.05) across age, indicating a potential for their selection. As expected, the thermotolerant group had higher GEBVs for HT and feed saved but lower for milk production. In general, younger cows had superior GEBVs for Balanced Performance Index (BPI), Type Weighted Index (TWI) and Australian Selection Index (ASI), whilst older cows were superior in fertility, feed saved (FS) and HT. The study demonstrated highly significant (P≤ 0.001) negative correlations (-0.28 to -0.74) between HT and GEBVs for current Australian Dairy cattle selection indices (BPI, TWI, ASI, HWI) and significant (P≤ 0.05) positive correlations between HT and GEBVs for traits like FS (0.45) and fertility (0.25). Genomic selection for HT will help improve cow efficiency and sustainability of dairy production under hot summer conditions. However, a more extensive study involving more lactating cows across multiple farms is recommended to confirm the associations between the phenotypic predictors of HT and GEBVs.

Advancement in biotechnology and genomics research have promoted access to DNA markers and their use in breeding programs. Genome-wide association study (GWAS), Genomic selection (GS) and Marker-Assisted Selection (MAS) are some of the applications of DNA markers in plant breeding. Researchers have suggested combining these individual applications for better selection accuracies. This study examines the potential advantages of incorporating GWAS-results into MAS and GS as well as the validity of the different methods for combining these approaches. From this study, it was concluded that number of QTNs have greater effects on prediction accuracies compared to heritability estimates. Also, the increase in prediction accuracy from the invalid method of incorporating GWAS results into GS and MAS model is similar to results recorded with using the valid approach. However, greater difference may be observed in another scenario which can lead to spurious results when used to make breeding decisions.

The rise of digital technology and artificial intelligence has led to a significant change in the way financial services are delivered. One such development is the emergence of robo-advising, which is an automated investment advisory service that utilizes algorithms to provide investment advice and portfolio management to investors. Robo-Advisors collect information about their clients’ preferences, financial situation and future goals through questionnaires, then recommend ETF based portfolios supposed to fit investor’s risk profile. However, questionnaires seem to be vague, and robos do not reveal the methods used for investor profiling or asset allocation. This study aims at contributing by proposing an investor profiling method, based only on the level of relative risk aversion (RRA) of investors. We show how to determine RRA optimal portfolios and compare the performance of our portfolios with portfolios proposed by the Riskalyze platform.

In this paper, we propose and evaluate two mechanisms aimed at improving the communication reliability of IEEE 802.15g SUN (Smart Utility Networks) in industrial scenarios: RTS (Re-Transmission Shaping), which uses acknowledgements to track channel conditions and dynamically adapt the number of re-transmissions per packet, and AMS (Adaptive Modulation Selection), which makes use of reinforcement learning based on MAB (Multi-Armed Bandits) to choose the modulation that provides the best reliability for each packet re-transmission. The evaluation of both mechanisms is performed through computer simulations using a dataset obtained from a real-world deployment and two widely used metrics, the PDR (Packet Delivery Ratio) and the RNP (Required Number of Packet transmissions). The PDR measures the ratio between received and transmitted packets, whereas the RNP is the number of packet repetitions before a successful transmission. The results show that both mechanisms allow to increase the communication reliability while not jeopardizing the battery life-time constraints of end devices. For example, when three re-transmissions per packet are allowed, the PDR reaches 98/96\% with a RNP of 2.03/1.32 using RTS and AMS, respectively. Additionally, the combination of both proposed mechanisms allows to reach a 99% PDR with a RNP of 1.7, making IEEE 802.15.4g SUN compliant with the stringent data delivery requirements of industrial applications.

Packet re-transmissions are a common technique to improve link reliability in low-power wireless networks. However, since packet re-transmissions increase the end-device energy consumption and the network load, a maximum number of re-transmissions per packet is typically set, also considering the duty-cycle limitations imposed by radio-frequency regulations. Moreover, the number of re-transmissions per packet is typically set to a constant value, meaning that all packet re-transmissions are treated the same regardless of actual channel conditions (i.e., multi-path propagation or internal/external interference effects). Taking that into account, in this paper we propose and evaluate the concept of re-transmission shaping, a mechanism that manages packet re-transmissions to maximize link reliability, while minimizing energy consumption and meeting radio-frequency regulation constraints. The proposed re-transmission shaping mechanism operates by keeping track of unused packet re-transmissions and allocating additional retransmission when the instantaneous link quality decreases due to channel impairments. To evaluate the re-transmission shaping mechanism we use trace-based simulations using a IEEE 802.15.4g SUN data-set and two widely used metrics, the PDR (Packet Delivery Ratio) and the RNP (Required Number of Packets). The obtained results show that re-transmission shaping is a useful mechanism to improve link reliability of low-power wireless communications, as it can increase PDR from 77.9% to 99.2% while sustaining a RNP of 2.35 re-transmissions per packet, when compared to using a single re-transmission per packet.

This paper formulates a new strategy for participatory forest management consisting of encouraging public participation as long as it increases empathy among participants. The strategy requires the homogeneous representation of the opinion of a participant (i.e. to determine how they assess a forest plan and identify the best one). Utility assessments are prepared for participants through pair-comparisons between meaningful points in the territory and from value functions based on forest indicators. The best plan is designed by applying combinatorial optimization algorithms to the utility of a participant. The calculating of empathy -of one participant relative to another - is based on the equivalence of their respective utilities when the current forest plan is modified. This involves calculating the opinions that are due to systematic changes in the collective plan for those participants that each participant supposes will affect the utility of the other participants. Calculating empathy also requires knowing the interactions among participants, which have been incorporated through agent-based simulation models. Application of the above methodology has confirmed the association between increases in empathy and convergence of opinions in different scenarios: well and medium-informed participants and with and without interaction among them, which verifies the proposed strategy. In addition, this strategy is easily integrated into available information systems and its outcomes show advantages over current participatory applications.

A review of the concept of "fitness" as it is used in evolutionary theory.

The role of chemical communication in social relationships of birds is receiving growing attention but our knowledge is still scarce compared to other taxa. Previous evidence suggests that chemical cues emitted by birds may carry information about their characteristics that may be useful in a sexual selection context. However, experimental studies are needed to investigate the role of bird chemical cues in signalling quality of potential partners. We performed an experimental study aimed to disentangle whether female zebra finches, Taeniopygia guttata, use chemical cues to assess the body condition and body size of potential partners. We offered focal females the scent of two males differing in body condition and body size. Our results show that females can assess the body condition of potential partners using olfaction. However, contrary to expected in a mate choice context, our results show that females avoided the scent of the male with greater body condition and body size. Our results therefore suggest that, despite performing the study during breeding period, social interactions may be mediating the avoidance of the scent of the conspecific of the opposite sex with better body condition and body size in this gregarious species.

Chipman contends, in stark contrast to the conventional view, that utility is not a real number but a vector and is inherently lexicographic in nature. According to these views, it will be proved that, for every preordered topological space (X,≾,t), the assumption t to be finer than the order topology t≾ on (X,t), i.e. t⊇t≾, and the assumption the quotient topology t∣∼≾ that is defined on the preordered set (X∣∼,≾∣∼) of indifference classes of ≾ to be Hausdorff imply that a cardinal number κ and a (complete) preorder ≲ on {0,1}κ that is coarser than the lexicographical ordering ≤lex on {0,1}κ, i.e. ≤lex⊂≲, can be chosen in such a way that there exists a continuous order-embedding ϑ:(X,≾,t)⟶({0,1}κ,≲,t≤lex). This theorem will be compared with a theorem that, in particular, describes necessary and sufficient conditions for ≾ to have a continuous multi-utility representation.

Genome-based testing in oncology is a rapidly expanding area of health care which is the basis of the emerging area of precision medicine. Efficient and considered adoption of novel genomic medicine testing is hampered in Canada by the fragmented nature of health care oversight as well as by lack of clear and transparent processes to support rapid evaluation, assessment and implementation of genomic tests. This article provides an overview of some key barriers and proposes approaches to addressing these challenges as a potential pathway to developing a national approach to genomic medicine in oncology.

Abstract: Background: Trauma is the leading cause of morbidity and mortality in young patients, acute surgical conditions cause morbidity and mortality in patients of all ages. Computed tomography scan (CT) assist greatly in the investigation and management of these patients with blunt and penetrating trauma, facilitating early diagnosis and expedites early appropriate treatment of these patients. Acute non-traumatic surgical conditions also mimic non-surgical pathologies and differentiating the two is important because management is different and any delay in surgical intervention results in high morbidity and some may even result in mortality. Method: Literature review of the role of multidetector CT scan (MDCT) in managing acute surgical emergencies and trauma-diagnosis in the trauma patient and patients presenting with acute surgical emergencies. The focus is on the role of the MDCT in the management of these patients. Results highlight that the use of MDCT expedites management of these patients, allowing early surgical intervention and preventing unnecessary surgical interventions where the intervention is not indicated, based on the MDCT results. Conclusion: This paper emphasizes the important role that is played by the MDCT in the management of the above patients.

A stochastic dominance (SD) relation can be defined by two different perspectives: One from the view of distributions, and the other one from the view of expected utilities. In early days, Fishburn investigated SD from the view of distributions and we refer this perspective as Fishburn’s SD. One of his many results was the development of fractional-order SD for continuous distributions. However, discrete fractional-order SD may not be generalized directly since some properties of fractional calculus do not have a discrete counterpart. In this paper, we develop a discrete analogue of fractional-order SD from the view of distributions. We generalize the order of SD by Lizama’s fractional delta operator, show the preservation of SD hierarchy, and formulate the utility classes that are congruent with our SD relations. This work brings a message that some results of discrete SD cannot be generalized directly from continuous SD. We characterize the difference between discrete and continuous fractional-order SD, as well as the way to handle them.

The physical and economic sustainability of using Built Environment Wind Turbine (BEWT) systems depends on the wind resource potential of the candidate site. Therefore, it is crucial to carry out a wind resource assessment prior to deployment of the BEWT. The assessment results can be used as a referral tool for predicting the performance and lifespan of the BEWT in the given built environment. To date, there is limited research output on BEWTs in South Africa with available literature showing a bias towards utility-scale or conventional ground based wind energy systems. This study aimed to assess wind power generation potential of BEWT systems in Fort Beaufort using the Weibull distribution function. The results show that Fort Beaufort wind patterns can be classified as fairly good and that BEWTs can best be deployed at 15m for a fairer power output as roof height wind speeds require BEWT of very low cut-in speed of at most1.2ms⁻¹.

In this essay, I revisit Neural Networks (NNs) through the lens of evolutionary dynamics. From the two most important features of NNs, I recover the two most general equations of evolutionary dynamics. These two equations may thus serve as a connection between NNs and a body of knowledge that has been built over multiple centuries and has found application across a vast range of disciplines. This connection may therefore also help explain why NNs with the two features in question are applicable across a much broader range of domains than initially envisioned.

The IEEE 802.15.4-2015 standard includes the SUN (Smart Utility Networks) modulations, i.e., SUN-FSK, SUN-OQPSK and SUN-OFDM, which provide long range communications and allow to trade data rate, occupied bandwidth and reliability. However, given the constraints of low-power devices and the challenges of the wireless channel, communication reliability cannot still meet the PDR (Packet Delivery Ratio) requirements of industrial applications, i.e., PDR>99%. Hence, in this paper we evaluate the benefits of improving communication reliability by combining packet transmissions with modulation diversity using multiple IEEE 802.15.4g SUN modulations. The results derived from a real-world deployment show that going from 1 to 3 packet transmissions with the same SUN modulation can increase PDR from 85.0/84.6/71.3% to 94.2/94.1/86.0% using SUN-FSK, SUN-OQPSK and SUN-OFDM, respectively. Combining the same number of packet transmissions with modulation diversity allows to further increase the average PDR to 97.1%, indicating its potential as a tool to help meeting the reliability requirements of industrial applications.

Retail buildings can provide energy flexibility to the grid with the possibility of load shifting and building automation systems. Demand response is a collective innovation in the smart grid domain. Various stakeholders should be involved in the demand response activities to ensure the success. The owners or senior management of retail buildings need to consider the stakeholders who are directly influenced by the demand response participation, e.g. customers and employees. Meanwhile, demand response activities are influenced by various factors, such as energy market structure, policy, etc. Therefore, this paper investigates the demand response readiness for retail buildings with three aspects: energy control preferences, stakeholder engagement, and cross-national differences. A questionnaire is designed and collected with store managers in Denmark (N=51) and the Philippines (N=36). The result shows that: 1) retail stores are much readier to participate in the implicit demand response by manual energy control compared to the utility control or building automation. Meanwhile, store managers have significant concerns about business activities and indoor lighting compared to other aspects; 2) the statistically significant influential factors for retail stores to participate in the demand response are related to whether the DR participation matches the company goals, influences business operation, and whether retail stores are lack of related knowledge; 3) retail stores believe that stakeholders should be informed about the DR activities but not involved in; 4) there are significant differences regarding the energy control preferences and concerns between retail stores in Denmark and the Philippines, but no significant difference regarding the stakeholder engagement.

Purpose: Expanded carrier screening (ECS) informs couples of their risk of having offspring affected by certain genetic conditions. Limited data exists assessing the actions and reproductive outcomes of at-risk couples (ARCs). We describe the impact of ECS on planned and actual pregnancy management in the largest sample of ARCs studied to date. Methods: Couples who elected ECS and were found to be at high risk of having a pregnancy affected by at least one of 176 genetic conditions were invited to complete a survey about their actions and pregnancy management. Results: Three hundred ninety-one ARCs completed the survey. Among those screened before becoming pregnant, 77% planned or pursued actions to avoid having affected offspring. Among those screened during pregnancy, 37% elected prenatal diagnostic testing (PNDx) for that pregnancy. In subsequent pregnancies that occurred in both the preconception and prenatal screening groups, PNDx was pursued in 29%. The decision to decline PNDx was most frequently based on the fear of procedure-related miscarriage, as well as the belief that termination would not be pursued in the event of a positive diagnosis. Conclusions: ECS results impacted couples’ reproductive decision-making and led to altered pregnancy management that effectively eliminates the risk of having affected offspring.

Cotton is the most important natural fiber cash crop, which has high commodity economic benefits and provides an important material foundation for China's construction. With the improvement of textile technology and living standard, higher requirements are put forward for raw cotton quality. Traditional cotton breeding methods need typing and selection. With the development of biotechnology and the research of genomics, genomic selection has been widely used in cotton breeding. Genomic selection is a new breeding method, which can be selected and bred by constructing a prediction model and using high-density molecular markers covering the whole genome. In this study, the application of various genomic selection models in cotton fiber quality was explored, which provided more reliable information for genetic breeding improvement in the future, thus helping to improve the efficiency of actual cotton breeding.

Agronomic traits such as biomass yield and abiotic stress tolerance are genetically complex and challenging to improve by conventional breeding strategies. Genomic selection (GS) is an alternative approach in which genome-wide markers are used to determine the genomic estimated breeding value (GEBV) of individuals in a population. In alfalfa, previous results indicated that low to moderate prediction accuracy values (<70%) were obtained in complex traits such as yield and abiotic stress resistance. There is a need to increase the prediction value in order to employ GS in breeding programs. In this paper we reviewed different statistic models and their applications in polyploid crops including alfalfa. Specifically, we used empirical data affiliated with alfalfa yield under salt stress to investigate approaches which use DNA marker importance values derived from machine learning models, and genome-wide association studies (GWAS) of marker-trait association scores based on different GWASpoly models, in weighted GBLUP analyses. This approach increased prediction accuracies from 50% to more than 80% for alfalfa yield under salt stress. This is the first report in alfalfa to use variable importance and GWAS-assisted approaches to increase the prediction accuracy of GS, thus helping to select superior alfalfa lines based on their GEBVs.

Background: The genetic variants identified by three large genome-wide association studies (GWAS) of educational attainment were used to test a polygenic selection model. Methods: Average frequencies of alleles with positive effect (polygenic scores or PS) were compared across populations (N=26) using data from 1000 Genomes. A null model was created using frequencies of random SNPs. Results: Polygenic selection signal of educational attainment GWAS hits is high among a handful of SNPs within genomic regions replicated across GWAS publications. A polygenic score comprising 9 SNPs predicts population IQ (r=0.88), outperforming 99% of the polygenic scores obtained from sets of random SNPs (Monte Carlo p= 0.011). Its predictive power remains unaffected after controlling for spatial autocorrelation (Beta= 0.83). The largest polygenic score (161 SNPs) exhibits similar predictive power (Beta=0.8). Random polygenic scores are moderate predictors of population IQ (thanks to spatial autocorrelation), and their predictive power increases logarithmically with the number of SNPs, indicating an exponential reduction in noise. Conclusion: This study provides guidance for using GWAS hits together with random SNPs for testing polygenic selection using Monte Carlo simulations.

Electricity demand forecasting has been a real challenge for power system scheduling in the different levels of the energy sectors. Various computational intelligence techniques and methodologies have been employed in the electricity market for load forecasting; although, scant evidence is available about the feasibility of each of these methods considering the type of data and other potential factors. This work introduces several scientific, technical rationale behind intelligent forecasting methods, based on the work of previous researchers in the field of energy. The fundamental benefits and main drawbacks of the aforementioned methods are discussed in order to depict the efficiency of each approach in various situations. In the end, a proposed hybrid strategy is represented.

Here I describe an overlooked form of non-biological paralife (i.e., near-life) that has been evolving on Earth for millions of years, and is currently in the final stages of transitioning into a new form of life. Any consideration of non-biological life or paralife is complicated by the fact that there is no consensus among biologists for the definition of life. This ambiguity has caused disagreement about whether subcellular reproduction systems like viruses are a form of life, despite having genomes, mutations, heritable phenotypes and system-improving evolution. To resolve this problem, I develop a definition of life that is entirely functional and independent of any of the structural idiosyncrasies of biological life on Earth: an order-generating system controlled by internally-encoded information that perpetuates itself by functioning to counteract its entropic decay. Using this definition, subcellular transposons, plasmids, and viruses are paralife because they match the definition of life in all ways except that they induce their order-generating functioning by a living host rather through their own self-sustaining production system. Using this functional definition of life, I show that utility- products (UPs) like fabricated hand tools are part of induced-reproduction systems that have features equivalent to biological genomes, mutations, heritable phenotypes, and a process of system-improving evolution. The perceived benefit of utility-products causes them to induce their reproduction by a biological life-form (humans). For these reasons, human utility products are functionally just as close to being a form of life as subcellular transposons, plasmids, and viruses, i.e., they are Utility-Product paralife (UP-parlife). I also show that some forms of UP-paralife are currently evolving into mechanical life that is capable of both self- sustaining reproduction and system-improving evolution without outside assistance. This transition requires the development of a high level of factory and/or UP automation and artificial intelligence (AI) that is capable of complex reasoning, imagination and creativity. Finally, I consider the influence of UP-life and UP-paralife on the development of the level of structural complexity in the universe, and I briefly speculate about how these non-biological forms of life and paralife will influence the expansion of scientific knowledge about the universe.

While the foundations of climate science and ethics are well established, fine-grained climate predictions, as well as policy-decisions, are beset with uncertainties. This chapter maps climate uncertainties and classifies them as to their ground, extent and location. A typology of uncertainty is presented, centered along the axes of scientific and moral uncertainty. This typology is illustrated with paradigmatic examples of uncertainty in climate science, climate ethics and climate economics. Subsequently, the chapter discusses the IPCC’s preferred way of representing uncertainties and evaluates its strengths and weaknesses from a risk management perspective. Three general strategies for decision-makers to cope with climate uncertainty are outlined, the usefulness of which largely depends on whether or not decision-makers find themselves in a context of deep uncertainty. The chapter concludes by offering two recommendations to ease the work of policymakers, faced with the various uncertainties engrained in climate discourse.

A micro decision-making utility model under uncertainty is presented as a complementary foundation for macro coronavirus models. The micro model consists of two functions, a risk averse utility function depending on wellness and a wellness random output which is a function of the input variable called “treatment” consisting of such elements as social distance, washing hands, wearing a face mask, and others. The decision maker selects a level of treatment that maximizes her/his expected utility, given the probabilities of the respective outputs. The focus is on how changes in a person’s psychological attitude towards the macro determined (announced) probabilities affects the optimum results of the model. Such changes create a micro-macro dynamic interaction which is briefly outlined. A short discussion of the model’s behavioral implications for health policy is also given.

Users of meteorological forecasts are often faced with the question of whether to make a decision now based on the current forecast or whether to wait for the next and hopefully more accurate forecast before making the decision. One would imagine that the answer to this question should depend on the extent to which there is a benefit in making the decision now rather than later, combined with an understanding of how the skill of the forecast improves, and information about the possible size and nature of forecast changes. We extend the well-known cost-loss model for forecast-based decision making to capture an idealized version of this situation. We find that within this extended cost-loss model, the question of whether to decide now or wait depends on two specific aspects of the forecast, both of which involve probabilities of probabilities. For the special case of weather and climate forecasts in the form of normal distributions we derive a simulation algorithm, and equivalent analytical expressions, for calculating these two probabilities. We apply the algorithm to forecasts of temperature and find that the algorithm leads to better decisions relative to three simpler alternative decision-making schemes. Similar problems have been studied in many other fields, and we explore some of the connections.

Self-selecting the intensity of aerobic training is a perspective that allows practitioners to develop autonomy (choice of pace), improve physical fitness and achieve weight loss. To compare the effect of four aerobic training protocols on cardiometabolic and psycho-affective responses in physically inactive adult men and women. The sample consisted of 11 individuals aged 56 ± 10 years and BMI 29.2 ± 2.4 kg/m2 performed four different treadmill aerobic training protocols, including: 1-individual with self-selected intensity (ISS), 2-individual with prescribed intensity (IPI) (64-76% of HR max), 3-small group with self-select. selected intensity (SGS) and 4-small group prescribed intensity (SGP) (64-76% of HR max). Heart rate (HR), lactate (LACT), perceived exertion (RPE), affective response (FS), activation scale (FAS), enjoyment (PACES) and intention to repeat the session (INT) were analyzed. Statistical analysis used ANOVA with Tukey's post hoc test (p&lt;0.05). HR, LACT and RPE analyzes did not show significant differences between protocols. The FS showed a smaller decline in the SI protocol (p&lt;0.05), and the intention to repeat the training session was greater in the SI protocol (p&lt;0.05). Conclusion: The data allow us to conclude that the SI training protocol produced superior results in affective response, pleasure and intention to repeat the session.

. Various elements such as evolutions in IoT services resulting from sensoring by vehicle parts and advances in small communication technology devices have significantly impacted the mass spread of mobility services that are provided to users in need of limited resources. In particular, business models are progressing away from one-off cost towards longer term cost as represented by shared services utilizing kick-boards or bicycles and subscription services for vehicle software. Advances in shared mobility services as described are calling for solutions that can enhance reliability of data aggregate by users leveraging mobility services in the next-generation mobility areas. However, mining process to renew status, ensures continued network communication and block creation demands high performance in public block chain. This thesis proposes random certificate node selection mechanism in block network that creates blocks via node that has tokens issued for block creation and lets only specific nodes selected by encrypting token information acquires token.

Significant attribute selection in machine learning is one of the key aspects aimed at simplifying the problem and reducing its dimensionality, and consequently speeding up computation. This paper proposes new algorithms for selecting not only relevant features but also for evaluating and selecting a subset of relevant objects in a dataset. Both algorithms are mainly based on the use of a fuzzy approach. The research presented here yielded preliminary results of a new approach to the problem of selecting relevant attributes and objects, and, in fact, to selecting appropriate ranges of their values. Detailed results obtained on the Sonar dataset show the positive effects of this approach. Moreover, the observed results may suggest the effectiveness of the proposed method in terms of identifying a subset of truly relevant attributes from among those identified by traditional feature selection methods.

In Taiwan, peanuts are classified by hand. The kernel quality is judged based on the appearance of peanuts. The identification takes considerable time and labor. Fatigue can induce misrecogni-tion or grade cognition instability due. Peanut defects are generally divided into health, underde-velopment, insect bite, and rupture. This study employed two imaging methods push-boom FX10 and Snapshot. Deep learning was used to detect peanut defects. A push-broom instrument was used for testing. There were 1,560 peanuts, including 240 good and 240 bad ones. There were a total of 1,080 good peanuts and bad peanuts in the test set data, the band selection was used, and a CNN model was built. Based on the results from three methods, 3DCNN could classify with 97%accuracy. The Snapshot was used to achieve the real-time design of a lightweight CNN model. Finally, five bands were selected using PCA, and the screening could be accurate and efficient. The maximum overall accuracy was about 98.5%. Kappa was 97.3% with real-time recognition. This will be a major advantage in the future practical application and commercialization processes. This technique can reduce labor costs, achieve intelligent detection and intelligent grading, and bring breakthroughs to smart agriculture for other crops as well.

Malaria remains an important cause of death, especially in sub-Saharan Africa with about 228 million malaria cases worldwide and an estimated 405,000 deaths in 2019. Currently, malaria is diagnosed in the health facility using a microscope (BS) or rapid malaria diagnostic test (MRDT) and with area where these tools are inadequate the presumptive treatment is performed. Apart from that self-diagnosis and treatment is also practiced in some of the households. With the high-rate self-medication on malaria drugs, this study aimed at computing the most significant features using feature selection methods for best prediction of malaria in Tanzania that can be used in developing a machine learning model for malaria diagnosis. A malaria symptoms and clinical diagnosis dataset were extracted from patients’ files from four (4) identified health facilities in the regions of Kilimanjaro and Morogoro. These regions were selected to represent the high endemic areas (Morogoro) and low endemic areas (Kilimanjaro) in the country. The dataset contained 2556 instances and 36 variables. The random forest classifier a tree based was used to select the most important features for malaria prediction. Regional based features were obtained to facilitate accurate prediction. The feature ranking as indicated that fever is universally the most influential feature for predicting malaria followed by general body malaise, vomiting and headache. However, these features are ranked differently across the regional datasets. Subsequently, six predictive models, using important features selected by feature selection method, were used to evaluate the features performance. The features identified complies with malaria diagnosis and treatment guideline provided with WHO and Tanzania Mainland. The compliance is observed so as to produce a prediction model that will fit in the current health care provision system in Tanzania.

Supplier selection is an important part of supply chain management (SCM) for any organisation to achieve their objectives. The problem has attracted great interest from academics and practitioners. The selection process starts with determining the most important criteria out of a wide range. Many academic researchers apply multi-criteria decision-making (MCDM) techniques for supplier selection. However, the complexity of such approaches may increase significantly, especially when considering a large number of suppliers and selection criteria. This paper proposes an integrated approach combining machine learning classification with the Analytic Hierarchy Process (AHP) to select and evaluate the most suitable supplier. A Decision Tree (DT) classifier is used to select the most important criteria, instead of applying AHP on the complete set of criteria. The applicability of the approach is demonstrated using data from Libyan companies. Results show that decision trees can successfully lead to a most important subset of selection criteria, which would lead to a less complex application of AHP.

A previous study involving whole genome sequencing of the white shark suggested unique molecular evolution accounting for gigantism and the enhanced longevity of sharks including positive selection of dozens of protein-coding genes potentially involved in genome stability. We performed a reanalysis on some of the genes and identified serious flaws in their results. In this short article, we scrutinize one of the serious problems we identified, report other concerns, and point out a potential bias in analyzing iconic shark species in general.

The genetic variants identified by three large genome-wide association studies (GWAS) of educational attainment were used to test a polygenic selection model. Average frequencies of alleles with positive effect (polygenic scores or PS) were compared across populations (N=26) using data from 1000 Genomes. The PS of 161 GWAS significant SNPs in a recent meta-analysis was highly correlated to population IQ (r=0.863) and to the polygenic score of four alleles independently associated with general cognitive ability. High correlations with PISA scores for a subsample were observed.SNP p value predicted correlation to population IQ and factors from the two previous GWAS (r= -.25). Factor analysis produced similar estimates of selection pressure for educational attainment across the three datasets. Polygenic and factor scores computed using the top 20 significant SNPs showed very high correlation to population IQ (r=0.88; 0.9). Similar findings were obtained using 52 populations from another database (ALFRED). The results together constitute a replication of preliminary findings and provide strong evidence for recent diversifying polygenic selection on educational attainment and underlying cognitive ability.

Synechococcus sp. PCC 11901 reportedly demonstrates the highest, sustained growth of any known cyanobacterium under optimized conditions. Due to its recent discovery our knowledge of its biology, including the factors underlying sustained, fast growth, is limited. Furthermore, tools specific for genetic manipulation of PCC 11901 are not established. Here, we demonstrate that PCC 11901 shows faster growth than other model cyanobacteria, including the fast-growing species Synechococcus elongatus UTEX 2973, under optimal growth conditions for UTEX 2973. Comparative genomics between PCC 11901 and Synechocystis sp. PCC 6803 reveal conservation of most metabolic pathways but PCC 11901 has a simplified electron transport chain and reduced light-harvesting complex. This may underlie its superior light utilization, reduced photoinhibition and higher photosynthetic and respiratory rates. To aid biotechnology applications we developed a vitamin B12 auxotrophic mutant but were unable to generate unmarked knockouts using two negative selectable markers, suggesting that recombinase- or CRISPR-based approaches may be required for repeated genetic manipulation. Overall, this study establishes PCC 11901 as one of the most promising species currently available for cyanobacterial biotechnology and provides a useful set of bioinformatics tools and strains for advancing this field, in addition to insights into the factors underlying its fast growth phenotype.

Natural selection is commonly seen not just as an explanation for adaptive evolution, but as the inevitable consequence of “heritable variation in fitness among individuals”. Although it remains embedded in biological concepts, such a formalisation makes it tempting to explore whether this precondition may be met not only in life as we know it, but also in other physical systems. This would imply that these systems are subject to natural selection and may perhaps be investigated in a biological framework, where properties are typically examined in light of their putative functions. Here we relate the major questions that were debated during a three-day workshop[1] devoted to discussing whether natural selection may take place in non-living physical systems. We start this report with a brief overview of research fields dealing with “life-like” or “proto-biotic” systems, where mimicking evolution by natural selection in test tubes stands as a major objective. We contend the challenge may be as much conceptual as technical. Taking the problem from a physical angle, we then discuss the framework of dissipative structures. Although life is viewed in this context as a particular case within a larger ensemble of physical phenomena, this approach does not provide general principles from which natural selection could be derived. Turning back to evolutionary biology, we ask to what extent the most general formulations of the necessary conditions or signatures of natural selection may be applicable beyond biology. In our view, such a cross-disciplinary jump is in large part impeded by reliance on individuality as a central yet implicit and loosely defined concept. Overall, these discussions thus lead us to conjecture that understanding, in physico-chemical terms, how individuality emerges and how it can be recognised, will be essential in the search for instances of evolution by natural selection outside of living systems. [1] Natural Selection Beyond Life? Observing the physico-chemical world through Darwinian glasses; 12-15 November 2019 - Institut d'Etudes Scientifiques, Cargèse, France

The feeling of beauty is one of the positive human feelings. In architecture and design, it is associated with forms appropriate to their purpose. The usefulness of products to meet needs is also important. They are well received by users. The task of designers is to achieve perfection in their work. The rational design and use of artificial living environments are expressions of concern for natural resources (sustainable environment). These issues are addressed, i.e., by an interdisciplinary initiative of the New European Bauhaus. The aim of the paper is to expand knowledge about beauty and utility. They are important when choosing mass-produced goods. The article uses research methods: interpretive research, analyses, comparisons, logical argumentation, etc. Experimental qualitative research using a checklist was used. The results expand the knowledge on the selected topic and research scope of the research of the paper. The survey can be used to rationally evaluate architecture, interiors, and design facilities. It can be helpful for various aesthetic and functional strategies dedicated to different users. The paper demonstrates the role of beauty and functionality in the built environment. They are analyzed in the context of social and technical conditions, as well as contemporary sustainable design

This paper presents a study on the idle vibration reduction of a diesel sport utility vehicle (SUV). To reduce idle vibration, the transmission paths of vibration from the engine to the driver seat floor were investigated with the vehicle components related to idle vibration. Furthermore, operational deflection shape (ODS) tests were conducted to visualize the vibration shapes during engine idling. Experimental modal analyses were performed to obtain the natural frequencies and mode shapes. Through the ODS and modal tests, the vibration characteristics of the diesel SUV during idling were identified. Considering these vibration characteristics, a multi-body dynamic model for the diesel SUV described by differential equations of motion was established to evaluate the idle vibration. To implement the dynamic model effectively, the equivalent stiffnesses and damping coefficients included in the model were determined experimentally or analytically. The established dynamic model was verified by comparing the natural frequencies and idle vibration levels between simulations. Using this dynamic model, we analyzed the effects of various design variables on idle vibration and obtained an optimal design for reducing the idle vibration level. Finally, we present a design guide to reduce the idle vibration for diesel SUVs.

A framework for optimal sensor placement (OSP) for virtual sensing using the modal expansion technique and taking into account uncertainties is presented based on information and utility theory. The OSP maximizes a utility function that quantifies the expected information gained from the data for reducing the uncertainty of quantities of interest (QoI) predicted at the virtual sensing locations. The utility function is extended to make the OSP design robust to uncertainties in structural model and modelling error parameters, resulting in a multidimensional integral of the expected information gain over all possible values of the uncertain parameters and weighted by their assigned probability distributions. Approximate methods are used to compute the multidimensional integral and solve the optimization problem that arises. The Gaussian nature of the response QoI is exploited to derive useful and informative analytical expressions for the utility function. A thorough study of the effect of model, prediction and measurement errors and their uncertainties, as well as the prior uncertainties in the modal coordinates on the selection of the optimal sensor configuration is presented, highlighting the importance of accounting for robustness to errors and other uncertainties.

Economics has long sought to bridge the principles of microeconomics into the realm of macroeconomics. This paper presents a formal attempt to do so by using a maximum entropy based approach derived from statistical mechanics coupled with subjective game theory and elements from political philosophy. This approach is then applied to income distributions and to the Cobb-Douglas production function, to create a framework for future applications, and to illustrate where past work had made implicit assumptions regarding the system. The paper then explores the consequences of the approach, illustrating self-contradictions in the political philosophy of distributive justice, formally deriving an equation of state for the transactions on the Bitcoin network, and deriving from this the ideal gas law and polytropic process for an economy proving that expansionary monetary policy is not stimulative.

Adaptive mechanisms, such as channel hopping and packet replication, are used in low-power wireless networks to deal with the spatial and temporal variations in the link quality, and meet the reliability requirements of industrial applications (i.e., PDR>99%). However, the benefits of such mechanisms are limited and may have a large impact on end-to-end latency and energy consumption. Hence, in this paper we propose using adaptive modulation diversity, which allows to dynamically select different modulations, to improve link reliability. We present three adaptive modulation diversity selection strategies and validate them using the data derived from a real-world deployment using the IEEE 802.15.4g SUN modulations (i.e., SUN-FSK, SUN-OQPSK and SUN-ODFM) in an industrial environment. The results show that by using adaptive modulation diversity it is possible to improve link reliability regardless of node conditions.

In this article we present a deployment of 11 nodes using the three different SUN (Smart Utility Network) modulation schemes, as defined in the IEEE 802.15.4-2015 standard. The nodes were deployed in a 110.044 m2 warehouse for 99 days, and the resulting dataset contains a total of 10.710.868 measurements with RSSI (Received Signal Strength Indicator), CCA (Clear Channel Assessment) and PDR (Packet Delivery Ratio) values. The analyzed results show a high variability in average RSSI (i.e., between -82.1 dBm and -101.7 dBm) and CCA (i.e., between -111.2 dBm and -119.9 dBm) values, which are caused by the effects of multi-path propagation and external interference. Despite being above the sensitivity limit for each modulation, this values result in poor average PDR values (i.e., from 65.9% to 87.4%), indicating that additional schemes are required for low-power wireless communications to meet the dependability requirements of industrial applications. For that purpose, we also introduce the concept of modulation diversity, which can be combined with packet repetition to meet such requirements (i.e., PDR>99%) while minimizing the energy expenditure of nodes and meeting regulatory constraints.

The goal of our study was to identify signatures of selection in the Turopolje pigs and other commercial pig breeds. We conducted a comprehensive analysis of five datasets, including one local pig breed (Turopolje) and four commercial pig breeds (Large White, Landrace, Pietrain, and Duroc), using strict quality control measures. Our final dataset consisted of 485 individuals and 54,075 single nucleotide polymorphisms (SNPs). To detect selection signatures within these pig breeds, we utilized the XP-EHH and XP-nSL methodologies, which allowed us to identify candidate genes that have been subject to positive selection. Our analysis consistently highlighted the PTBP2 and DPYD genes as commonly targeted by selection across all studied breeds. Both of these genes are associated with muscular development in pigs and other species. Furthermore, in the Large White breed a number of genes were detected by the two methods, such as ATP1A1, CASQ2, CD2, IGSF3, MAB21L3, NHLH2, SLC22A15, VANGL1. In Duroc breed a different set of genes was detected, such as ARSB, BHMT, BHMT2, DMGDH, JMY. The function of these genes was related to body weight, production efficiency and meat quality, average daily gain and other similar traits. Overall, our results have identified a number of genomic regionsthat are under selective pressure between local and commercial pig breeds. This information can help to improve our understanding of the mechanisms underlying pig breeding, and ultimately contribute to the development of more efficient and sustainable pig production practices. Our study highlights the power of using multiple genomic methodologies to detect genetic signatures of selection, and provides important insights into the genetic diversity of pig breeds.

Solar concentration is the ability to harness solar radiation to increase the temperature of a receiver. The receiver is the component into which a heat transfer fluid can be flowed for an ORC system and produce electricity, or it can be used for high-temperature thermal storage, or even to implement thermochemical cycles. The choice of material is critical to ensure optimal performance and long-lasting operation. It is also essential that such material can operate at high temperatures and high thermal gradients. In short, material identification involves high thermal stresses that result in structural deformation. Different metal alloys were used to verify that the yield strength limit was not exceeded due to thermal stress induced by concentrated solar radiation. The problem was implemented in Matlab starting from the general heat equation. The purpose is to test whether thermal stress exceeds the yield strength, which is the condition in which elastic bonds in the material are changed, causing deformation. The best material identified was Inconel 740H, which had a high yield strength value and the lowest temperature difference. Under extreme working conditions, it can withstand thermally induced shocks.

Strawberry is produced in tropical regions using imported cultivars adapted to temperate and subtropical climates. These cultivars, under tropical conditions, produce below their genetic potential. Through multivariate analyses, the objective was to evaluate and select short-day strawberry genotypes based on intraspecific crosses, product characteristics, and fruit quality. The genotypes were obtained from the cross between 'Camino Real' (female parent) and the first-generation genotypes RVCA16, RVCS44, RVFS06, RVFS07, and RVDA11 (male parent), obtained in previous selections. The experimental design consisted of augmented blocks with standard controls, consisting of first-generation genotypes and commercial cultivars. The fruits were harvested and evaluated for productivity and post-harvest characteristics: Total Fruit Mass (MTF); Total Number of Fruits (TFN); Average Fruit Mass (AFM); Commercial Fruit Mass (CFM); Fruit Commercial Number (CFN); Average Commercial Mass of Fruits (ACFM); Total Soluble Solids (TSS); Firmness (F); Brightness (L); Hue Angle (°Hue); Chroma (C). The selection index of Mulamba and Mock (1978) was used with an intensity of 3% to obtain superior genotypes and submitted to multivariate analysis for comparative purposes. Of the 1500 genotypes evaluated, it was possible to select 44 genotypes with characteristics superior to the 13 controls. The RVDA11CR59 genotype showed better values for the attributes of interest, but the RVCS44CR population, from the cross between 'Camino Real' × RVCS44 ('Camarosa' × 'Sweet Charlie'), obtained the highest number (16) of individuals among those selected. Significant traits had high heritability but were not necessarily reflected in high selection gain. Coefficients of genetic variation were high, indicating sufficient genetic variability to select genotypes for these traits. When multivariate analyses were used, it was possible to group the selected genotypes into the same cluster according to the similarity and balance in the responses to the evaluated variables, demonstrating that these analyses help other parameters choose superior genotypes. The multivariate analysis allowed the selection of more balanced genotypes for production and post-harvest traits for tropical climates.

Online speech enhancement is of great interest, because of its diverse areas of application. The Demucs architecture has been recently shown to achieve a high level of performance, even with small audio segments, while requiring a relatively small amount of computational resources. However, an issue that it bears, as well as most of state-of-the-art speech enhancement techniques, is that of target speech selection: it is assumed that only one speech source is present. However, in real-life scenarios, more than one speech source may be present, in it is uncertain which speech source within the fed mixture should be enhanced. Thus, it is of interest to complement speech enhancement techniques (such as Demucs-based) with a target selection scheme, so as to ensure that only the source of interest is enhanced. In this work, two target selection strategies are explored: 1) an embedding-based strategy, using a codified sample of the target speech, and 2) a location-based strategy, using a beamforming-based pre-filter to select the target that is in front of a 2-microphone array. It is shown that while both strategies improve the performance of the Demucs-based technique when one or more speech interferences are present, they both have their pros and cons. Specifically, while the beamforming-based strategy achieves overall a better performance compared to the embedding-based strategy, it is sensitive against the location variation of the target speech source which the embedding-based strategy does not suffer from.

By applying a running average (with a window-size= d), we could transform Discrete data to broad-range, Continuous values. When we have more than 2 columns and one of them is containing data about the tags of classification (Class Column), we could compare and sort the features (Non-class Columns) based on the R2 coefficient of the regression for running averages. The parameters tuning could help us to select the best features (the non-class columns which have the best correlation with the Class Column). “Window size” and “Ordering” could be tuned to achieve the goal. this optimization problem is hard and we need an Algorithm (or Heuristics) for simplifying this tuning. We demonstrate a novel heuristics, Called Simulated Distillation (SimulaD), which could help us to gain a somehow good results with this optimization problem.

MicroRNA-155 (miR-155) expression promotes inflammatory responses in macrophages. Activation of macrophages with lipopolysaccharide (LPS) elevates miR-155, while the anti-inflammatory cytokine interleukin-10 (IL10) reduces miR-155 levels. MiR-155 exists in two forms, miR-155-5p and miR-155-3p, produced from the precursor of miR-155 (pre-miR-155). MiR-155-5p is the most abundant strand in activated macrophages, but in response to LPS, the miR-155-3p level is upregulated first, followed by miR-155-5p later. We have previously identified CELF2 protein which interacts with pre-miR-155 and impairs miR-155-5p expression. We now show that CELF2 only regulates the miR-155-5p expression and that another protein FUBP1 controls miR-155-3p levels in response to LPS and IL10.

Biologists seek to identify a small number of significant features that are important, non-redundant, and relevant from diverse omics data. For example, statistical methods like LIMMA and DEseq distinguish differentially expressed genes between a case and control group from the transcript profile. Researchers also apply various column subset selection algorithms on genomics datasets for a similar purpose. Unfortunately, genes selected by such statistical or machine learning methods are often highly co-regulated, making their performance inconsistent. Here, we introduce a novel feature selection algorithm that selects highly disease-related and non-redundant features from a diverse set of omics datasets. We successfully applied this algorithm to three different biological problems: a) disease to normal sample classification, b) multiclass classification of different disease samples, and c) disease subtypes detection. Considering classification ROC-AUC, False-positive, and False-negative rates, our algorithm outperformed other gene selection and differential expression (DE) methods for all six types of cancer datasets from TCGA considered here for binary and multiclass classification problems. Moreover, genes picked by our algorithm improved the disease subtyping accuracy for four different cancer types over the state-of-the-art methods. Hence, we posit that our proposed feature reduction method can support the community to solve various problems, including the selection of disease-specific biomarkers, precision medicine design, and disease sub-type detection.

Tests of selection based on population differentiation were performed on two highly polygenic traits important for success and quality of life: body height and educational attainment (EA). Polygenic scores (PGS) of EA and height, computed across three public genomic databases revealed differences between populations (1000 Genomes, HGDP, gnomAD) that matched the average IQ and height of ethnic groups (r ~0.9). A moderately strong correlation between latitude and EA PGS (r= 0.67) implies the effect of climate (seasonality or winter temperature) on selection for cognitive ability. The effect of latitude was reduced (β= 0.28) but remained significant after adding the sub-continental group variable to the regression model.The global Fst index revealed population differentiation at height and EA loci, significantly deviating from random SNPs. Substantial Linkage Equilibrium (LD) Decay between YRI and CEU was found (r= 0.6) but there was no correlation between LD decay and population differences in polygenic scores for EA (r= 0.015, p= 0.45), and slight inflation of height PGS difference due to LD decay (r= -0.04, p= 0.0315). Selecting the SNPs most robust to LD decay (r>0.8) resulted in larger PGS gaps for EA, but smaller for height. Finally, it is shown that PGS differences are more sensitive to the significance of GWAS loci than Fst, reflecting the major limitations of Fst as an index of selection.

In recent years, research has focused on generating mechanisms to assess the levels of subjects' cognitive workload when performing various activities that demand high concentration levels, such as driving a vehicle. These mechanisms have implemented several tools to analyze cognitive workload where the electroencephalographic (EEG) signals are the most used due to its high precision. However, one of the main challenges in the EEG signals implementing is finding the appropriate information to identify cognitive states. Here we show a new feature selection model for pattern recognition using information from EEG signals based on machine learning techniques called GALoRIS. GALoRIS combines Genetic Algorithms and Logistic Regression to create a new fitness function that identifies and selects the critical EEG features that contribute to recognizing high and low cognitive workload and structures a new dataset capable of optimizing the model's predictive process. We found that GALoRIS identifies data related to high and low cognitive workload of subjects while driving a vehicle using information extracted from multiple EEG signals, reducing the original dataset by more than 50%, maximizing the model's predictive capacity-achieving a precision rate greater than 90%.

A new form of coronavirus called severe acute respiratory disease coronavirus type 2 (SARS-CoV-2) is currently causing a pandemic. A six-month evolutionary history of SARS-CoV-2 is witnessed by characterising the total genome of 821 samples using comparative phylogenomic approaches. Our analyses produced striking inclusive results that may guide scientists/professionals for the past/future of pandemic. Phylogenetic and time estimation analyses suggest the proximate origin of pandemic strain as Guangdong and the origin time as first half of September 2019, not Wuhan and December 2019, respectively. The viral genome experienced a substitution rate similar to other RNA viruses, but it is particularly high in some of the peptides encoding sequences such as leader protein, E gene, orf8, orf10, nsp10, N gene, S gene and M gene and nsp4, while low in nsp11, orf7a, 3C-like proteinase, nsp9, nsp8 and endoRNase. Most strikingly, the divergence rate of amino acid sequences is high proportional to nucleotide divergence. Additionally, specific non-synonymous mutations in nsp3 and nsp6 evolved under positive selection. The exponential growth rate (r), doubling time (Td) and R₀ were estimated to be 47.43 per year, 5.39 days and 2.72, respectively. Comparison of synapomorphies distinguishing the SARS-CoV-2 and the candidate ancestor bat coronavirus indicates that mutation pattern in nsp3 and S gene enabled the new strain to invade human and become a pandemic strain. We arrive at the following main conclusions: (i) six months evolution of viral genome is nearly neutral, (ii) origin of pandemic is not Wuhan and predates formal reports, (iii) although viral population is ongoing an exponential growth, the doubling time is evolving towards shortening, and (iv) divergence rate of total genome is similar to other RNA viruses, but it is prominently high in some genes while low in some others and evolution in these genes should be closely monitored as their protein products intervening to pathogenicity, virulence and immune response.

Feature selection is one of the main data preprocessing steps in machine learning. Its goal is to reduce the number of features by removing extra and noisy features. Feature selection methods must consider the accuracy of classification algorithms while performing feature reduction on a dataset. Meta-heuristic algorithms are the most successful and promising methods for solving this issue. The symbiotic organisms search algorithm is one of the successful meta-heuristic algorithms which is inspired by the interaction of organisms in the nature called Parasitism Commensalism Mutualism. In this paper, three engulfing binary methods based on the symbiotic organisms search algorithm are presented for solving the feature selection problem. In the first and second methods, several S-shaped and V-shaped transfer functions are used for binarizing the symbiotic organisms search algorithm, respectively. These methods are called BSOSS and BSOSV. In the third method, two new operators called BMP and BCP are presented for binarizing the symbiotic organisms search algorithm. This method is called EBSOS. The third approach presents an advanced binary version of the coexistence search algorithm with two new operators, BMP and BCP, to solve the feature selection problem, named EBSOS. The proposed methods are run on 18 standard UCI datasets and compared to base and important meta-heuristic algorithms. The test results show that the EBSOS method has the best performance among the three proposed approaches for binarization of the coexistence search algorithm. Finally, the proposed EBSOS approach was compared to other meta-heuristic methods including the genetic algorithm, binary bat algorithm, binary particle swarm algorithm, binary flower pollination algorithm, binary grey wolf algorithm, binary dragonfly algorithm, and binary chaotic crow search algorithm. The results of different experiments showed that the proposed EBSOS approach has better performance compared to other methods in terms of feature count and accuracy criteria. Furthermore, the proposed EBSOS approach was practically evaluated on spam email detection in particular. The results of this experiment also verified the performance of the proposed EBSOS approach. In addition, the proposed EBSOS approach is particularly combined with the classifiers including SVM, KNN, NB and MLP to evaluate this method performance in the detection of spam emails. The obtained results showed that the proposed EBSOS approach has significantly improved the accuracy and speed of all the classifiers in spam email detection.

The practice of spatial econometrics revolves around a weighting matrix, which is often supplied by the user on previous knowledge. This is the so called $\mathbf{W}$ issue. Probably, the aprioristic approach is not the best solution although, nowadays, there few alternatives for the user. Our contribution focuses on the problem of selecting a $\mathbf{W}$ matrix from among a finite set of matrices, all of them considerer appropriate for the case. We develop a new and simple method based on the Entropy corresponding to the distribution of probability estimated for the data. Other alternatives, which are common in current applied work, are also reviewed. The paper includes a large Monte Carlo to calibrate the effectiveness of our approach compared to the others. A well-known case study is also included.

Background: The genetic variants identified by three large genome-wide association studies (GWAS) of educational attainment and the largest intelligence GWAS were used to test a polygenic selection model. Methods: Average frequencies of alleles with positive effect (polygenic scores or PS) were compared across populations (N=26) using data from 1000 Genomes. Factor analysis was used to extract a signal of polygenic selection. Results: A polygenic selection factor of educational attainment GWAS hits is high among a handful of SNPs within genomic regions replicated across GWAS publications and it is highly correlated to the genetic intelligence factor (r= 0.96). These factors are both highly predictive of average population IQ (r=0.9), and are robust to tests of spatial autocorrelation. Several Monte Carlo simulations yielded highly significant p values. Furthermore, the polygenic selection model shows high replicability, with the EA and intelligence factor scores being virtually identical to those from an older study (r=0.96-0.99). A larger sample of populations (N=53) produced similar results. Conclusion: This study shows robust results after accounting for spatial autocorrelation and Monte Carlo simulation using random SNPs and shows robust reproducibility of results from a previous study.

Day-ahead forecasting of electricity prices is important in deregulated electricity markets for all the stakeholders: energy wholesalers, traders, retailers, and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participate in predicting the price for each hour of a day. We propose two different strategies, namely, Fixed Weight Method (FWM) and Varying Weight Method (VWM), for selecting each hour's expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features derived from information such as past electricity price data, weather data, and calendar data. The proposed ensemble model offers better results than both the Pattern Sequence-based Forecasting (PSF) method and our own previous work using Artificial Neural Networks (ANN) alone do on the datasets for New York, Australian, and Spanish electricity markets.

(1) Background: AI technology has been deeply applied to online shopping platform to provide more accurate and personalized services for consumers. It is of great significance to study the different functional experience of AI for consumers to improve the current application status of AI technology.(2)Method: Based on the "S-O-R" model, this study divided the AI technology expe-rienced by the consumers of online shopping platform into accuracy, insight and interaction experience. Takes the perceived value as the mediating variable from the prospect of perceived utility value and perceived hedonic value. This article use empirical research method to analyze the effect of three dimensions of online shopping AI experience to research the internal influence mechanism of consumers purchase intention. (3) Results:① The accuracy, insight and interaction experience of AI marketing technology have a significant positive impact on consumers' per-ceived utility value and hedonic value respectively; ②Both of the perceived utility value and perceived hedonic value obtained by AI technology experience can promote the formation of consumers' purchase intention; ③ The perceived hedonic value was better than perceived utility value to promote the consumers' purchase intention; ④The results of multi group analysis show that some younger and less experiences consumers groups prefer the pleasure experience such as shopping desire stimulation, shopping process relaxation and pleasure that AI marketing brought. However, utilitarian value cannot promote this kind of consumers' purchase intention. (4) Con-clusions: Perceived utility value and perceived hedonic value can be the intermediary between AI technology and consumers' purchase intention.

Based on the Expected Utility Theory and Regret Theory, the Extended Regret Theory (ERT) is proposed in this paper to study the optimal pricing strategy of retailers in e-commerce environment. Taking the diversity of sales channels and the uncertainty of consumers in e-commerce environment into consideration, author of the paper designs an extended regret utility function which comprehensively considers both pessimistic and optimistic attitudes of decision makers in retailing industry to describe their regret-avoidance behavior. According to the sensitivity analysis, it is found that the optimal retail price decreases as the consumer price sensitivity coefficient increases, yet does not show variation with changes of the consumers pessimism degree. Moreover, the optimal retail price(s) obtained under EUT, ERT and combination of EUT and ERT represent the same.

The development of small scale hydroelectric power plants in Norway is determined by natural conditions, policies, attitudes and property rights. The owner of the river is the central decision maker. It is he who decides whether he will develop the power plant himself, Whether he wants to enter into a contract with an external investor and let him develop the power plant, whether he will sell his property rights or postpone the decisions. All available choices will involve risk. In order for him to make the best choice he must find the certainty equivalent to each of the choices and choose the one with the highest certainty equivalent. This is the first time the utility theory of John von Neumann and Oskar Morgenstern has been applied to decision makers in the hydro power industry in Norway.

Public goods are produced at all levels of the biological hierarchy, from the secretion of diffusible molecules by cells to social interactions in humans. However, the cooperation needed to produce public goods is vulnerable to exploitation by free-riders — the Tragedy of the Commons. The dominant solutions to this problem of collective action are that some form of positive assortment (due to kinship or spatial structure) or enforcement (reward and punishment) is necessary for public-goods cooperation to evolve and be maintained. However, these solutions are only needed when individual contributions to the public good accrue linearly, and the assumption of linearity is never true in biology. We explain how cooperation for nonlinear public goods is maintained endogenously and does not require positive-assortment or enforcement mechanisms, and we review the considerable empirical evidence for the existence and maintenance of nonlinear public goods in microbiology, cancer biology, and behavioral ecology. We argue that it is time to move beyond discussions about assortment and enforcement in the study of cooperation in biology.

To solve the problem of energy constraint and spectrum scarcity for cognitive radio wireless sensor networks (CR-WSNs), an underlay decode-and-forward relaying scheme is considered, where the energy constrained secondary source and relay nodes are capable of harvesting energy from a multi-antenna power beacon (PB) and using that harvested energy to forward the source information to the destination. Based on the time switching receiver architecture, three relaying protocols, namely, hybrid partial relay selection (H-PRS), conventional opportunistic relay selection (C-ORS), and best opportunistic relay selection (B-ORS) protocols are considered to enhance the end-to-end performance under the joint impact of maximal interference constraint and transceiver hardware impairments. For performance evaluation and comparison, we derive exact and asymptotic closed-form expressions of outage probability (OP) and throughput (TP) to provide significant insights into the impact of our proposed protocols on the system performance over Rayleigh fading channel. Finally, simulation results validate the theoretical results.

In this work, optimal consumption and investment strategies are derived for risk-averse investors under the 4/2 stochastic volatility class of models. We work under an expected utility (EUT) framework and consider a Constant Relative Risk Aversion (CRRA) investor, who might also be ambiguity-averse. The corresponding Hamilton-Jacobi-Bellman (HJB) and HJB-Isaacs (HJBI) equations are solved in closed-form for a subset of the parametric space and under some restrictions on the portfolio setting, for complete markets. Conditions for proper changes of measure and well defined solutions are provided. These are the first analytical solutions for the 4/2 stochastic volatility model and the embedded 3/2 model for the type of excess returns established in .

Food consumption in Europe is changing. Red meat consumption has been steadily decreasing in the past decades. The rising interest of consumers for healthier and more sustainable meat products provide red meat producers with the opportunity to differentiate their offers by ecolabels, origin and health claims. This international study analyses the European consumer preferences for red meat (beef, lamb and goat) in seven countries: Finland, France, Greece, Italy, Spain, Turkey and the United Kingdom. Through a choice experiment, 2.900 responses were collected. Mixed multinomial logit models were estimated to identify heterogeneous preferences among consumers at the country level. Results indicate substantial differences between the most relevant attributes for the average consumers, as well as their willingness to pay for them in each country. Nevertheless, national origin and organic labels were highly valued in most countries.

Distributed Systems architectures are becoming the standard computational model for processing and transportation of information, especially for Cloud Computing environments. The increase in demand for application processing and data management from enterprise and end-user workloads continues to move from a single-node client-server architecture to a distributed multitier design where data processing and transmission are segregated. Software development must considerer the orchestration required to provision its core components in order to deploy the services efficiently in many independent, loosely coupled - physically and virtually interconnected - data centers spread geographically, across the globe. This network routing challenge can be modeled as a variation of the Travelling Salesman Problem (TSP). This paper proposes a new optimization algorithm for optimum route selection using Algorithmic Information Theory. The Kelly criterion for a Shannon-Bernoulli process is used to generate a reliable quantitative algorithm to find a near optimal solution tour. The algorithm is then verified by comparing the results with heuristic solutions in 3 test cases. A statistical analysis is designed to measure the significance of the results between the algorithms and the entropy function can be derived from the distribution. The tested results shown an improvement in the solution quality by producing routes with smaller length and time requirements. The quality of the results proves the flexibility of the proposed algorithm for problems with different complexities without relying in nature-inspired models such as Genetic Algorithms and Simulated Annealing. This algorithm can be used by orchestration applications to deploy services across large cluster of nodes by making better decision in the route design.

Recently, the application of Artificial Intelligence (AI) in many areas of life has allowed raising the efficiency of systems and converting them into smart ones, especially in the field of energy. Integrating AI with power systems allows electrical grids to be smart enough to predict the future load, which is known as Intelligent Load Forecasting (ILF). Hence, suitable decisions for power system planning and operation procedures can be taken accordingly. Moreover, ILF can play a vital role in electrical demand response, which guarantees a reliable transitioning of power systems. This paper introduces a Perfect Load Forecasting Strategy (PLFS) for predicting future load in smart electrical grids based on AI techniques. The proposed PLFS consists of two sequential phases, which are; Data Preprocessing Phase (DPP) and Load Forecasting Phase (LFP). In the former phase, input electrical load dataset is prepared before the actual forecasting takes place through two essential tasks, namely feature selection and outlier rejection. Feature selec-tion is done using Advanced Leopard Seal Optimization (ALSO) as a new natural inspired opti-mization technique, Citation: To be added by editorial staff during production. Academic Editor: Firstname Last-name Received: date Revised: date Accepted: date Published: date Copyright: © 2023 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). While outlier rejection is accomplished through Interquartile Range (IQR) as a measure of statis-tical dispersion. On the other hand, actual load forecasting takes place in LFP using a new pre-dictor called; Weighted K-Nearest Neighbor (WKNN) algorithm. The proposed PLFS has been tested through excessive experiments. Results have shown that PLFS outperforms recent load forecasting techniques as it introduces the maximum prediction accuracy with the minimum root mean square error.

Ransomware constitutes a distinctive category of pernicious software that sequesters a user's digital assets by encryption, holding them hostage until a sum is extorted from the victim. These incursions have escalated to become among the most prevalent and significant threats confronting both individuals and corporate entities. In combatting this virulent program, dynamic analysis has been established as the favored detection modality. Such analyses typically hinge on Windows API calls, the conduits through which programs requisition services from the operating system. Yet, the superfluous and unrelated Windows API calls interjected by adversaries into the execution stream of suspect binaries precipitate an excessively noisy behavioral sequence, which impairs the performance of counter-ransomware mechanisms. The research outlined herein introduces a novel non-signature-based detection paradigm that harnesses efficacious Windows API call sequences through supervised machine learning strategies. An innovative Enhanced Min Max (EmRmR) filter technique is proposed, aiming to purge noisy features and isolate the most indicative feature subset that encapsulates the ransomware's true behavior. The EmRmR method, diverging from the traditional Min Max approach, circumvents the superfluous calculations that are a hallmark of the conventional algorithms, thereby necessitating a reduced number of evaluations. Additionally, a refinement procedure has been integrated to contract the program's call trace volume by discarding those Windows API calls lacking a robust correlation with ransomware's pivotal behavior. Subsequent to rigorous experimental analyses and juxtaposition with extant behavior-based detection methodologies, the proposed strategy has demonstrated its efficacy in differentiating ransomware behavior, delivering high detection precision alongside a diminution in false-positive occurrences.

Diverse candidate antibodies are needed to successfully identify therapeutic and diagnostic applications. The variable domain of IgNAR (VNAR), a shark single-domain antibody, has attracted attention owing to its favorable physicochemical properties. The phage display method used to screen for optimal VNARs loses sequence diversity because of the bias caused by the differential ease of protein expression in Escherichia coli. Here, we investigated a VNAR selection method that combined panning with various selection pressures and next-generation sequencing (NGS) analyses to obtain additional candidates. We constructed a VNAR phage library from the spleen of the Japanese topeshark (Hemitriakis japanica). Drawing inspiration from the physiological conditions of sharks and physicochemical properties of VNARs, we examined the effects of NaCl and urea concentrations, low temperature, and preheating at the binding step of panning. NGS analysis was performed to select the specifically enriched clones under each panning condition. The identified VNARs exhibited higher reactivity than those obtained by panning without selection pressure. Additionally, they possess physicochemical properties that reflect their respective selection pressures. These results can greatly enhance our understanding of VNAR properties and offer guidance for the screening of high-quality VNAR clones that are present at low frequencies.

Background: Pancreatic cancer (PC) is a disease with poor prognosis and survival rate. There is a pertinent need to identify the risk factors of this disease. The purpose of this study is to identify a subset of factors (a.k.a. features) as predictors of PC from the Prostate, Lung, Colorectal and Ovarian (PLCO) cancer dataset consisting of responses to 65 questions about demographics, cancer and health history, medication usage, and smoking habits from 154,897 participants. Method: There are two challenges to selecting the subset of features that predict PC with highest probability: the problem is computationally intractable, and the PLCO dataset is highly imbalanced. We use an innovative method to use the dataset in a balanced way, without involving up- or down-sampling. We use nine feature selection methods to select the optimal subset of features from the preprocessed and balanced dataset. Results: Our preprocessed dataset consists of 32 risk factors (8 demographics, 5 cancer history, 13 health history, 2 medication usage, 4 smoking habits). Risk factors belonging to cancer and health history, followed by smoking habits, were consistently chosen by the feature selection methods. We also discuss findings in the medical sciences literature that corroborate our findings. Conclusions: The study found that risk factors belonging to cancer and health history are the most prominent ones for PC. In particular, previously diagnosed with PC is chosen as the most prominent risk factor by majority of methods. While most of our findings are consistent with the literature, some of our findings shed light on novel factors that may not have received their due attention by the research community.

Feature selection is a crucial step in machine learning, aiming to identify the most relevant features in high-dimensional data, in order to reduce the computational complexity of model development and improve its generalization performance. Ensemble feature ranking methods combine the results of several feature selection techniques to identify a subset of the most relevant features for a given task. In many cases, they produce a more comprehensive ranking of features than the individual methods used in them. This paper presents a novel approach to ensemble feature ranking, which uses a weighted average of the individual ranking scores calculated by the individual methods. The optimal weights are determined using a Taguchi-type design of experiments. The proposed methodology significantly improves classification performance on the CSE-CIC-IDS2018 dataset, particularly for attack types where traditional average-based feature ranking score combinations resulted in low classification metrics.

In this study, machine learning was used to predict and analyze the behavior of occupants in Gifu City residences during winter. Global warming is currently progressing worldwide, and it is important to control greenhouse gas emissions from the perspective of adaptation and mitigation. Occupant behavior is highly individualized and must be analyzed to accurately determine a building's energy consumption. The accuracy of heating behavior prediction has been studied using three different methods: logistic regression, support vector machine (SVM), and deep neural network (DNN). The generalization ability of the support vector machine and the deep neural network was improved by parameter tuning. Parameter tuning of the SVM showed that the values of C and gamma affected the prediction accuracy. The prediction accuracy improved by approximately 11.9 %, confirming the effectiveness of parameter tuning on SVM. Parameter tuning of the DNN showed that the values of layer and neuron affected the prediction accuracy. Although parameter tuning also improved the prediction accuracy of DNN, and the rate of increase was lower than that of SVM.

Three soybean field trials were conducted in Indiana to evaluate the integration of seed treatment, cultivar selection, and seeding rate on sudden death syndrome (SDS) root infection, foliar symptoms, yield and net return. Two soybean cultivars, one moderately resistant and one susceptible to SDS were planted at three seeding rates (272,277 seeds/ha, 346,535 seeds/ha, and 420,792 seeds/ha). Fluopyram and pydiflumetofen seed treatments were applied to both cultivars and were compared to a control. Low foliar SDS disease pressure was observed in our study. Seed treatment of either fluopyram or pydiflumetofen, and use of a moderately resistant cultivar decreased root infection relative to the control and the use of a susceptible cultivar. Fluopyram significantly reduced root rot severity by 8.8% and increased yield by 105 kg/ha relative to the control, but was not different from pydiflumetofen. However, pydiflumetofen performed the same as the control with respect to root rot severity and yield. Findings from this study support the use of a seed treatment to protect roots from infection and the use of a moderately resistant cultivar planted at a seeding rate of 346,535 seeds/ha to protect yield and maximize on net returns when a field has low foliar SDS pressure.

We study the problem of predicting time series data using the autoregressive integrated moving average (ARIMA) model in an online manner. Existing algorithms require model selection, which is time consuming and inapt for the setting of online learning. Using adaptive online learning techniques, we develop algorithms for fitting ARIMA models with fewest possible hyperparameters. We analyse the regret bound of the proposed algorithms and examine their performance using experiments on both synthetic and real world datasets

Enhanced adoption of new cassava varieties is dependent on the ability to breed for acceptability attributes that define boiled roots and flour-based meals. This study assessed the role of pasting properties in defining farmers’ acceptability. Cassava accessions in a Triadic Comparison of Technologies (TRICOT) approach were assessed for acceptability in addition to moisture content root softness (boiled roots)and pasting properties (flour). Results showed location based differences in moisture content (13-17%) and boiled root softness (0.5-6.0 N/cm). Pasting properties such as peak viscosity (4300-5500 cP), breakdown (2204-3234 cP) and final viscosity (2953-3493) also varied significantly among accessions. Linear relationships were observed between consumer acceptance parameters and pasting properties {(final viscosity and root mealiness (r=0.7), ease of mingling (r=0.6), product stickiness (r=0.5) and overall performance of the flour based meal (r=0.6)}.Therefore pasting property analysis can differentiate cassava accessions and is an important tool in selection of consumer acceptable varieties.

Since December 2019, the world is fighting against coronavirus disease (COVID-19). This disease is caused by a novel coronavirus termed as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This work focuses on the applications of machine learning algorithms in the context of COVID-19. Firstly, regression analysis is performed to model the number of confirmed cases and death cases. Our experiments show that autoregressive integrated moving average (ARIMA) can reliably model the increase in the number of confirmed cases and can predict future cases. Secondly, a number of classifiers are used to predict whether a COVID-19 patient needs to be admitted to an intensive care unit (ICU) or semi-ICU. For this, classification algorithms are applied to a dataset having 5644 samples. Using this dataset, the most significant attributes are selected using features selection by ExtraTrees classifier, and Proteina C reativa (mg/dL) is found to be the highest-ranked feature. In our experiments, random forest, logistic regression, support vector machine, XGBoost, stacking and voting classifiers are applied to the top 10 selected attributes of the dataset. Results show that random forest and hard voting classifiers achieve the highest classification accuracy values near 98%, and the highest recall value of 98% in predicting the need for admission into ICU/semi ICU units.

In the last two decades, there have been massive advancements in high throughput technologies, which resulted in the exponential growth of public repositories of gene expression datasets for various phenotypes. It is possible to unravel biomarkers by comparing the gene expression levels under different conditions, such as disease vs. control, treated vs. not treated, drug A vs. drug B, etc. This problem refers to a well-studied problem in the machine learning domain, i.e., the feature selection problem. In biological data analysis, most of the computational feature selection methodologies were taken from other fields, without considering the nature of the biological data. For gene expression data analysis, most of the existing feature selection methods rely on expression values alone to select the genes; and biological knowledge is integrated at the end of the analysis in order to gain biological insights or to support the initial findings. Thus, integrative approaches that utilize the biological knowledge while performing feature selection are necessary for this kind of data. The main idea behind the integrative gene selection process is to generate a ranked list of genes considering both the statistical metrics that are applied to the gene expression data, and the biological background information which is provided as external datasets. Since the integrative approach attracted attention in the gene expression domain, lately the gene selection process shifted from being purely data-centric to more incorporative analysis with additional biological knowledge.

Estimation of breeding values through Best Linear Unbiased Prediction (BLUP) using pedigree-based kinship and Marker-Assisted Selection (MAS) are the two fundamental breeding methods used before and after the introduction of genetic markers, respectively. The emergence of high-density genome-wide markers has led to the development of two parallel series of approaches inspired by BLUP and MAS, which are collectively referred to as Genomic Selection (GS). The first series of GS methods alters pedigree-based BLUP by replacing pedigree-based kinship with marker-based kinship in a variety of ways, including weighting markers by their effects in genome-wide association study (GWAS), joining both pedigree and marker-based kinship together in a single-step BLUP, and substituting individuals with groups in a compressed BLUP. The second series of GS methods estimates the effects for all genetic markers simultaneously. For the second series methods, the marker effects are summed together regardless of their individual significance. Instead of fitting individuals as random effects like in the BLUP series, the second series fits markers as random effects. Differing assumptions regarding the underlying distribution of these marker effects have resulted in the development of many Bayesian-based GS methods. This review highlights critical concept developments for both of these series and explores ongoing GS developments in machine learning, multiple trait selection, and adaptation for hybrid breeding. Furthermore, considering the increasing use and variety of GS methods in plant breeding programs, this review addresses important concerns for future GS development and application, such as the use of GWAS-assisted GS, the long-term effectiveness of GS methods, and the valid assessment of prediction accuracy.

Disease classification based on biological data is an important area in bioinformatics and biomedical research. It helps the doctors and medical practitioners for the early detection of disease and support them as a computer-aided diagnostic tool for accurate diagnosis, prognosis, and treatment of disease. Earlier Microarray gene expression data have wide application for the classification of disease, but now Next-generation sequencing (NGS) has replaced the Microarray technology. From the last few years, RNA sequence (RNA-Seq) data are widely used for the transcriptomic analysis. Hence, RNA-Seq based classification of disease is in its infancy. In this article, we present a general framework for the classification of disease constructed on RNA-Seq data. This framework will guide the researchers to process RNA-Seq, extract relevant features and apply the appropriate classifier to classify any kind of disease.

The high sensitivity of chiroptical responses to conformational changes and supramolecular interactions has prompted an increasing interest in the development of chiroptical applications. However, prediction and understanding the chiroptical responses of the necessary large systems may not be affordable for calculations at high levels of theory. In order to facilitate the development of chiroptical applications, methodologies capable of evaluating the chiroptical responses of large systems are necessary. Exciton chirality method has been extensively used for the interaction between two independent chromophores through the Davydov model. For systems presenting C2 or D2 symmetry one can get to the same results by applying the selection rules. In the present article, analysis of the selection rules for systems with symmetries Cn and Dn with n = 3 and 4 is used to uncover the origin of their chiroptical responses. We hope that the use of the chiroptical symmetry analysis (CSA) for systems presenting the symmetries explored herein as well as for systems presenting higher symmetries will serve as a useful tool for the development of chiroptical applications.

For decades, sepsis research has been motivated by the idea of a dangerous overreaction of the immune system in sepsis. But is it true that the response to sepsis is dysregulated? This review surveys the history of sepsis trials and found that evidence for dysregulation does not exist in many of the physiologic mechanisms of sepsis. It is time to consider the alternative hypothesis, that sepsis traits are often functional, and do more harm than good. This review discusses the implications of this perspective for the future of sepsis research

In recent years, governments applied intelligent transportation system (ITS) technique to provide several convenience services (e.g., garbage truck app) for residents. This study proposes a garbage truck fleet management system (GTFMS) and data feature selection and data clustering methods for travel time prediction. A GTFMS includes mobile devices (MD), on-board units, fleet management server, and data analysis server (DAS). When user uses MD to request the arrival time of garbage truck, DAS can perform the procedure of data feature selection and data clustering methods to analyses travel time of garbage truck. The proposed methods can cluster the records of travel time and reduce variation for the improvement of travel time prediction. After predicting travel time and arrival time, the predicted information can be sent to user’s MD. In experimental environment, the results showed that the accuracies of previous method and proposed method are 16.73% and 85.97%, respectively. Therefore, the proposed data feature selection and data clustering methods can be used to predict stop-to-stop travel time of garbage truck.

Enabling D2D communication in the mm-Wave band has many obstacles which must be mitigated. The primary concern is the introduction of interference from various sources. Thus, we focused our work on the performance of decode and forward (DF) relay assisted D2D communication in the mm-Wave band for increasing the coverage probability and energy efficiency (EE). Three modes are proposed for D2D communication to prevail. Bit-wise binary XOR operation is executed at the relay node which increases the security feature. Radius of coverage expression is derived which indicates the switching of the modes. The diffused incoherent scattering power is also considered as a part of power consumption. Furthermore, a unique relay selection scheme, Dynamic relay selection (DRS) method is proposed to select the optimal relay for information exchange. Comparison of the proposed DF relay scheme with amplify and forward (DF) scheme is also shown. Finally, simulation results prove the efficacy of the proposed work.

Electricity demand forecasting has been a real challenge for power system scheduling in the different levels of the energy sectors. Various computational intelligence techniques and methodologies have been employed in the electricity market for load forecasting; although, scant evidence is available about the feasibility of each of these methods considering the type of data and other potential factors. This work introduces several scientific, technical rationale behind intelligent forecasting methods, based on the work of previous researchers in the field of energy. The fundamental benefits and main drawbacks of the aforementioned methods are discussed in order to depict the efficiency of each approach in various situations. In the end, a proposed hybrid strategy is represented.

It is desirable to upgrade previous evolutionary theories, which have remained incomplete and controversial for decades. Here we employ the concept of carbon-based entities (CBEs), which include methane, amino acids, proteins, organisms, and other entities containing relatively many carbon atoms. We deduce the driving force, mechanisms, steps, modes, tempos of CBE evolution, through integration of biology, physics, and chemistry using logics for complex issues. We hence establish the Carbon-Based Evolutionary Theory (CBET). The CBET suggests that evolution is the increase in hierarchy, diversity, fitness of CBEs under natural selection and driven by thermodynamics due to the chemical effect of the thermodynamic features of the Earth on CBEs. It provides better explanations for life origin, macroevolution events, natural selection, sympatric speciation, and evolution tempos than previous evolutionary theories. It reveals the evolutionary basis of multiple important social notions, including diversity, collaboration, altruism, obeying rules, and proper increase in freedom. It refutes some wrong notions in thermodynamics, including negative entropy (negentropy) and that biological order is equal to thermodynamic order, which have misled many people. The CBET is supported by its deduction and application. It could be a rare bridge linking laws of thermodynamics, evolution of life, and development of human society, and could have great significance in various sciences.

The Giant Babax, an endemic bird in China and a nationally protected wildlife species of Class Ⅱ, is primarily distributed in southern and southeastern Tibet. To investigate the factors influencing nesting site selection, this study conducted research on nest characteristics and nesting site preferences at four sample sites around Lhasa from March to October 2023y, using systematic search methods, tracking methods, and sampling methods. The research findings are as follows: (1) The average nest depth was (7.70±1.47)cm, nest height was (19.58±2.97)cm, inner diameter was (10.64±1.73)cm, outer diameter was (24.65±4.8)cm, average weight was (162.3±13.5)g, and average nest spacing within the plot was 396.93m. (2) The nests of Giant Babax were predominantly found on vegetation such as Salix longistamina (38%), Hippophae gyantsensis (20%), Rosa sericea Lindl. (14%), and Populus szechuanica Schneid(11%). They showed a preference for artificial secondary forests as nesting sites. (3) Giant Babax exhibited a preference for habitats with robust trees that had high canopies while being distant from houses and roads, but close to water sources; moreover, the canopy of the nesting tree played a crucial role in their choice of nesting sites. (4) The reproductive success rate reached 76%, with key factors affecting it being the canopy coverage of the nesting tree and the number of helpers. These research results provide fundamental data support for population conservation efforts and ecological resource management strategies concerning the Giant Babax.

Flooding, exacerbated by climate change, poses a significant threat to certain areas, increasing in frequency and severity. In response, the construction of supplementary dams has emerged as a reliable solution for flood management. This study employs a Remote Sensing (RS) approach integrated with GIS to assess the feasibility of constructing a supplementary dam near Linville, Brisbane, Australia, with the aim of mit-igating floods and preventing overtopping failure at the Wivenhoe Dam. Using QGIS software and a 25-meter resolution DEM from the Queensland Spatial Catalogue ‘QSpatial’ website, four potential dam sites were analysed, considering cross-sections, watershed characteristics, and water volume calculations. Systematic selection criteria were applied to identify the most optimal option based on dam wall di-mensions, volume-to-area, and volume-to-cost ratios. The selected option was further assessed against predefined criteria, yielding one optimal choice. The study provides insights into the feasibility and ef-fectiveness of supplementary dam construction for flood mitigation in the region, with recommendations for future research and implementation.