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.

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.

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).

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.

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.

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.

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.

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.

(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.

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.

This review examines the categorization of Essentially Derived Varieties (EDV) introduced in the 1991 revision of the Convention of the Union internationale pour la protection des obtentions végétales (UPOV). Challenges in the implementation of the concept and progress made on a crop-by-crop basis to provide greater clarity and more efficient implementation are reviewed. The current approach to EDV remains valid provided i) clarity on thresholds can be achieved including through resource intensive research on an individual crop species basis and ii) that threshold clarity does not lead to perverse incentives to avoid detection of essential derivation. However, technological advances leading to new varieties resulting from the simultaneous introduction or change in expression of more than “a few” genes will so challenge the concept to require a new Convention. Revision could include deletion of the concept of essential derivation and revision on a crop-by-crop basis of the breeder exception. Countries that allow utility patents for individual plant varieties per se should consider removing that possibility unless plant breeders utilize those encouragements for risk taking and investment to broaden the germplasm base upon which the long-term sustainability of plant breeding resides.

In view of intensified disasters and fatalities caused by natural phenomena and geographical expansion, there is a pressing need for a more effective environment logging for a better management and urban planning. This paper proposes a novel utility computing model (UCM) for structural health monitoring (SHM) that would enable dynamic planning of monitoring systems in an efficient and cost-effective manner. The proposed UCM consists of network-attached data drive that stores data from SHM logger, population count system and Geographic Information System (GIS) enhanced with a Cloud IoT data backup, display, and analysis server. The UCM using this data and data from building information systems applies a simple machine learning algorithm to generate real-time structure health and suggests re-planning of SHM units. The health of structure varies dynamically with disturbances created by higher occupancy and structure density per zone. The proposed SHM-UCM is unique in terms of its capability to manage heterogeneous SHM resources. This was tested in a case study on Qatar University (QU) in Doha Qatar, where it looked at where SHM nodes are distributed along with occupancy density in each building. This information was taken from QU simulated occupation and zone calculation models and then compared to ideal SHM system data. Results show the effectiveness of the proposed model in logging and dynamically planning SHM.