The real systems are often complex, nonlinear, and noisy in various areas including mathematics, natural science, and social science. We present the symplectic entropy (SymEn) measure as well as an analysis method based on SymEn to estimate the nonlinearity of the complex system by analyzing the given time series. The SymEn estimation is a kind of entropy based on symplectic principal component analysis (SPCA) which represent organized but unpredictable behaviors of systems. The key to SPCA is to preserve the global submanifold geometrical properties of the systems through symplectic transform in the phase space, which is a kind of the measure-preserving transforms. The capability of preserving the global geometrical characteristics makes the SymEn a test statistic to detect the nonlinear characteristics in several typical chaotic time series and the stochastic characteristic in the Gaussian white noise. The results are in agreement with findings in the approximate entropy (ApEn), the sample entropy (SampEn) and the fuzzy entropy (FuzzyEn). Moreover, the SymEn method is also used to analyze the nonlinearities of the real signals (including the EEG signals for ASD and healthy subjects, and the sound and vibration signals for the mechanical systems). The results indicate that the SymEn estimation can be taken as a measure for describing the nonlinear characteristics in the data collected from the natural complex systems.

DC residential distribution system (RDS) consisted by DC living home will be a significant integral part in the future green transmission. Meanwhile, the increasing number of distributed resources and intelligent devices will change the power flow between main grid and demand sides. The utilization of distributed generations (DGs) requires an economic operation, stability, environmentally friendly in the whole DC system. This paper not only presents an optimization schedule and transactive energy (TE) approach through centralized energy management system (CEMS), but a control approach to implement and ensure DG output voltages to various DC buses in DC RDS. Based on data collection, prediction and a certain objection, the expert system in CEMS can work out the optimization schedule, after this, the voltage droop control for steady voltage is aligned with the command of unit power schedule. In this work, a DC RDS is as a case study to demonstrate the process, the RDS is associated with unit economic models, cost minimization objective is proposed to achieve based on real-time electrical price. The results show that the proposed framework and methods will help the targeted DC residential system to reduce the total cost and reach stability and efficiency.

Inherent in every Active Magnetic Bearing (AMB) are differences between the expected geometric axes and the actual magnetic axes due to a combination of discrepancies, including physical variation from manufacturing tolerances and misalignment from mechanical assembly, fringing and leakage effects, as well as variations in magnetic material properties within a single AMB. A method is presented here for locating the magnetic axes of an AMB that will facilitate the accurate characterization of the bearing air gaps for potential improvement in field tuning, performance analyses and certain shaft force measurement techniques. This paper presents an extension of the application of the bias current perturbation method for the determination of the magnetic center [4] to the determination of magnetic axes for the further development of accurate current-based force measurement techniques [1].

Grid energy storage system for PV Applications is connected with three different power sources i.e. PV Array, Battery and the Grid. It is advisable to have Isolation between these three different sources to provide safety for the equipment. The configuration proposed in this paper provides the complete isolation between the three sources. A Power Balancing Control (PBC) for this configuration is proposed to operate the system in three different modes of operation. Control of a dual active bridge (DAB) based battery charger which provides a galvanic isolation between batteries and other sources is explained briefly. Various modes of operation of a Grid energy storage system are also presented in this paper. Hardware-In-Loop (HIL) Simulation is carried out to check the performance of the system and the PBC algorithm. Power circuit (comprises of inverter, dual active bridge based battery charger, grid, PV cell, batteries, contactors and switches) is simulated and the controller hardware and user interface panel are connected as HIL with the simulated power circuit through Real Time Digital Simulator (RTDS). HIL simulation results are presented to explain the control operation, steady state performance in different modes of operation and the dynamic response of the system.

Autonomous and cooperative vehicle systems represent a key priority in the automotive realm. Networked driving simulation can be utilized as a safe, cost-effective experimental replica of real traffic environments in order to support and accelerate the development of such systems. In networked driving simulation, different independent systems collaborate to achieve a common task: multi-driver traffic scenario simulation. Yet distinct system complexity levels are necessary to fulfill the requirements of various application scenarios, such as development of vehicle systems, analysis of driving behavior, and training of drivers. With myriad alternatives of available systems and components, developers of networked driving simulation are typically confronted with high design complexity. There are no systematic approaches to date for the design of networked driving simulation in accordance with the specific requirements of the concerned application scenarios. This paper presents a novel design method for networked driving simulation. The method consists mainly of a procedure model that is accompanied by a configuration software. The procedure model includes the necessary phases for the systematic design of application-oriented platforms for networked driving simulation. The configuration software embeds supportive decision-making processes that enable developers to apply the design method and easily create different system models. The design method was validated by generating system models and developing platforms of networked driving simulation for three different application scenarios.

NASA mission systems proposals are often compared using an equivalent system mass (ESM) framework wherein all elements of technology to deliver an effect- its components, operations and logistics of delivery- are converted to effective masses since this has a known cost scale in space operations. To date, ESM methods and the tools for system comparison have not considered complexities wherein systems that serve a mission span multiple transit and operations stages, such as would be required to support a crewed mission to Mars, and thus do not account for the different mass equivalency factors operational during each period and the inter-dependencies of the costs across the mission. Further, ESM does not account well for the differential reliabilities of the underlying technologies. Less reliability should incur an equivalent mass cost for technologies that might otherwise provide a mass advantage. We introduce an extensions to ESM to address these limitations and show that it provides a direct method for analyzing, optimizing and comparing different mission systems. We demonstrate our extended ESM (xESM) calculation with crop production technologies -- an aspect of the developing offworld biomanufacturing suite -- since it represents a case with strong coupling among stages of the mission and a relatively high-risk profile.

Spatio-temporal aspects of data lead to critical information. Sensors capture data at all scales continually so it is imperative that useful information be extracted ubiquitously and regularly. Location plays a vital part by helping understand relations between datasets. It is crucial to link developmental works with spatial attributes and current challenge is to create an open platform that manages real-time sensor data and provides critical spatial analytics atop expert domain knowledge provided in the system. That is a two-faced problem where the solution tackles not only data from multiple sources but also runs data management platform, a spatial data infrastructure(SDI) as backbone framework able to harness sensor web(SW). The paper proposes development of such a globally shared open spatial expert system(ES), SmaCiSENS, a first of a kind geo-enabled knowledge based(KB) ES for multiple fields, smarter cities to climate modeling. SmaCiSENS is integration of SW and SDI with domain KB on data and problems, ready to infer solutions. The paper describes an architecture for semantic enablement for SW, SDI; connect interfaces, functions of SDI and SW, and sensor data application program interfaces (APIs) to better manage climate modeling, geohazard, global changes, and other vital areas of attention and action.

Autophagy is a highly conserved catabolic mechanism that mediates the degradation of damaged cellular components by inducing their fusion with lysosomes. This process provides cells with an alternative source of energy for the synthesis of new proteins and the maintenance of metabolic homeostasis in stressful environments. Numerous studies have demonstrated beneficial roles for the induction as well as the suppression of autophagy in cancer cells. Autophagy may induce either survival or death depending on the cell/tissue type. Radiation therapy is widely used therapeutic option to treat cancer, and it induces autophagy in human cancer cell line. Also, melatonin seems to affect cancer cell death via regulation of programmed cell death. In this review, we summarize the current understanding of autophagy and its regulation in cancer.

The utilization for water resource has been of great concern to human life. To assess the natural water system in Kangding County, the integrated methods of hydrochemcial analysis, multivariate statistics and geochemical modelling were conducted on surface water, groundwater and thermal water samples. Surface water and groundwater were dominated by Ca-HCO₃ type, while thermal water belonged to Ca-HCO₃ and Na-Cl type. The analyzing results concluded the driving factors that affect hydrochemical components. Following the results of the combined assessments, hydrochemcial process was controlled by the dissolution of carbonate and silicate minerals with slight influence from anthropogenic activity. The mixing model of groundwater and thermal water was calculated using silica-enthalpy method, yielding cold-water fraction of 0.56-0.79 and estimated reservoir temperature of 130-199 ^oC, respectively. δD and δ¹⁸O isotopes suggested surface water, groundwater and thermal springs were of meteoric origin. Thermal water should have deep circulation through the Xianshuihe fault zone, while groundwater flows through secondary fractures where it recharges with thermal water. Those analytical results were used to construct a hydrological conceptual model, providing a better understanding of the natural water system in Kangding County.

This paper addresses a wildlife monitoring problem using a team of UAVs for efficient monitoring of wildlife. The state-of-the-art technology using UAVs has been an increasingly popular tool to monitor wildlife compared to the traditional methods such as satellite imagery-based sensing or GPS trackers. However, there still exist unsolved problems as to how the UAVs need to cover a spacious domain to detect animals as many as possible. In this paper, we propose the optimal transport-based wildlife monitoring strategy for a multi-UAV system, to prioritize monitoring areas while incorporating complementary information such as GPS trackers and satellite-based sensing. Through the proposed scheme, the UAVs can explore the large-size domain effectively and collaboratively with a given priority. The time-varying nature of wildlife due to their movements is modeled as a stochastic process, which is included in the proposed work to reflect the spatio-temporal evolution of their position estimation. In this way, the proposed monitoring plan can lead to efficient wildlife monitoring with a high detection rate. Various simulation results including statistical data are provided to validate the proposed work.

In this study, we present case studies to explore the dynamics of implementation and maintenance of health interventions. We analyze how specific interventions are built and eroded, how the building and erosion mechanisms are interconnected, and why we can see significantly different erosion rates across otherwise similar organizations. We use multiple comparative obesity prevention case studies to provide empirical information on the mechanisms of interest, and use qualitative systems modeling to integrate our evolving understanding into an internally consistent and transparent theory of the phenomenon. Our preliminary results identify reinforcing feedback mechanisms, including design of organizational processes, motivation of stakeholders, and communication among stakeholders, which influence implementation and maintenance of intervention components. Over time, these feedback mechanisms may drive a wedge between otherwise similar organizations, leading to distinct configurations of implementation and maintenance processes.

The research field of energy system analysis is dealing with increasingly complex energy systems and their respective challenges. Moreover, the requirement for open science has become a focal point of public interest. Both drivers have triggered the development of a broad range of (open) energy models and frameworks in recent years. However, there are hardly any approaches on how to evaluate these tools in terms of their capabilities to tackle energy system modelling challenges. This paper describes a first step towards a flexible evaluation of software to model energy systems. We propose a qualitative approach as an useful supplementary to existing model fact sheets and transparency checklists. We demonstrate the applicability by evaluating the newly developed “Open Energy Modelling Framework” with respect to existing challenges in energy system modelling. The case study results highlight that challenges related to complexity and scientific standards can be tackled to a large extent while the challenges of model utilization and interdisciplinary modelling are only tackled partially. However, the challenge of uncertainty remains for the most part unaddressed at present. Advantages of the evaluation approach lie in its simplicity, flexibility and transferability to other tools. Disadvantages mostly stem from its qualitative nature. Our analysis reveals that some challenges in the field of energy system modelling cannot be addressed by a software as they are on meta level like model result communication and interdisciplinary modelling.

The effect of prostaglandin E1 (PGE1) encapsulated in nanoparticles (Nano PGE1) on motor dysfunction and muscle atrophy induced by sciatic nerve injury (SNI) was investigated in rats, and was compared with PGE1 encapsulated in lipid microspheres (Lipo PGE1) or PGE1 clathrated in cyclodextrin (PGE1-CD). The hind limb muscle weight ratio decreased until 2 weeks after SNI. All 3 PGE1 formulations significantly improved SNI-induced motor dysfunction. Nano PGE1 significantly promoted recovery from muscle atrophy at 2 and 3 weeks after SNI. Lipo PGE1 was also effective, but multiple doses were required. Compared with the SNI control group, the Nano PGE1 group showed upregulation of vascular endothelial growth factor (VEGF) and agrin expression in the injured sciatic nerve and atrophic muscles. Nano PGE1 accumulated prominently at the site of nerve injury and persisted for longer than Lipo PGE1 or PGE1-CD. Expression of all EP receptors was detected in the normal sciatic nerve, and EP2 expression increased after SNI. Finally, Nano PGE1 promoted ERK1/2 and Akt phosphorylation. These findings suggest that PGE1 released from nanoparticles accumulates at sites of nerve injury and increases VEGF production by augmenting ERK1/2 phosphorylation via EP receptor signaling, thus promoting tissue repair and regeneration.

The ubiquity of mobile sensors (such as GPS, accelerometer and gyroscope) together with increasing computational power have enabled an easier access to contextual information, which proved its value in next generation of the recommender applications. The importance of contextual information has been recognized by researchers in many disciplines, such as ubiquitous and mobile computing, to filter the query results and provide recommendations based on different user status. A context-aware recommendation system (CoARS) provides a personalized service to each individual user, driven by his or her particular needs and interests at any location and anytime. Therefore, a contextual recommendation system changes in real time as a user’s circumstances changes. CoARS is one of the major applications that has been refined over the years due to the evolving geospatial techniques and big data management practices. In this paper, a CoARS is designed and implemented to combine the context information from smartphones’ sensors and user preferences to improve efficiency and usability of the recommendation. The proposed approach combines user’s context information (such as location, time, and transportation mode), personalized preferences (using individuals past behavior), and item-based recommendations (such as item’s ranking and type) to personally filter the item list. The context-aware methodology is based on preprocessing and filtering of raw data, context extraction and context reasoning. This study examined the application of such a system in recommending a suitable restaurant using both web-based and android platforms. The implemented system uses CoARS techniques to provide beneficial and accurate recommendations to the users. The capabilities of the system is evaluated successfully with recommendation experiment and usability test.

Background: Underreporting of Visceral Leishmaniasis (VL) in India remains a problem to public health controls. Effective and reliable surveillance systems are critical for monitoring disease outbreaks and public health control programs. However, in India, government surveillance systems are affected by levels of scarcity in resources and therefore, uncertainty surrounds the true incidence of asymptomatic and clinical cases, affecting morbidity and mortality rates. The State of Bihar alone contributes up to the 40\% of the worldwide VL cases. The inefficiency of surveillance systems occurs because of multiple reasons including delay in seeking health care, accessing non-authentic health care clinics, and existence of significant asymptomatic self healing infectious cases. This results in a failure of the system to adequately report true transmission rates and number of symptomatic cases that have sought medical advice (thus, high underreporting of cases). Objectives and Methods: There are several methods to estimate the extent of underreporting in the surveillance system. In this research, we use a mathematical dynamic model and two different types of data sets, namely, monthly incidence for 2003-2005 and yearly incidence from 2006-2012 from the Bihar's 21 most VL affected districts out of its 38 districts. The goals of the study are to estimate critical metrics to measure level of transmission and to evaluate the estimation process between the two data sets and 21 districts. In particularly, our focus is on (i) estimating infection transmission potential, underreporting level in incidence and proportion of self-healing cases, (ii) quantifying reproduction number of the$R_0$, and (iii) comparing underreporting incidence levels and proportion of self-healing cases between the two periods 2003-2005 and 2006-2012 and between 21 districts. Results: Our research suggests that the number of asymptomatic individuals in the population who eventually self-heal may have a significant effect on the dynamics of VL spread. The estimated mean self-healing proportion (out of all infected) is found to be $\sim 0.6$ with only 7 out of 21 affected districts having self-healing proportion less than $0.5$ for both data sets. The estimated mean underreporting level is at least $64$\% for the state of Bihar. The estimates of the basic reproduction numbers obtained are similar in magnitude for most of the districts, being in the range of (0.88, 2.79) and (0.98, 1.01) for 2003-2005 and 2006-2012, respectively. Conclusions: The estimates for the two types (monthly and yearly) of temporal data suggest that monthly data are better for estimation if less number of data points are available, however, in general, using such data set results in larger variances in parameters as compared to estimates obtained through aggregated yearly data. Estimated values of transmission related metrics are lower than those obtained from earlier analyses in the literature, and the implications of this for VL control are discussed. The spatial heterogeneity in these control metrics increases the risk of epidemics and makes the control strategies more complex.

Unmanned aerial vehicles (UAVs)-based communication system is a promising solution to meet coverage and capacity requirements of future wireless networks. However, UAV-enabled communications is constrained with its coverage, energy consumption, and flying regulations, and the number of works focusing on the sustainability aspect of UAV-assisted networking has been limited in the literature so far. In this paper, we propose a solution to this limitation; particularly, we design a $Q$-learning-based UAV positioning scheme for sustainable wireless connectivity considering key constraints, that are, altitude regulations, non-flight zones, and transmit power. The objective is to find the optimal position of the UAV base station (BS) and minimize the energy consumption while maximizing the number of users covered. Moreover, a weighting mechanism is developed, where the energy consumption and number of users covered can be prioritized according to network/battery conditions. The proposed Q-learning-based solution is compared to the baseline k-means clustering method, where the UAV BS is positioned at the centroid location that minimizes the cumulative distance between the UAV BS and the users. The results demonstrate that the proposed solution outperforms the baseline k-means clustering-based method in terms of the number of users covered while achieving the desired minimization of the energy consumption.

Urban forests are critical infrastructure for mitigating environmental and social challenges cities face. Municipalities and non-governmental entities, among others, often set goals (e.g., tree planting or canopy targets) to support urban forests and their benefits. We focus on canopy goals and develop conceptual underpinnings for an analysis of where additional canopy, as one important dimension of the urban forest, can fit within the landscape, while considering factors that influence where trees can be planted and where canopy can grow – ‘practical canopy.’ We apply this in New York City (NYC) to inform the setting of a canopy goal by the NYC Urban Forest Task Force (UFTF) for the NYC Urban Forest Agenda, which may trigger a virtuous cycle that supports the urban forest there. We further develop framing for a ‘priority canopy’ analysis to understand where urban forest expansion should be prioritized given more context (e.g., environmental hazards, local preferences), which can inform how expansion of the urban forest is achieved. We estimate an opportunity for 15,899 ha of new canopy in NYC given existing opportunities and constraints (practical canopy), which, if leveraged, could result in nearly doubling the canopy as of 2017 (17,253 ha). However, like existing canopy, practical canopy is not evenly distributed, in general, or across jurisdictions and land uses. Relying solely on areas identified as practical canopy to expand the urban forest would exacerbate inequities in its distribution. We discuss how the NYC UFTF established an aspirational but achievable goal of 30% canopy cover by 2035, which was informed by this analysis and guided by priorities of equity, health, and resilience. Achievement of this goal will ultimately require a combination of protecting and stewarding the existing resource, and leveraging opportunities for tree planting. Achieving a more equitable urban forest will also require identification of priority canopy, and, in cases, creation of new opportunities for tree planting and canopy expansion. Overall, the collaborative establishment of such goals based on local context can be instrumental in creating a virtuous cycle, moving conservation actors toward exercising influence and agency within the social ecological system.

The scarcity and pollution of freshwater are extremely crucial issues today and the expansion of water reuse have been considered as an option to reduce its impact. This study aims to assess the efficiency of an integrated greywater treatment system and hydroponic lettuce production as a part of a green wall structure and to evaluate the health risk associated with the production and consumption of lettuce through quantitative microbial risk assessment (QMRA) and chemical health risk assessment. The study was conducted based on the unique configuration of source separation system; on-site greywater treatment system; green wall structure as a polishing step; and hydroponic lettuce production in the green wall structure. The final effluent from the system was used to grow three lettuce varieties by adding urine as a nutrient solution. Both water samples and plant biomass were collected and tested for E. coli and heavy metals contamination. The system has gained a cumulative 5.1 log10 reduction of E. coli in the final effluent and no E. coli found in the plant biomass. QMRA results indicated that the system attained the health-based targets, 10–6 DALYs per person per year. Similarly, health risk index (HRI) and targeted hazard quotient (THQ) results did not exceed the permissible level, thus the chemical health risk concern was insignificant.

Background: Human Immnodeficiency virus (HIV) continues to take a heavy toll on the lives of many people with worst impact on health and wellbeing for the affected individuals in fragile states. The HIV situation in Somalia is not clearly known and experiences of the people living with HIV in this war-torn region unexpressed. This pilot qualitative study sought to explore the experiences of people living with diagnosed HIV in Mogadishu and their resilience in access to care and social support. Method: Face-to-face in-depth interviews were conducted in Somali in May 2013 among patients who were receiving Antiretroviral therapy (ART) from the HIV clinic in Mogadishu. Participants were recruited through drug dispensers at the HIV clinic in Benader Hospital. These were tape recorded, transcribed and translated for content analysis. Results: Three women and four men who were living with HIV shared the following narratives. Their perception was that they had either got HIV from their spouces or through health care contamination. They were very knowledgable about the realities of HIV, how the medication works, nutritional requirements and drug adherence. They were always willing to go an extra mile to secure a good life for themselves. However the external HIV stigma impacted their access to care. They faced challenges in their homes and at work which compelled them to seek support from non-governmental organisations (NGOs) or close family members. This stigma often affected their disclosure to the wider community due to the uncertainity of the repercussions, leading to a life of extreme loneliness and financial difficulties. The participants’ coping mechansms included living together and starting their own NGO for support with very strong optimism about their prognosis. Conclusions: The people living with diagnosed HIV in Mogadishu are highly knowledgeable about HIV transmission, the realities of living with diagnosed HIV infection and efficacy of HIV treatment. Our small sample suggests adequate access to ART through NGOs. However, widespread HIV stigma limits HIV status disclosure to the families and communities which creates a risk of self isolation and ill health. But affected individuals have developed resilient mechanisms of managing the risks. They strive to remain employed for economic security, adhere to HIV treatment, engage in support groups and maintain utmost optimism about their prognosis.

The anti-money laundering (AML) process has failed both in identifying suspicious cases in due time as in assisting the AML analysts in decision making. Starting from a new generic anti-fraud approach, this article presents the main aspects related to the development of a multi-agent system that goes beyond the capture of suspicious transactions, seeking to assist the human expert in the analysis of suspicious behaviour. First, a transactional behavioural profile of clients is obtained in a data mining process. A set of rules, obtained through data mining over a real database, in conjunction with specific rules based on legal aspects and in the expertise of the AML analysts make up the agents' knowledge base. The cases for which the system was unable to suggest a decision are flagged as requiring more detailed analysis. The system analysed 6 months of real transactions and indicated several suspicious profiles, a set of these suspects was investigated by the AML analysts who proved the suspicion of several cases, including some that had not been identified by the systems in execution.

Lean is one of the systematic approach to achieve higher value for organizations through eliminate non-value-added activities. It is an integrated set of tools, techniques, and principles designed to optimize cost, quality and delivery while improving safety. In Vietnam, industry waste management and treatment has become serious issue. The aim of this research is to present the effective of Lean application for industrial wastes collecting and delivery improvement. Through a case study, this paper showed the way of Lean tools and principles applied for wastes management and treatment such as Value Stream Mapping, Pull system, Visual Control, and Andon.... to get benefit on both economic and environment. In addition, the results introduced a good experience for Vietnamese enterprises in cost saving and sustainable development in waste management.

That how to improve the sensitivity and the detection ability of the Large-caliber sensor is critical to satisfy the requirement of heavy vehicle wear condition monitoring. In this paper, we introduced the LC resonance principle to the design of sensor with large flow channel (7mm). The resonant exciting coil increases the impedance change of the exciting circuit caused by wear particles which magnify the current difference between the two exciting coils and improve the sensitivity of the sensor. The resonant induction coil greatly suppresses the interference signals and magnifies the weak induced electromotive force which is beneficial to enhance the detection ability. In order to boost the detectability for different materials particles, a phase controlled variable-frequency exciting system (PCVFES) was adopted to automatically switch the exciting frequency between 284kHz for ferromagnetic particles and 420KHz for non-ferromagnetic particles. For the weak signal detection, based on the essential characteristic of the signal, we apply a simpler method that modified lock-in amplifier (MLIA) rather than the conventional algorithms (Wavelet transform, EMD). Results show that using these methods the sensitivity and the detection ability of the sensor are significantly improved and the 75μm iron particles and 220μm copper particles were successfully detected which realizes the initial abnormal wear monitoring for the large flow project.

Dermal interstitial fluid (ISF) is a novel source of biomarkers that can be considered as an alternative to blood sampling for disease diagnosis and treatment. Nevertheless, in vivo extraction and analysis of ISF are challenging. On the other hand, microneedle (MN) technology can address most of the challenges associated with dermal ISF extraction and is well-suited for long-term, continuous ISF monitoring as well as in situ detection. In this review, we first briefly summarise the different dermal ISF collection methods and compare them with MN methods. Next, we elaborate on the design considerations and biocompatibility of MNs. Subsequently, the fabrication technologies of various MNs used for dermal ISF extraction, including solid MNs, hollow MNs, porous MNs and hydrogel MNs, are thoroughly explained. In addition, different sensing mechanisms of ISF detection will be discussed in detail. Subsequently, we identify the challenges and propose the possible solutions associated with ISF extraction. A detailed investigation is provided for the transport and sampling mechanism of ISF in vivo. Also, the current in vitro skin model integrated with the MN arrays will be discussed. Finally, future directions to develop a point-of-care (POC) device to sample ISF are proposed.

The mind-body problem is the central issue in both of philosophy and life science for several centuries. To date, there is still no conclusive theory to interpret the relation between the mind and the body, even just the mind alone. Here, we promote a novel model, a derived mathematic equation called the entropic system equation, to describe the innate characters of the mind and the mechanism of the mind-body coupling phenomenon. As the semi-open thermodynamic systems far from equilibrium, the living organisms could be logically considered as an entropic system. In the living organisms or the entropic systems, there also are three essential existing elements including mass, energy and information, in which the mind and the body are hypothetically coupled by free energy and entropic force.

We have presented a controllable and human-readable polynomial neural network (CR-PNN) that is the first human-readable neural network. One can imagine its influence on system identification. Subsequently, we developed a relation spectrum in a medical application, which is likely to stand alongside the Fourier spectrum. However, the system analysis methodology is incomplete in contrast to signal processing methodology. Here, we presented the system filters for the first time. In this paper, we used the simulation system to verify the availability of the system analysis methodology. The system analysis methodology showed great properties in system identification and filter. The contribution of this paper is the system analysis methodology: transform method (CR-PNN), relation spectrum, and system filter design.

Objective: To evaluate the existing meta-analytic evidence of associations between exposure to ultra-processed foods, as defined by the Nova food classification system, and adverse health outcomes. Design: Systematic umbrella review of existing meta-analyses. Data Sources: MEDLINE, PsycINFO, Embase, and the Cochrane Database of Systematic Reviews, as well as manually searching reference lists from 2009 to June 2023. Inclusion Criteria: Systematic reviews and meta-analyses of cohort, case-control, and/or cross-sectional study designs that evaluated the associations between exposure to ultra-processed foods and adverse health outcomes in humans across the lifespan. Results: The search identified 45 unique pooled analyses, including 13 dose-response and 32 non-dose-response associations (n=9,888,373). Overall, direct associations were found between exposure to ultra-processed foods and 32 (71%) health outcome domains spanning cardiometabolic, gastrointestinal, and respiratory conditions, cancer, mental health, and mortality. Based on a pre-specified evidence classification criteria, convincing evidence (Class I) supported direct associations between greater ultra-processed food exposure and higher risks of incident cardiovascular disease-related mortality (RR: 1.50; 95%CIs: 1.37 to 1.63), type two diabetes (dose-response RR: 1.12; 95%CIs: 1.11 to 1.13), and depressive outcomes (HR: 1.21; 95%CIs: 1.16 to 1.28), as well as higher risks of prevalent anxiety outcomes (OR: 1.48; 95%CIs: 1.37 to 1.59) and combined common mental disorder outcomes (OR: 1.53; 95%CIs: 1.43 to 1.63). Highly suggestive (Class II) evidence indicated that greater exposure to ultra-processed foods was directly associated with higher risks of incident all-cause mortality (RR: 1.21; 95%CIs: 1.15 to 1.27), heart disease-related mortality (dose-response HR: 1.66; 95%CIs: 1.51 to 1.84), and type two diabetes (OR: 1.40; 95%CIs: 1.23 to 1.59), together with higher risks of prevalent obesity (OR: 1.55; 95%CIs: 1.36 to 1.77), adverse sleep-related outcomes (OR: 1.41; 95%CIs: 1.24 to 1.60), and wheezing (RR: 1.40; 95%CIs: 1.27 to 1.55). Out of the remaining 34 pooled analyses, 21 were graded as suggestive or weak strength (Class III-IV) and 13 were graded as no evidence (Class V). Using the GRADE framework, 22 pooled analyses were rated as "Low" quality, with 19 rated as "Very Low” and four rated as "Moderate" quality. Conclusions: Higher exposure to ultra-processed food was associated with a greater risk of adverse health outcomes, especially cardiometabolic, common mental disorder, and mortality outcomes. These findings provide a rationale to evaluate the effectiveness of using population-based measures to target and reduce dietary exposure to ultra-processed foods for improved human health. Systematic Review Registration: PROSPERO CRD42023412732.

The 21^st century has witnessed precipitous changes spanning from the way of life to the technologies that emerged. We have entered a nascent paradigm shift (industry 4.0) where science fictions have become science facts, and technology fusion is the main driver. Thus, ensuring that any advancement in technology reach and benefit all is the ideal opportunity for everyone. In this study, disruptive technologies of industry 4.0 was explored and quantified in terms of the number of their appearances in published literature. The study aimed at identifying industry 4.0 key technologies which have been ill-defined by previous researchers and to enumerate the required skills of industry 4.0. Comprehensive literature survey covering the field of engineering, production, and management was done from multidisciplinary databases: Google scholar, ScienceDirect, Scopus, Sage, Taylor & Francis and Emerald insight. Results of the electronic survey showed that 35 disruptive technologies were quantified and 13 key technologies: Internet of things, Big data, 3D printing, Cloud computing, Autonomous robots, Virtual and Augmented reality, Cyber physical system, Artificial intelligence, Smart sensors, Simulation, Nanotechnology, Drones and Biotechnology were identified. Both technical and personal skills to be imparted into the human workforce for industry 4.0 were reported. The study identified the need to investigate the capability and the readiness of developing countries in adapting industry 4.0 in terms of the changes in the education systems and industrial manufacturing settings. The study proposes the need to address integration of industry 4.0 concepts into the current education system.

One of the key areas of the International Renewable Energy Agency’s (IRENA) programme of work is the analysis of renewable technology costs and performance and the dissemination of these results to as wide an audience as possible. In addition to analysis, IRENA seeks to engage a broad range of stakeholders in the context of this work through the Renewable Costing Alliance. The Costing Alliance brings together companies, industry association, governments and researchers to share, confidentially, data for real-world renewable energy project cost and performance, helping to build further on IRENA’s costing work to date. In this context the authors analyse the current cost differentials and cost reduction potentials for solar PV in Japan compared to best practice levels and identify the reasons for higher costs in Japan and how to reduce them. This study analyses the current installed cost differentials for utility-scale, commercial and residential rooftop solar PV systems. In addition to identifying the reasons for the cost differentials, potential policy recommendations to reduce the cost differential are identified.

The paper is devoted to fundamental issues concerning to the ideology of the future sustainable development. Its implementation has available in the first place basing on the recognized framework concept of sustainable development (SD). At the same time, this concept requires serious improvement. As follows from the argumentation, a realistic design of institutional changes in connection with resource and organizational/behavioral changes needed for SD is called for to realize in the line of overall social system transformation. Its adequate understanding presupposes an exhaustive study of interconnected transformations in the main social fields as system processes taking into account the influence of relatively exogenous factors (technological, demographic and climate changes).According to the author, the transition to global sustainable social transformation is achievable. Main attention focuses on a number of possible fundamental shifts, the synergetic implementation of which will make such a transition possible.Proceeding from the arguments, the sustainable reproduction of the economy of a mature post-industrial type, including the green sectors, implies its stable and quiet growth, although the rapid growth of individual markets, especially innovative ones, may take place. The main findings also include the substantiation in favor of the choice of the national middle way model as the most appropriate for the corresponding sustainable system transformation of society.

Geological disposal facilities for radioactive waste pose significant challenges for robust monitoring of environmental conditions within the engineered barriers that surround the waste canister. Temperatures are elevated, due to the presence of heat generating waste, relative humidity varies from 20% to 100%, and swelling pressures within the bentonite barrier can typically be 2-10 MPa. Here, we test the robustness of a bespoke design MEMS sensor-based monitoring system, which we encapsulate in polyurethane resin. We place the sensor within an oedometer cell and show that despite a rise in swelling pressure to 2 MPa, our relative humidity (RH) measurements are unaffected. We then test the sensing system against a traditional RH sensor, using saturated bentonite with a range of RH values between 50% and 100%. Measurements differ, on average, by 2.87% RH, and are particularly far apart for values of RH greater than 98%. However, bespoke calibration of the MEMS sensing system using saturated solutions of known RH, reduces the measurement difference to an average of 1.97% RH, greatly increasing the accuracy for RH values close to 100%.

Lithium batteries and an increasing focus on CO₂ reduction have become an integral part of daily life and business for many people. Boron and boron compounds have been widely studied together in the history and development of lithium batteries. With a broad examination of battery components and systems but a boron-centric approach to raw materials, this review seeks to summarize past and recent studies on the following: which boron compounds are studied in lithium battery, in which parts of lithium batteries, what improvements are offered for battery performance, and what improvement mechanisms can be explained. The uniqueness of boron and its extensive application beyond batteries contextualizes the interesting similarity with studies on batteries. The paper predominantly focuses on lithium-ion batteries (LIBs) but also mentions other lithium batteries. At the end, the article aims to predict prospective trends for future studies that may lead to the successful and extensive use of boron compounds on a commercial scale.

This paper reviews the design and component of two types of RWHS, namely roof harvesting system (RHS) and pond harvesting system (PHS). The performance in terms of quantity and quality of collected rainwater and energy consumption for RWHS with different capacities were evaluated, as well as the benefits and challenges particularly in environmental, economic and social aspects. Presently, RHS is more commonly applied but its effectiveness is limited by its small scale. The PHS is of larger scale and has greater potentials and effectiveness as an alternative water supply system. Results also indicate the many advantages of PHS especially in terms of economics, environmental aspects and volume of water harvested. While RHS may be suited to individual or existing buildings, PHS has greater potentials and should be applied in newly developed urban areas with wet equatorial climate.

A dogma is normally considered as a principle or a belief accepted as an indisputable truth by some individuals and/or groups. Theoretically there can be no dogmas in science, but it has been demonstrated that scientific thought operates by conceptual changes. A dogma therefore can be understood as a concept present at the heart of some contemporary research programmes that need to be altered to overcome paradigms. Here we argue that two ideas relating to emergence of the biological system research need to be re-evaluated. First, is the idea that research programmes about the emergence of the biological system are the same as those of the origin of cells. Cells are strikingly important biological entities, hard core concepts for the entire field of biology. The emergence of the biological system happened much earlier than the origin of cells and thus the First Universal Common Ancestor (FUCA) should be viewed as a great-grandfather to the Last Universal Cellular Ancestor (LUCA); i.e. the latter is the first cellular life form. Second, RNA-world theories are the focus of mainstream research programmes for the origin of life stricto sensu. In the RNA-world view, self-replication of nucleic acids is seen as one of the most relevant events in the pre-biotic world. Without denying the relevance of self-replication, we argue here that the most germane event which occurred in the pre-biotic world was the crosstalk between nucleic acids and peptides. When these two macromolecules started to interact, the singularity that aggregated the complexity required to produce life emerged. Thus, comprehension of the early origins of the translation machinery and the assembly of the genetic code is key. Therefore, the relevance of cell theory and self-replication should be re-evaluated as well as the concept of life itself.

The rapid development of multicopters has led to many security problems. In order to prevent multicopters from invading restricted areas or famous buildings, an Anti-UAV Defense System (AUDS) has been developed and become a research topic of interest. Topics under research in relation to this include electromagnetic interference guns for unmanned aerial vehicles (UAVs), high-energy laser guns, US military net warheads, and AUDSs with net guns. However, these AUDSs use either manual aiming or expensive radar to track UAVs. This paper proposes a dual-axis rotary platform with UAV automatic tracking. The tracking platform uses visual image processing technology to track and lock the dynamic displacement of a UAV. When a target UAV is locked, the system uses a nine-axis attitude meter and laser rangefinders to measure its flight altitude and calculates its longitude and latitude coordinates through sphere coordinates to provide UAV monitoring for further defense or attack missions. Tracking tests of UAV flights in the air were carried out using a DJI MAVIC UAV at a height of 30 meters to 100 meters. It was set up for UAV image capture and visual recognition for tracking under various weather conditions by a thermal imager and a full-color camera respectively. When there was no cloud during the daytime, the images captured by the thermal imager and full-color camera provide a high-quality image recognition result. However, under dark weather, black clouds will emit radiant energy and seriously affect the capture of images by a thermal imager. When there is no cloud at night, the thermal imager performs well in UAV image capture. When the UAV is tracked and locked, the system can effectively obtain the flight altitude and longitude and latitude coordinate values.

As our understanding of the thalamocortical system deepens, the questions we face become more complex. Their investigation requires the adoption of novel experimental approaches complemented with increasingly sophisticated computational modeling. In this review, we take stock of current data and knowledge about the circuitry of the somatosensory thalamocortical loop in rodents, discussing common principles across modalities and species whenever appropriate. We review the different levels of organization, including the cells, synapses, neuroanatomy, and network connectivity. We provide a complete overview of this system that should be accessible for newcomers to this field while nevertheless being comprehensive enough to serve as a reference for seasoned neuroscientists and computational modelers studying the thalamocortical system. We further highlight key gaps in data and knowledge that constitute pressing targets for future experimental work. Filling these gaps would provide invaluable information for systematically unveiling how this system supports behavioral and cognitive processes.

This study describes the development of an automated bag valve mask (BVM) compression system, which, during acute shortages and supply chain disruptions can serve as a temporary emergency ventilator. The resuscitation system is based on the Arduino controller with a real-time operating system installed on a largely RepRap 3-D printable parametric component-based structure. The cost of the system is under $170, which makes it affordable for replication by makers around the world. The device provides a controlled breathing mode with tidal volumes from 100 to 800 milliliters, breathing rates from 5 to 40 breaths/minute, and inspiratory-to-expiratory ratio from 1:1 to 1:4. The system is designed for reliability and scalability of measurement circuits through the use of the serial peripheral interface and has the ability to connect additional hardware due to the object-oriented algorithmic approach. Experimental results demonstrate repeatability and accuracy exceeding human capabilities in BVM-based manual ventilation. Future work is necessary to further develop and test the system to make it acceptable for deployment outside of emergencies in clinical environments, however, the nature of the design is such that desired features are relatively easy to add with the test using protocols and parametric design files provided.

Pre-Western contact Hawaiʻi stands as a quintessential sustainability example of a large human population that practiced intensive agriculture, yet minimally displaced native habitats that comprised the foundation of its vitality. An explicit geospatial footprint of human-transformed areas across the pre-contact Hawaiian archipelago comprised less than 15% of total land area, yet provided 100% of human needs, supporting a thriving Polynesian society. A post-contact history of disruption of traditional Hawaiian land-use and its supplanting by Western land tenure and agriculture based on ranching, sugarcane, and pineapple, culminated in a landscape, in which over 50% of native habitats have been lost, while self-sufficiency has plummeted to 15% or less. Recapturing the ʻāina momona (productive lands) of ancient times can be accomplished through study of pre-contact agriculture, assessment of biological and ecological changes imposed on Hawaiian social-ecological systems, and conscious planned efforts to increase self-sufficiency and reduce importation. Impediments include the current tourism-based economy, competition from habitat-modifying introduced species, a suite of agricultural pests severely limiting traditional agriculture, and changes in climate rendering some pre-contact agricultural centers suboptimal. Modified agricultural methods will be required to counteract these limitations, and diversified agriculture to broaden the production base, without contributing to further degradation of native habitats.

A new approach to the measurement with elastic lidar of depolarization produced by atmospheric aerosols is presented. The system uses two different telescopes: one for depolarization measurements and another for total-power measurements. The system architecture and principle of operation are described. The first experimental results are also presented, corresponding to a collection of atmospheric conditions over the city of Barcelona.

Integration of battery energy storage in photovoltaic (PV) systems can reduce the electricity costs and provide desirable flexibility and reliability to these systems decreasing renewable energy fluctuations. This paper presents a review of the PV-battery application in Brazil, highlighting the challenges and prospects based on the state-of-art. A PV-battery systems description is presented in this work, as well as the most applied battery technology and its comparison. The paper also describes the set of applications such as voltage and frequency regulation, renewable energy integration, power quality, etc. In the Brazilian scenario, there are applications of PV-battery systems, most of them part of research and development projects (R&D’s), and some real cases are shown, including its goals, applied equipment, operation modes, strategies, and perspectives. Additionally, this work evaluates the Brazilian scenario regarding the energy storage systems implementation challenges, such as regulatory barriers, business models, and opportunities for R&D in the energy market. In conclusion, it is need develop proper regulatory models to expand PV-battery systems and make them visible to the agents in the electricity sector.

An effective battery management system (BMS) is indispensable for any lithium-ion battery (LIB) powered systems such as electric vehicles (EVs) and stationary grid-tied energy storage systems. Massive wire harness, scalability issue, physical failure of wiring, and high implementation cost and weight are some of the major issues in conventional wired-BMS. One of the promising solutions researchers have come up with is the wireless BMS (WBMS) architecture. Despite research and development on WBMS got momentum more than a decade ago, it is still in a preliminary stage. Significant further upgradation is still required towards developing an industry-ready WBMS, especially for high-power LIB packs. Therefore, an in-depth survey exclusively on WBMS architectures is presented in this article. The aim is to provide a summary of the existing developments as well as to present an informative guide to the research community for future development by highlighting the issues, emerging trends, and challenges. In-depth analysis of the existing WBMS topologies will not only help the researchers to understand the existing challenges and future research scopes clearly while at the same time enthuse them to focus their research inclination in the domain of WBMS.

The world has been highly impacted by the COVID-19 as the virus has spread to all continents – about 200 countries in total. The latest update claims about 4,000,000 confirmed cases and about 300,000 confirmed deaths owing to the COVID-19 pandemic. This probably makes the COVID-19 as the most dangerous contagious disease in the era 2000s. Apart from massive publications on this topic, there is no available qualitative analysis that describes the dynamic spreads of the COVID-19 and its impacts on healthcare and the economy. Through the system archetypes analysis, this paper explains that the dynamic spread of the COVID-19 consists of the limits to growth and the success to successful structures. The limits to growth elucidates that more symptomatic and asymptomatic patients owing to infected droplets may be bounded by self-healing and isolated treatments. The success to successful structure explains that once the COVID-19 affects the economy through the lockdown, there will be a limited fund to support the government aids and the aggregate demand. In overall, this paper gives readers simplified holistic insights into understanding the dynamic spread of the COVID-19.

The identification of bloodstain is one of the most important approaches in obtaining evidence in criminalistics. A threshold method based on spectral co-efficient and interclass variance is proposed in this paper, it is a non-contact, non-destructive method for quickly identifying bloodstains. The spectra of bloodstains and other suspected substances were all extracted from their hyper-spectral image. Then calculate the correlation coefficients of these spectral and interclass variances, analyze the differences between substances. The best blood recognition threshold was determined as 0.9. After preprocessing for eliminating systematic errors, experiments with the threshold 0.9 are carried out to identify bloodstains on the calico and red T-shirt. The method can remarkably identify the bloodstain from other non-blood substances both quickly and efficiently. The blood extraction rate can reach to 93.35% and 89.19%, respectively. It is an important step toward the implementation of bloodstain non-contact and non-destructive identification in forensic casework.

Background: The endocannabinoid system (ECS) is a very heterogeneous array consisting of many proteins like ligands, enzymes and receptors synthetized in various tissues and immunity cells. The main endogenous ligands are unsaturated fatty acid derivatives like anandamide(AEA), 2-arachidonoylglycerol(2-AG), but many others are under study. Endocannabinoids are involved in both physiological and pathological conditions. ECS plays an important role in the regulation of main processes which lead to cancer and also in sex steroid hormone-related cancers. Methods: With focus on gynaecological cancers, main papers and review articles, up to April 2018, on the role of the ECS, were acquired by PubMed searches using the search terms: ‘cancer’, ‘cannabinoid’, ‘endocannabinoid’, ‘gynaecology’ and ‘malignancy’. Results: The review of recent literature data showed the involvement of the endocannabinoid system in numerous physiological and pathological conditions of the female genital tract up to the development of gynaecological malignancy as cervical, endometrial and ovarian cancer. Conclusions: The endocannabinoid system has an important role in antitumor actions involving different signalling receptor and receptor-independent pathways. It represents an exciting challenge to researchers for its potential use in diagnosis and treatment of all gynaecological malignancies

Among all the active confinement techniques, the use of pre-tensioned stainless steel straps has recently gained much attention. The flexibility of the stainless steel straps allows us to bend and pass them through the thickness of the masonry, thus creating a three-dimensional strengthening system between the two opposite facings. The use of the same perforation for the passage of several straps closed in a loop generates a continuous strengthening system that prevents parts of the structure from falling and injuring the occupants during seismic events. However, the perforations can nullify the in-plane strengthening, as they act as cylindrical hinges and make the reinforcement system labile for certain strap arrangements. Diagonal compression tests on square masonry panels performed in the present study show that the straps improve neither strength nor ductility when running along the mortar head and bed joints, arranged in square meshes. Conversely, they improve both strength and ductility when the straps make angles of 45° with the mortar joints. Furthermore, the experimental results show that the straps exert an anisotropic effect that decreases the apparent in-plane Poisson ratio. They also provide new insights into the diagonal compression test and allow formulating a new proposal for the pseudo-ductility factor.

We examined whether the ambient illuminance during extended wakefulness modulates the homeostatic increase in human deep sleep [i.e. slow wave sleep (SWS) and electroencephalographic (EEG) slow-wave activity (SWA)] in healthy young and older volunteers. Thirty-eight young and older participants underwent 40 hours of extended wakefulness [i.e. sleep deprivation (SD)] once under dim light (DL: 8 lux, 2800K), and once under either white light (WL: 250 lux, 2800K) or blue-enriched white light (BL: 250 lux, 9000K) exposure. Subjective sleepiness was assessed hourly and polysomnography was quantified during the baseline night prior to the 40-h SD and during the subsequent recovery night. Both the young and older participants responded with a higher homeostatic sleep response to 40-h SD after WL and BL than after DL. This was indexed by a significantly faster intra-night accumulation of SWS and a significantly higher response in relative EEG SWA during the recovery night after WL and BL than after DL for both age groups. No significant differences were observed between the WL and BL condition for these two particular SWS and SWA measures. Subjective sleepiness ratings during the 40-h SD were significantly reduced under both WL and BL compared to DL, but were not significantly associated with markers of sleep homeostasis in both age groups. Our data indicate that not only the duration of prior wakefulness, but also the experienced illuminance during wakefulness affects homeostatic sleep regulation in humans. Thus, working extended hours under low illuminance may negatively impact subsequent sleep intensity in humans.

Smart grid software interconnects multiple Engineering disciplines (power systems, communication, software and hardware technology, instrumentation, big data, etc.). The software architecture is an evolving concept in smart grid systems in which systematic architecture development is a challenging process. The architecture has to realize the complex legacy power grid systems and cope up with current Information and Communication Technologies (ICT). The distributed generation in smart grid environment expects the software architecture to be distributed and to enable local control. Smart grid architecture should also be modular, flexible and adaptable to technology upgrades. In this paper, the authors have made a comprehensive review on architecture for smart grids. An in depth analysis of layered and agent based architectures is presented and compared under various domains.

ABSTRACT Ethiopia is a developing country, where majority of the population lives in rural areas without access to electricity. 83% of the total population of the country use traditional biomass energy as a basic source of energy. In contrast, the country is endowed with sufficient renewable energy resources which can be used as a standalone electric energy supply system for electrifying remote areas of the country. These resources are mainly micro hydropower and wind which can be used individually or the best combination of one another. The application of hybrid renewable energy system has become an important alternative solution for rural electrification program. The Modeling and control of a hybrid PV-Wind-Hydro DG system is also addressed. Dynamic models for the major system components, namely, wind energy conversion system, PV energy conversion system, hydro, inverter, and overall fuzzy logic controller units are developed. Then, a simulation model for the proposed hybrid power system has been developed using MATLAB /Simulink environment. This is done by creating subsystem sets of the major dynamic component models and then assembling into a single aggregate model. The overall power management strategy for coordinating and/or controlling the different energy sources is also presented in the thesis work. Generally there are 800 households with total electric demand of 71.6KW.To satisfy this demand 52%, 35% and 13% is to be contributed from wind/hydro/solar respectively. To use the power economically fuzzy logic controller is used. The controller monitors the demand and the available sources and then switches to the appropriate power supply according to the written rules. Simulations have been carried out to verify the system dynamic performance using a practical load profile and weather data. The result shows that the overall power management strategy is effective and the load demand is balanced. To complete this work, a grid extension from the closest substation has been compared with hybrid system. Cost of the grid extension is estimated based on the data obtained from EEP office. This is done in order to compare the cost of the designed hybrid power system against the cost of grid extension. The result shows that breakeven grid extension distance to be 23.9km which indicates that grid extension is preferable.

The interest on the use of renewable energy resources is increasing, especially towards wind and hydro powers, which should be efficiently converted into electric energy via suitable technology tools. To this aim, data--driven control techniques represent viable strategies that can be employed for this purpose, due to the features of these nonlinear dynamic processes working over a wide range of operating conditions, driven by stochastic inputs, excitations and disturbances. Some of the considered methods, such as fuzzy and adaptive self--tuning controllers, were already verified on wind turbine systems, and similar advantages may thus derive from their appropriate implementation and application to hydroelectric plants. These issues represent the key features of the work, which provides some guidelines on the design and the application of these control strategies to these energy conversion systems. The working conditions of these systems will be also taken into account in order to highlight the reliability and robustness characteristics of the developed control strategies, especially interesting for remote and relatively inaccessible location of many installations.

Without protective and/or therapeutic agents the SARS-CoV-2 infection known as coronavirus disease 2019 (COVID-19) is quickly spreading worldwide. It has surprising transmissibility potential, since it could infect all ages, gender, and human sectors. It attacks respiratory, gastrointestinal, urinary, hepatic, and endovascular systems and can reach the peripheral nervous system (PNS) and central nervous system (CNS) through known and unknown mechanisms. The reports on the neurological manifestations and complications of the SARS-CoV-2 infection are increasing exponentially. Herein, we enumerate seven candidate routes, which the mature or immature SARS-CoV-2 components could use to reach the CNS and PNS, utilizing the within-body crosstalk between organs. The majority of SARS-CoV-2 infected patients suffer from some neurological manifestations (e.g., confusion, anosmia, and ageusia). It seems that although the mature virus did not reach the CNS or PNS of the majority of patients, its unassembled components and/or the accompanying immune-mediated responses may be responsible for the observed neurological symptoms. The viral particles and/or its components have been specifically documented in endothelial cells of lung, kidney, skin, and CNS. This means that the blood-endothelial-barrier may be considered as the main route for SARS-CoV-2 entry into the nervous system, with the barrier disruption being more logical than barrier permeability, as evidenced by postmortem analyses.

This article offers an overview of congenital and acquired vascular anomalies involving the portal venous system in dogs and cats, as determined by multidetector-row computed tomography angiography. Congenital absence of the portal vein, portal vein hypoplasia, portal vein thrombosis and portal collaterals are described. Portal collaterals are further discussed as high- and low-flow connections, and categorized in hepatic arterioportal malformation, arteriovenous fistula, end-to-side and side-to-side congenital portosystemic shunts, acquired portosystemic shunts, cavoportal and porto-portal collaterals. Knowledge of different portal system anomalies helps understand the underlying physiopathological mechanism and is essential for surgical and interventional approaches.

This review summarizes the use of CRISPR system in yeasts, identifying advantages and disadvantages of its applications. 39 articles were evaluated including 12 articles that discussed the advantages of new CRISPR systems that improved the initial system, and another 27 were evaluated, among these: three were applications in Cryptococcus neoformans, four in candida sp., three in Schizosaccharomyces pombe, nine in Saccharomyces cerevisiae, four in Yarrowia lipolytica, and four in industrially important yeasts such as Pichia pastoris and Saccharomyces pastorianus. It was concluded that the CRISPR system is one of the most versatile genetic editing systems available nowadays. It can be applied in different organisms for several effects including gene knock-outs, performing point mutations, gene expression, or even applying multiple edition operations in several genes. However, we recognize that numerous studies lack a control group of the mutated strains, which leaves many questions unanswered. For instance, the extent and precision of this techniques, it also represents a risk to biosecurity standards. Therefore, this review shows the compilation of CRISPR system information, which could be used to generate different alternatives in the industry and clinical fields.

Emergency control load-shedding is a key solution to prevent blackouts in the power system. This paper proposed a new model of emergency controls load shedding based on the fast identification of the unstable state of the power system. K-means clustering algorithm divided the instability mode into the clusters. The results of analysis of this cluster were used as the basis for classification control. Building load shedding strategies is consisted of the pre-designed rules based on AHP algorithm. When the recognition of the power system “instability” is detected, the signal of load shedding control is triggered immediately, therefore the decision time is greatly shortened comparing to the traditional methods. The effectiveness of the proposed method was tested on the IEEE 39-bus to overcome the limitations of the last traditional methods.

In any region of a space, the gravitational field cannot be eliminated. The speed of light in a vacuum has never been observed and cannot be observed with current technology. Till now, only the speed of light in a gravitational field has been observed. It results in that all the observed speed of light are always being varied by the gravity. Here, it is presented that light could be dispersion in a gravitational field analogous to the dispersion of light in the Newtonian prism experiment. The relativistic mass density on the surface of a neutron star is on the level of 10¹⁷kg/m³ while on the surface of the Earth is only 10^-7kg/m³, the effect of the gravitational field acting on the speed of light by a neutron star is much larger than that by the Earth. Therefore, the dispersion of light in strong gravitational field could be generally observed from the image of a star and it should have been observed through the double-image gravitational lensing and the spectroscopic binary system. And, it should affect the observed numbers, spectrogram, size and distance of the celestial objects.

The Arctic freshwater budget is critical for understanding the climate in the northern regions. However, the hydrology of the Arctic circumpolar tundra region (ACTR) and the largest pan-Arctic rivers are still not well understood. In the present paper, we analyze the spatiotemporal variations in terrestrial water storage (TWS) of the ACTR, including three of its largest pan-Arctic river basins (Lena, Mackenzie, Yukon), using monthly Gravity Recovery and Climate Experiment (GRACE) data from 2002 to 2016. Together with global land reanalysis, river runoff, and inundation extent area (IEA) data, we identify declining TWS trends throughout the ACTR that we attribute largely to increasing evapotranspiration driven by increasing summer air temperatures. In terms of regional changes, large and significant negative trends in TWS are observed mainly over the North American continent. At basin scale, we show that, in the Lena River basin, the autumnal TWS signal persists until the winter of the following year, while in the Mackenzie River basin, the TWS levels in the autumn and winter has no significant impact on the following year. As global warming is expected to be particularly significant in the northern regions, our results are important for understanding future TWS trends, with possible further decline.

Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus SARS-CoV-2, has been confirmed in over 10,000,000 individuals worldwide and has resulted in more than 500,000 deaths in a few months since it first surfaced. With such a rapid spread it is no surprise that there has been a massive effort around the world to collectively elucidate the mechanism by which the virus is transmitted. Despite this, there is still no definitive consensus regarding droplet versus airborne transmission of SARS-CoV-2. Public health officials around the world have introduced guidelines within the scope of droplet transmission. However, increasing evidence and comparative analysis with similar coronaviruses, such as severe acute respiratory syndrome (SARS-CoV-1) and middle eastern respiratory syndrome (MERS), suggest that airborne transmission of SARS-CoV-2 cannot be effectively ruled out. As the data supporting COVID-19 airborne transmission grows, there needs to be an increased effort in terms of technical and policy measures to mitigate the spread of viral aerosols. These measures can be in the form of broader social distancing and facial covering guidelines, exploration of thermal inactivation in clinical settings, low-dose UV-C light implementation, and greater attention to ventilation and airflow control systems. This review summarizes the current evidence available about airborne transmission of SARS-CoV-2, available literature about airborne transmission of similar viruses, and finally the methods that are already available or can be easily adapted to deal with a virus capable of airborne transmission.

Objectives: In December 2019 a novel human-infecting coronavirus, SARS-CoV-2, has emerged. The WHO has stated the epidemic as a “public health emergency of international concern”. A drammatic situation has emerged with thousands of deaths, occurring mainly in the aged and very ill people. Epidemiological studies suggest that immune system function is impaired in elderly individuals and these subjects often present a severe deficiency in nutrients as fatsoluble and hydrosoluble vitamins. Design: In this first part of the review about Cov2 in aged people, we searched for reviews describing the characteristics of autoimmune diseases and the available therapeutic protocols for their treatment. We sed them as a paradigm with the purpose to retrieve pathogenetic mechanisms in common among these pathological conditions and SARS-CoV-2 infection, as well as the alteration induced in immune system function by this virus, or by its homologous SARS-CoV. Results: SARS-CoV-2 infection induces an important immune system dysfunction with the development of an exhuberant proinflammatory response in the host, and with the development of a life-threatening condition defined as Cytokine Release Syndrome (CRS). This leads to the Acute Respiratory Syndrome (ARDS), mainly in the aged people. High mortality and lethality rates have been observed in the elderly subjects with CoV-2-related infection. Conclusion: These diseases may serve as a paradigm for the study of CRS emerging in the course of SARS CoV-2 infection. This review discusses about the possible activity of Vitamin A, D, E and C in restoring normal antiviral Immune System function or the potential therapeutic role of these micronutrients as a part of a multi-treatment strategy against SARS- CoV-2 infection.

A practical method is presented to estimate rare earth elements (REE) concentrations in magmatic vapour phase (MVP) in equilibrium with water-saturated granitic melts based on empirical fluid-melt partition coefficients of REE (k_P^REE). The values of k_P^REEcan be calculated from a set of new polynomial equations linking to the chlorine molality (m_Cl^v) of the MVP associated with granitic melts, which are established via a statistical analysis on the existing experimental dataset. These equations may be applied to the entire pressure range (0.1 to 10.0 kb) within the continental crust, suggesting that light REEs behave differently in magmatic fluids, i.e. either being fluid compatible with higher m_Cl^v or fluid incompatible with lower m_Cl^v values. In contrast, heavy REEs are exclusively fluid incompatible and partition favourably into granitic melts. Consequently, magmatic fluids tend to be rich in LREE relative to HREE, leading to REE fractionation during the evolution of magmatic hydrothermal systems. Maximum k_P^REEvalue for each element is predicted and presented in a REE distribution diagram constrained by the threshold of m_Cl^v. REE contents of the granitic melt is approximated by whole-rock analysis, so that REE concentrations in the associated MVP would be estimated from the value of k_P^REE given chemical equilibrium retains. Two examples are provided respectively, to show the use of this method as a REE tracer to fingerprint the source of ore-fluids responsible for the Lake George intrusion-related Au-Sb deposit in New Brunswick (Canada), and for the Bakircay Cu-Au (-Mo) porphyry systems in northern Turkey.

This paper attempts at applying adaptive network-based fuzzy inference system (ANFIS) for analysis of the resonant frequency of a microstrip rectangular patch antenna with two equal size slots which are placed on the patch vertically. The resonant frequency is calculated as the position of slots is shifted to the right and left sides on the patch. As a result , the antenna resonates at more than one frequency . Commonly, machine algorithms based on artificial neural networks are employed to recognize the whole resonant frequencies. However ,they fail to estimate the resonant frequencies correctly as in some cases variations are not very sensible and the resonant frequencies overlap each other . It can be concluded that artificial neural networks could be replaced in such designs by the adaptive network-based fuzzy Inference system due to its high approximation capability and much faster convergence rate.

The consistently positive Australian economic environment and stable population increase have led to a higher demand for new houses in recent years. Prefabrication is a promising method to help alleviate the issues related to housing shortage and affordability due to reduced material wastage, construction delays due to weather conditions, unexpected costs, shortage in labour and onsite risks. With the advancements in automation and manufacturing methods such as Design for Manufacturing and Assembly (DfMA), the quality and precision of prefabricated materials is tightly controlled, and the fabrication and assembly period are reduced. However, the full potential of prefabricated construction is yet to be realised in part due to most of developments being focused on its superstructure. A review of the current available options suitable for houses is necessary to understand the present state of the residential footing industry, which will help evaluate the necessary innovations for the growth of the Australian construction industry considering the local reactive soil conditions. This paper presents a summary of existing footing systems and potential prefabricated footing solutions for low-rise residential structures with one storey to two storeys. This paper also reviews the benefits and challenges of designing, manufacturing, transporting, handling and installing of prefabricated footings on site, which have great influence on the acceptance of these innovative footing systems.

This paper presents a methodology for determining the sensitivity of the main air flow directions in ventilation subnetworks to changes of aerodynamic resistance and of air density in mine workings. Formulae for determination of the sensitivity of the main subnetwork air flows by establishing the degree of dependency of the air volume stream in a given working on the variations in resistance or air density of other workings of the network have been developed. They have been implemented in the VentGraph mine ventilation network simulator. This software, widely used in Polish collieries provides an extended possibility to predict the process of ventilation, air distribution and, in the case of underground fire, also the spread of combustion gasses. The new method facilitates assessment by mine ventilation services of the stability of ventilation systems in exploitation areas and determine of the sensitivity of the main subnetwork air flow directions to changes of aerodynamic resistance and air density. Recently in some Polish collieries new longwalls are developed in seams located deeper then the bottom of the intake shaft. Such solution is called “exploitation below the level of access” or “sublevel”. The new approach may be applied to such developments to assess the potential of changes of direction and air flow rates. In addition, interpretation of the developed sensitivity indicator is presented. While analyzing air distributions for sublevel exploitation, application of current numerical models for calculations of the distribution results in tangible benefits, such as the evaluation of the safety or risk levels for such exploitation. Application of the VentGraph computer program, and particularly the module POŻAR (fire) with the newly developed options, enables an additional approach to the sensitivity indicator in evaluating air flow safety levels for the risks present during exploitation below the level of the intake shaft. The analyses performed and examples presented enabled useful conclusions in mining practice to be drawn.

Increasing demand for electricity and modernization of power systems within competitive markets induce power systems to operate close to their stability limits. Therefore, a power system continuous monitoring and control by using voltage stability indices are known exigence. This is the first-ever effort, examined more than 40 voltage stability indices thorough an exhaustive study based in on their formulation, application, performance, and assessment measures. Which is conducted based on a logical and chronological order considering the most recent worldwide applied voltage stability indices. However, the generalizability of such an immense diversity of these indices in term of multivariable objectives subjects to a certain limitation to summate into a deciphered outcome. Despite all these obstacles, this study systematically reviews available indices in the literature within the past three decades to set out an integrated knowledge with an insight up-to-date exposition. That followed with a comparative analysis in term of their similarity, functionality, applicability, formulation, merit, demerit, and overall performances. Also, a broad categorization of voltage stability indices is addressed. This study with an immediate implication in practice as an exhaustive roadmap can be counted a planning and operation reference in the context of voltage stability for students, researchers, scholars, and practitioners.

Battery electric vehicles provide an opportunity to balance supply and demand in future power systems with high shares of fluctuating renewable energy. Compared to other storage systems such as pumped-storage hydroelectricity, electric vehicle energy demand is highly dependent on charging and connection choices of vehicle users. We present a model framework of a utility-based stock and flow model, a utility-based microsimulation of charging decisions, and an energy system model including respective interfaces to assess how the representation of battery electric vehicle charging affects energy system optimization results. We then apply the framework to a scenario study for controlled charging of nine million electric vehicles in Germany in 2030. Assuming a respective fleet power demand of 27 TWh, we analyze the difference between power-system-based and vehicle user-based charging decisions in two respective scenarios. Our results show that taking into account vehicle users’ charging and connection decisions significantly decreases the load shifting potential of controlled charging. The analysis of marginal values of equations and variables of the optimization problem yields valuable insights on the importance of specific constraints and optimization variables. In particular, state-of-charge assumptions and representing fast charging drive curtailment of renewable energy feed-in and required gas power plant flexibility. A detailed representation of fleet charge connection is less important. Peak load can be significantly reduced by 5% and 3% in both scenarios, respectively. Shifted load is very robust across sensitivity analyses while other model results such as curtailment are more sensitive to factors such as underlying data years. Analyzing the importance of increased BEV fleet battery availability for power systems with different weather and electricity demand characteristics should be further scrutinized.

The link between language processing and motor systems has been the focus of increasing interest to Cognitive Neuroscience. Some classical papers studying Event Related Potentials (ERPs) induced by linguistic stimuli have found differences in electrophysiological activity when comparing action and non-action words; more specifically, a bigger p200 for action words. On the other hand, a series of studies have validated the use of a grip force sensor (GFS) to measure language-induced motor activity during both isolated words and sentence listening, finding that action words induce an augmentation in the grip force around 250-300 ms after the onset of the stimulus. The purpose of the present study is to combine both techniques to assess if the p200 is related to the augmentation of the grip force measured by a GFS. We measured ERP and GFS changes elicited by listening to action and non-action words while maintaining an active grasping task in 10 healthy subjects. Our results show that the amplitude of the p200 in central electrodes is correlated to the augmentation in the GFS around 300 ms induced by linguistic stimuli. To our knowledge, this is the first study where the electrophysiological activity and the changes in the grip force induced by auditory language processing are put together, opening new venues of interpretation for the sensorimotor interaction in language processing.

Serious damage caused by floor heave in the coal given chamber of a vertical coal bunker is one of the challenges faced in underground coal mines. Engineering practice shows that it is more difficult to maintain the coal given chamber (CGC) than a roadway. More importantly, repairing the CGC during mining practice will pose major safety risks and reduce production. Based on the case of the serious collapse that occurred in the bearing structure of the CGC at the lower part of the 214# coal bunker in Xiashijie mine, China, this work analysed (i) the main factors influencing floor heave and (ii) the failure mechanism of the load-bearing structure in the CGC using FLAC^2D numerical models and expansion experiment. The analysis results indicate that: the floor heave, caused mainly by mine water, is the basic reason leading to the instability and repeated failure of the CGC in the 214# coal bunker. Then a new coal bunker, without building the CGC, is proposed and put into practice to replace the 214# coal bunker. The FLAC^3D software program is adopted to establish the numerical model of the wall-mounted coal bunker (WMCB), and the stability of the rock surrounding the WMCB is simulated and analysed. The results show that: (1) the rock surrounding the sandstone segment is basically stable. (2) The surrounding rock in the coal seam segment, which moves into the inside of the bunker, is the main zone of deformation for the entire rock mass surrounding the bunker. Then the surrounding rock is controlled effectively by means of high-strength bolt–cable combined supporting technology. According to the geological conditions of the WMCB, the self-bearing system, which includes (i) H-steel beams, (ii) H-steel brackets, and (iii) self-locking anchor cables, is established and serves as a substitute for the CGC to transfer the whole weight of the bunker to stable surrounding rock. The stability of the new coal bunker has been verified by field testing, and the coal mine has gained economic benefit to a value of 158.026174 million RMB over three years. The new WMCB thus made production more effective and can provide helpful references for construction of vertical bunkers under similar geological conditions.

The contamination on the insulators may increase its surface conductivity and, as a consequence, electrical discharges occur more frequently, which can lead to interruptions in the power supply. To maintain reliability in the electrical distribution power system, components that have lost their insulating properties must be replaced. Identifying the components that need maintenance, is a difficult task as there are several levels of contamination that are hardly noticed during inspections. To improve the quality of inspections, this paper proposes to use the k-nearest neighbours (k-NN) to classify the levels of insulator contamination, based on the image of insulators at various levels of contamination simulated in the laboratory. Using computer vision features such as mean, variance, asymmetry, kurtosis, energy, and entropy are used for training the k-NN. To assess the robustness of the proposed approach, statistical analysis and a comparative assessment with well-consolidated algorithms such as decision tree, ensemble subspace, and support vector machine models are presented. The k-NN showed results of up to 85.17 % accuracy using the k-fold cross-validation method, with an average accuracy higher than 82 % for multi-classification of the contamination of the insulators, being superior to the compared models.

Clay ball is a pavement surface defect which refers to a clump in which clay or dirt is mixed with hot asphalt mixture. Clay ball is typically caused by a combination of aggregate contamination of clay or soil, high aggregate moisture, and low production temperature at the asphalt plant. It usually appears a few weeks or months after paving under traffic load, after being liquefied and knocked from the pavement surface. Clay balls can be the source of potholing, raveling, and other issues such as moisture infiltration and reduced ride quality. This paper presents an investigation of the clay balls on US-31 one winter after construction in Hamilton County, Indiana. In order to understand the pavement condition, their severity was measured using both visual observation and infrared image collection system. In addition, a clay ball distribution pattern, its density, and cores condition were evaluated. A precipitation effect on clay ball formation was investigated for finding a cause of the clay balls. The investigation found that infrared image collection system was appropriate in detecting the clay balls. The clay balls were elliptic in shape with 1 inch to 4 inches in diameter, and the maximum clay ball depth is almost penetrating the entire surface course. It was also found that the asphalt paving on the raining days or right after raining could increase the potential of clay balls. Monitoring of aggregate moisture during construction on or after raining days should be able to reduce the risk of clay balls.

Lung cancer is a deadly disease if not diagnosed in its early stages. However, early detection of lung cancer is a challenging task due to the shape and size of its nodules. Radiologists need support from automated tools for precise opinion. Automated detection of the affected lungs nodule is difficult because of the shape similarity among healthy tissues. Over the years, several expert systems have been developed that help radiologists to diagnose lung cancer. In this article, we propose a framework to precisely detect lungs cancer by classifying it between benign and malignant nodules. The framework is tested using the subset of the publicly available at the Lung Image Database Consortium image collection (LIDC-IDRI). Multiple techniques including filtering and noise removing are applied for pre-processing. Subsequently, the OTSU and the semantic segmentation are used to accurately detect the unhealthy lungs nodules. In total, 13 nodules features were extracted using Principal Components Analysis (PCA) algorithm. Four optimal features are selected based on the classification performance. In the classification phase, 9 different classifiers are used along with two types of validation schemes i.e. train test holdout validation with 70-30 data split and 10 fold cross-validation. Our experiments show that the proposed system provides 99.23\% accuracy using logic boost classifier.

Doubly Fed Induction Generator (DFIG) has a stator winding directly coupled with grid. Whereas, rotor winding is connected via a fault-prone back to back power converters. DFIG is known to be vulnerable to the grid faults. In early times, when a fault occurred, these generators were required to disconnect from the grid to secure the generator and power converters. However, due to the increased penetration of wind turbines into the power system, grid operators demanded that the wind turbines remain connected to the grid, as disconnecting them would further disrupt the grid. When a fault at the grid terminal occur, a high stator current is induced which further result in high rotor current. This current will trigger the DC-link voltage to rise. This high currents and DC-link voltage will cause harm to the converters. Thus, in this paper work, the crowbar protection system is employed for protecting the converters against excess energy. Furthermore, the analysis of DFIG is rendered by integrating the crowbar protection with the Battery Energy Storage System (BESS) for a much effective outcome in enhancing machine to drive the fault. MATLAB-Simulink software is used for modeling and simulation. All system parameters are obtained from ADAMA-II Wind Farm.

Zebrafish have been found to be the premier model organism in biological and biomedical research, specifically offering many advantages in developmental biology and genetics. This unique aquatic species has been found to have the capacity to regenerate their spinal cord after injury. However, the complete molecular and cellular mechanisms behind glial bridge formation in the central and peripheral nervous systems upon glial cell injury remains unclear. This review paper focuses on the molecular mechanisms and cellular processes that underlie spinal cord regeneration in four initial phases: proliferation and initial migration; migration and differentiation; glial bridge formation; and remodeling. We propose that within these four phases the cellular mechanisms that underlie spinal cord regeneration each express a terminating signal that aborts one step of the process and initiates the next. Specifically, future studies would be devoted to investigate transmitting signals in the spinal cord injury micro-environment in hope to contribute to the understanding of underlying cellular mechanisms by connecting each process of spinal cord regeneration in zebrafish.

The exposure of people to opportunistic premise plumbing pathogens (OPPPs) such as Legionella, Mycobacterium and Pseudomonas in aerosolised water has been linked to opportunistic infections. Water mist systems (WMS) that are used to cool public places by flash evaporation of tiny water aerosols are gaining prominence in hot climatic regions of Australia. Their potential to be colonised by OPPPs has not been adequately studied. The public health impact of OPPPs is significant, as Legionella caused 63% of waterborne disease case hospitalisations in the United States associated with drinking water systems during 2013–2014, and the incidence of Mycobacterium avium over the same period was 647 cases per 100,000. As WMS are part of premise plumbing, they have structural characteristics that can promote biofilm formation, as well as the presence of free-living amoebae (FLA), low residual disinfection levels, elevated water temperatures and oligotrophic conditions, all of which can promote OPPP inhabitancy. This review highlights the potential public health risks of using WMS as a cooling intervention in public places and advocates for their regulation in places of public assembly and entertainment.

This study purposes the use of plug-in electric vehicles for demand side management (DSM) considering uncertainties in demand as well as uncertainties due to mobility of PEV to mitigate peak demand. The solution also seeks to reduce electric cost in addition to reducing the effects of greenhouse gases. In recent years DSM using distributed storage system such as battery energy management system (BESS) and plugged-in electric vehicles (PEV) have become very prevalent with most implementations resorting to deterministic load forecast. These methods do not consider the potential growth in demand making their solutions less robust. In this study we propose a real-time density demand forecast and stochastic optimization for robust operation of PEV for a building. This method accounts for demand uncertainties in addition to uncertainties in mobile energy storage as found in PEV, making the resulting solution robust as compared to the deterministic case. A case study on a real site in South Korea is used for verification and testing. The proposed study is verified and tested against existing algorithms. The result verifies the effectiveness of the proposed approach

This paper proposes a hybrid medium access control (MAC) protocol for wireless sensor network (WSN) data gathering, employing unmanned aerial vehicles (UAV). The UAV sends a beacon frame periodically to inform sensor nodes regarding its presence. Afterward, each sensor node which receives beacon frame contends to send registration frame to the UAV. The UAV will transmit the second beacon frame to the registered nodes to notify their transmission schedule. The time-slot scheme is used for the transmission schedule. The transmission schedule of each sensor is determined based on their priority. Specifically, the priority of each sensor is determined during the registration process. Furthermore, the architecture of UAV-WSN data gathering system is introduced in this paper. Simulations are performed, showing that the proposed MAC protocol achieves fairness while enhancing network performance.

In this paper, a local meshless method (LMM) based on radial basis functions (RBFs) is utilized for the numerical solution of various types of PDEs, due to the flexibility with respect to geometry and high order of convergence rate. In case of global meshless methods, the two major deficiencies are the computational cost and the optimum value of shape parameter. Therefore, research is currently focus towards localized radial basis function approximations, as the local meshless method is proposed here. The local meshless procedures is used for spatial discretization whereas for temporal discretization different time integrators are employed. The proposed local meshless method is testified in terms of efficiency, accuracy and ease of implementation using regular and irregular domains.

The development of wireless sensor networks (WSNs) technology comes with inherent limitations and vulnerabilities that make it exploitable by intruders. The fundamental goal of this article is to solve security problems related to black hole attacks, which interfere with the proper performance of the network and can lead to data leakage and loss. The proposed solution in this article is to apply IDS to a WSN for the first time. We also used the NS2.35 simulator to compare the three routing protocols, AODV, AODV under Hacker Node (HNAODV), and the proposed solution (IDSHNAODV) in the WSN model to reduce the effects of black hole attacks.

In this research paper we describe the development phase of a low-cost LoRa IoT solution applied to a kindergarten school with three years results. A set of sensors solution was developed in a LoRa communication board, battery powered, providing a simplified setup process. These sensors were used in order to measure temperature, humidity, luminosity, air quality and presence. Also, energy monitor solutions were integrated. The acquired data is transmitted and analysed for knowledge extraction, identifying savings and other related KPIs. From data, automatic saving actions were performed towards heating and cooling systems, lighting and a set of if-then actions were developed for automatic cost-saving actions, based on infrared signals to heating/cooling systems using some procedure of external command devices. This approach avoids the usage of proprietary vendor solutions in a flexible approach that can easily be deployed to any building facility. This is an important achievement since most of the building consumption is based on heating and cooling systems. In a three years test of the solution, the total energy consumption savings surpassed 20%

To remain competitive in the current market, an enterprise must differentiate itself based on higher value propositions. For this purpose, since improving the product or service performance can reach some limits, one potential solution is to move towards new combinations of products and services. This evolution, called servitization, leads to the generation of Product-Service Systems (PSS). Servitization requires not only a clear understanding of enterprise core business but also a clear vision on the prevailing trends and challenges of PSS development from both business and technological points of view. In addition, the evolution path should be aligned with the enterprise strategy. This paper first highlights the notion of symbiotic PSS where Product Systems and Service Systems, and their stakeholders, interoperate seamlessly based on a win-win approach. Then it proposes a PSS Conceptual Framework (PSS-CF) which can be applied in the early stages of servitization to increase the understating of PSS dimensions and to facilitate the prioritization of the servitization investments. The framework dimensions were discussed in several iterations, from both academic and industrial points of view, in the frame of a European research project. Moreover, the applicability of the framework was studied in four different industrial Use-Cases.

Climate projections show that southern Indonesia such as West Nusa Tenggara is projected to experience a lower precipitation and higher temperatures. To date, research on climate change impact on Indonesian rice production yield is limited. As climate change is projected to decrease rainfall and to increase temperatures, this paper offers a qualitative analysis using system archetypes to understand the impacts of climate change on rice production. Two system archetypes are identified including Limits to Growth and Success to Successful. Both archetypes explain that rice production is hampered by high minimum temperature as photosynthesis output is decreased by increasing respiration. This paper shows that using a simple tool, system archetypes, we can describe the impacts of climate change on rice production. The outputs of this study such as a causal loop diagram and system archetypes can be a basis to develop a simulation model in understanding the impacts of climate change on main crops.

The land-surface developments of the European Centre for Medium-range Weather Forecasts (ECMWF) are based on the Carbon-Hydrology Tiled Scheme for Surface Exchanges over Land (CHTESSEL) and form an integral part of the Integrated Forecasting System (IFS), supporting a wide range of global weather, climate and environmental applications. In order to structure, coordinate and focus future developments and benefit from international collaboration in new areas, a flexible system named ECLand which would facilitates modular extensions to support numerical weather prediction (NWP) and society-relevant operational services, e.g. Copernicus, is presented . This paper introduces recent examples of novel ECLand developments on (i) vegetation, (ii) snow, (iii) soil, (iv) open water/lake (v) river/inundation, and (vi) urban areas. The developments are evaluated separately with long-range, atmosphere-forced surface offline simulations, and coupled land-atmosphere-ocean experiments. This illustrates the benchmark criteria for assessing both, process fidelity with regards to land surface fluxes and reservoirs of the water-energy-carbon exchange on the one hand, and on the other hand the requirements of ECMWF’s NWP, climate and atmospheric composition monitoring services using an Earth system assimilation prediction framework.

Alzheimer’s Disease (AD) is a neurodegenerative condition that currently has no known cure. The principles of the expanding field of network medicine (NM) have recently been applied to AD research. The main principle of NM proposes that diseases are much more complicated than one mutation in one gene, and incorporate different genes, connections between genes, and pathways that may include multiple diseases to create full scale disease networks. AD research findings as a result of the application of NM principles have suggested that functional network connectivity, myelination, myeloid cells, and genes and pathways may play an integral role in AD progression, and may be integral to the search for a cure. Different aspects of the AD pathology could be potential targets for drug therapy to slow down or stop the disease from advancing, but more research is needed to reach definitive conclusions. Additionally, the holistic approaches of network pharmacology in traditional Chinese medicine (TCM) research may be viable options for the AD treatment, and may lead to an effective cure for AD in the future.

In nonlinear self-consistent system, Lorenz system (Lorenz equations) is a classic case with chaos solutions which are sensitively dependent on the initial conditions. As it is difficult to get the analytical solution, the numerical methods and qualitative analytical methods are widely used in many studies. In these papers, Runge-Kutta method is the one most often used to solve these differential equations. However, this method is still a method based on explicit time scheme, which would be the main reason for the chaotic solutions to Lorenz system. In this work, numerical experiments based on implicit time scheme and explicit scheme are setup for comparison, the results show that: in implicit time scheme, the numerical solutions (counterexamples) are without chaos; for an original volume, the volume shrinks exponentially fast to 0 in common.

Legionnaires’ disease is normally acquired by inhalation of legionellae from a contaminated environmental source. Water systems of large buildings, such as hospitals, are often contaminated with legionellae and therefore represent a potential risk for the hospital population. In this study, we demonstrated the constant presence of Legionella in water samples from the water system of a large university hospital in Messina (Sicily, Italy) consisting of 11 separate pavilions during a period of 15 years (2004-2018). In total, 1346 hot water samples were collected between January 2004 and December 2018. During this period, to recover Legionella spp. from water samples the standard procedures reported by the Italian Guidelines emanated in 2000 were adopted; from May 2015 to 2018 Italian Guidelines revised in 2015 (ISS, 2015), were used. The most water samples (72%) were positive to L. pneumophila serogroups 2-14 whereas L. pneumophila serogroup 1 accounted for the 18% and Legionella spp. for the 15%. Most of the positive samples were found in the buildings where are situated critical wards as ICU, Neurosurgery, Surgeries, Pneumology and Neonatal Intensive Unit Care. We highlighted the importance of a continuous monitoring of hospital water samples to prevent the potential risk of nosocomial legionellosis.

This paper presents a methodology to design sensor fusion parameters using real performance indicators of navigation in UAVs based on PixHawk flight controller and peripherals. This methodology and the selected performance indicators allows to find the best parameters for the fusion system of a determined configuration of sensors and a predefined real mission. The selected real platform is described with stress on available sensors and data processing software, and the experimental methodology is proposed to characterize sensor data fusion output and determine the best choice of parameters using quality measurements of tracking output with performance metrics not requiring ground truth.

A new lateral force-resisting wall panel is presented for applications in mid-rise prefabricated lightweight steel construction in seismic prone regions. This panelised system is composed of a hot-rolled frame and cold-formed studs and tracks, which works as a lateral force resisting system in lightweight steel framing. The proposed hybrid panel system exhibits proper ductility and energy dissipation behaviour. The hysteretic responses of full-scale panel experiments demonstrate that the system can safely resist high cyclic loads. The hot-rolled steel part, on the other hand, can significantly improve the initial lateral stiffness of the total system that will control the maximum allowable drift of multi-story buildings. Finally, in a case study, by applying the proposed system to the design of a mid-rise building in a high seismic area, the performance of the hybrid panels, are compared with those of fully hot-rolled systems (moment resisting frames) and fully cold formed systems. The findings indicate that applying hybrid panels will result in lower weights and better performance in seismic prone regions.

Building more energy efficient and sustainable urban areas that will both mitigate the effect of climate change and adapt for the future climate, requires the development new tools and methods that can help urban planners, architect and communities achieve this goal. In the current study, we designed a workflow that links different methodologies developed separately, to derive the energy consumption of a university school campus for the future. Three different scenarios for typical future years (2039, 2069, 2099) were run as well as a renovation scenario (Minergie-P). We analyse the impact of climate change on the heating and cooling demand of the buildings and determined the relevance of the accounting of the local climate in this particular context. The results from the simulations showed that in the future there will a constant decrease in the heating demand while for the cooling demand there will be a significant increase. It was further demonstrated that when the local climate was taken into account there was an even higher rise in the cooling demand but also that the proposed renovations were not sufficient to design resilient buildings. We then discuss the implication of this work on the simulation of building energy consumption at the neighbourhood scale and the impact of future local climate on energy system design. We finally give a few perspective regarding improved urban design and possible pathways for the future urban areas.

In this research paper, a hybrid Artificial Neural Network (ANN)-Fuzzy Logic Control (FLC) tuned Flower Pollination Algorithm (FPA) as a Maximum Power Point Tracker (MPPT) is employed to emend root mean square error (RMSE) of photovoltaic (PV) modeling. Moreover, Gaussian membership functions have been considered for fuzzy controller design. This paper interprets Luo converter occupied brushless DC motor (BLDC) directed PV water pump application. Experimental responses certify the effectiveness of the suggested motor-pump system supporting diverse operating states. Luo converter is newly developed dc-dc converter has high power density, better voltage gain transfer and superior output waveform and able to track optimal power from PV modules. For BLDC speed controlling there is no extra circuitry and phase current sensors are enforced for this scheme. The recentness of this attempt is adaptive neuro-fuzzy inference system (ANFIS)-FPA operated BLDC directed PV pump with advanced Luo converter has not been formerly conferred.

Collisions of low energy electrons with molecules are important for understanding many aspects of the environment and technologies. Understanding the processes that occur in these types of collisions can give insights into plasma etching processes, edge effects in fusion plasmas, radiation damage to biological tissues and more. A radical update of the previous expert system for computing observables relevant to these processes, Quantemol-N, is presented. The new Quantemol Electron Collision (QEC) expert system simplifyies the user experience, improving reliability and implements new features. The QEC GUI interfaces the Molpro quantum chemistry package for molecular target setups and to the sophisticated UKRmol+ codes to generate accurate and reliable cross-sections. These include elastic cross-sections, super elastic cross-sections between excited states, electron impact dissociation, scattering reaction rates, dissociative electron attachment, differential cross-sections, momentum transfer cross-sections, ionization cross sections and high energy electron scattering cross-sections. With this new interface we will be implementing dissociative recombination estimations, vibrational excitations for neutrals and ions, and effective core potentials in the near future.

In this paper the authors present a method which facilitates computationally efficient parameter estimation of dynamical systems from a continuously growing set of measurement data. It is shown that the proposed method, which utilises Sequential Monte Carlo samplers, is guaranteed to be fully parallelisable (in contrast to Markov chain Monte Carlo methods) and can be applied to a wide variety of scenarios within structural dynamics. Its ability to allow convergence of one's parameter estimates, as more data is analysed, sets it apart from other sequential methods (such as the particle filter).

Proposed article deals with analyses on trends of problem-solving robotic applications in manu-facturing, especially in transportations and manipulations. Intelligent agent-based manufacturing systems with robotic agents are observed. Intelligent core of such units must be able to propose the plan of problem-solving in form of strategy. The logical model of adaptive strategies planning for intelligent robotic system is described in form of predicates with presentation of data processing on base of set theory descriptions. Dynamic structure of workspace and possible change of goals are considered as reasons for functional strategies adaptation. Proposed formal descriptions are sup-ported by model of mobile robotic platform, acting in warehouse.

Energy conversion and distribution (heat and electricity) is characterized by long planning horizons, investment periods and depreciation times, and it is thus difficult to plan and tell the technology that optimally fits for decades. Uncertainties include future energy prices, applicable subsidies, regulation, and even the evolution of market designs. To achieve higher adaptability to arbitrary transition paths, a technical concept based on integrated energy systems is envisioned and described. The problem of intermediate steps of evolution is tackled by introducing a novel paradigm in urban infrastructure design.It builds on standardization, modularization and economies of scale for underlying conversion units. Building on conceptual arguments for such a platform, it is then argued how actors like (among others) municipalities and district heating system operators can use this as a practical starting point for a manageable and smooth transition towards more environmental friendly supply technologies, and to commit to their own pace of transition (bearable investment/risk). environmental friendly supply technologies. Merits are not only supported by technical arguments but also by strategical and societal prospects like technology neutrality and availability of real options.

Anti-CD19 chimeric antigen receptor (CAR) T-cells represent a novel immunotherapy that has shown remarkable success in the treatment of adult relapsed or refractory (R/R) B-cell non-Hodgkin's lymphoma, adult R/R mantle cell lymphoma, and R/R acute paediatric lymphoblastic leukaemia. One barrier to the widespread use of CAR T-cell therapy is toxicity, primarily cytokine release syndrome (CRS) with a variable grade of severity. The main manifestations of CRS are fever, hypotension, cytopenia, organ dysfunction among others. Neurological toxicities vary widely and range from headaches to encephalopathy. In addition, anti-CD19 CAR T-cell therapy provokes an array of less frequent events, such as coagulopathies, delayed cytopenia, and cardiovascular toxicities. In general, toxicities are usually reversible and resolve on their own in most cases, though severe cases may require intensive care and immunosuppressive therapy. Deaths due to CRS, neurologic toxicity and infectious complications have been reported, which highlights the gravity of these syndromes and the critical nature of appropriate intervention. In this paper, we look at all available FDA- and EMA-approved information about the pathophysiology, clinical manifestations, risk factor reviews of existing toxicity grading systems, current management strategies, and guidelines for anti-CD19 CAR T-cell toxicities. We also present new approaches, which are under investigation, to mitigate these adverse events.

Electric Vehicles (EVs) are the need of the hour due to growing climate change problems linked with the transportation sector. Battery Thermal Management System (BTMS), which is accountable for certifying safety and performance of lithium-ion batteries (LiB), is the most vital part of an EV. LiB has auspicious gravimetric energy density but the heat generation due to chemical reactions inside a LiB during charging and discharging causes temperature rise which has a direct effect on LiB performance and safety. This study specifically focuses on aircooled BTMS, defines different types of air-cooled BTMS (active and Passive), discusses limitations associated with air-cooled BTMS, and investigates different optimization techniques and parameters to improve performance of air-cooled BTMS. Maintaining temperature within optimum range and uniform temperature distribution between cells of a battery pack are the major design parameters for improving the performance and efficiency of air-cooled BTMS. Various optimization techniques including cell arrangement with a battery pack, air-flow channel optimization, and air inlet/outlet position variations are discussed and each technique is thoroughly reviewed. Finally, it’s noted that passive air-cooled BTMS is not that effective for long-distance vehicles so most researchers shifted their focus toward active air-cooled BTMS. Active air-cooled BTMS requires a lot of power for effective performance. Lastly, the most recent field of air-cooled BTMS technology which is Air-Hybrid BTMS is discussed and declared a very promising solution for overcoming major limitations associated with air-cooled BTMS.

This paper deals with the problem of real-time management of Smart Grids. For this sake, the energy management is integrated with the power system through a telecommunication system. The use of Multiagent Systems leads the proposed algorithm to find the best-integrated solution, taking into consideration the operating scenario and the system characteristics. The proposed technique is tested with the help of an academic microgrid, so the results may be replicated.

Blade Health Monitoring (BHM) is often necessary in power plants and in aviation to prevent excessive blade vibration and cracks. This article proposes a network of blade tip timing sensors operated in a distributed BHM system. A number of cooperating agents is implemented in smart conditioning units which can autonomously operate in an adverse environment and communicate with other nodes via a serial interface. The project uses special versions of reduced instruction set chips that are able to operate near the hot section of the engine. Due to the limited number of types of microprocessors available in the extended temperature range grading, it was necessary to fully utilize the limited hardware resources and implement preemptive multitasking. For this purpose, a custom operating system and communication protocol were designed. The protocol hosts the middle layer which hides the implementation of the distributed system. The presented architecture ensures the sufficient computational capacity in individual nodes of the network operated in adverse conditions. It is scalable and resistant to transmission errors.

Decision tables have been used for many years in data processing and business applications to simulate complex rule sets. Several computer languages have been developed based on rule systems and they are easily programmed in several current languages. Land management and river-reservoir models simulate complex land management operations and reservoir management in highly regulated river systems. Decision tables are a precise yet compact way to model the rule sets and corresponding actions found in these models. In this study, we discuss the suitability of decision tables to simulate management in the river basin scale Soil and Water Assessment Tool (SWAT+) model. Decision tables are developed to simulate automated irrigation and reservoir releases. A simple auto irrigation application of decision tables was developed using plant water stress as a condition for irrigating corn in Texas. Sensitivity of the water stress trigger and irrigation application amounts were shown on soil moisture and corn yields. In addition, the Grapevine Reservoir near Dallas, Texas was used to illustrate the use of decision tables to simulate reservoir releases. The releases were conditioned on reservoir volumes and flood season. The release rules as implemented by the decision table realistically simulated flood releases as evidenced by a daily NSE (Nash-Sutcliffe Efficiency) of 0.52 and a percent bias of -1.1%. Using decision tables to simulate management in land, river and reservoir models was shown to have several advantages over current approaches including: 1) mature technology with considerable literature and applications, 2) ability to accurately represent complex, real world decision making, 3) code that is efficient, modular and easy to maintain, and 4) tables that are easy to maintain, support, and modify.

Heart failure (HF) has several etiologies including myocardial infarction (MI) and left ventricular remodeling (LVR), but its progression remains difficult to predict in clinical practice. Systems biology analyses of LVR after MI predict molecular insights of this event such as modulation of microRNA (miRNA) that could be used as a signature of HF progression. To define a miRNA signature of LVR after MI, we use 2 systems biology approaches integrating either proteomic data generated from LV of post-MI rat induced by left coronary artery ligation or multi-omics data (proteins and non-coding RNAs) generated from plasma of post-MI patients from the REVE-2 study. The first approach predicts 13 miRNAs and 3 of these miRNAs were validated to be associated with LVR in vivo: miR-21-5p, miR-23a-3p and miR-222-3p. The second approach predicts 24 miRNAs among 1310 molecules and 6 of these miRNAs were selected to be associated with LVR in silico: miR-17-5p, miR-21-5p, miR-26b-5p, miR-222-3p, miR-335-5p and miR-375. We identified a signature of 7 microRNAs associated with LVR after MI that support the interest of integrative systems biology analyses to define a miRNA signature of HF progression.

Detection of human movement requires lightweight, flexible systems to detect mechanical parameters (like strain and pressure) not interfering with user activity, and that he/she can wear comfortably. In this work we address such multifaceted challenge with the development of smart garments for lower limb motion detection, like a textile kneepad and anklet in which soft sensors and readout electronics are embedded for detecting movement of the specific joint. Stretchable capacitive sensors with a three-electrode configuration are built combining conductive textiles and elastomeric layers, and distributed at knee and ankle. They show an excellent behavior in the ~30% strain range, hence the correlation between their responses and the optically tracked Euler angles is allowed for basic lower limb movements. Bending during knee flexion/extension is detected, and it is discriminated from any external contact by implementing in real time a low computational algorithm. The smart anklet is designed to address joint motion detection in and off the sagittal plane. In this work, ankle dorsi/plantar flexion, adduction/abduction, and rotation are retrieved. Both smart garments show a high accuracy in movement detection, with a RMSE less than 4° in the worst case.

Electronic invoicing has become mandatory for Italian companies since January 2019. Invoices are structured in a predefined xml template where the information reported can be easily extracted and analyzed. The main aim of this paper is to exploit the information structured in electronic invoices to build an intelligent system which can facilitate accountants work. More precisely, this contribution shows how it is possible to automate part of the accounting process: all sent or received invoices of a company are classified into specific codes which represent the economic nature of the the financial transactions. In order to classify data contained in the invoices a machine learning multiclass classification problem is proposed using as input variables the information of the invoices to predict two different target variables, account codes and the VAT codes, which composes a general ledger entry. Different approaches are compared in terms of prediction accuracy. The best performance is achieved considering the hierarchical structure of the account codes.

The United Arab Emirates (UAE) is significantly dependent on desalinated water and groundwater resource, which is expensive and highly energy intensive. Despite the scarce water resource, only 54% of the recycled water was reused in 2015. In this study, an “Oasis” complex comprised of Sustainable Farming Compartments (SFCs) was proposed for reusing treated wastewater to decrease the ambient temperature of the SFC via an evaporative cooling system. A prototype SFC with half the original scale (width = 1.8 m, depth = 1.5 m, front height = 1.2 m back height = 0.9 m) was designed, built, and tested in an environmentally controlled laboratory and field site to evaluate the feasibility and effectiveness of the SFC under the climatic conditions in Abu Dhabi. Based on the experimental results, the temperature drops obtained from the SFC in the laboratory and field site were 5 ̊C at initial relative humidity of 60% and 7- 15 ̊C at initial relative humidity of 50%, respectively. An energy simulation using dynamic numerical simulations was performed in comparison to the results of the experiment. The energy-based dynamic simulation shows good agreement with the experimental results. The total power consumption of the SFC system was approximately three and a half times lower than that of an electrical air conditioner.

Background: India has one-fifth of the world's population and the number of people suffering from mental illness is assumed to be huge considering the contribution of mental disorders to the overall burden of the disease being 13.9 %. Objectives of Study: To estimate prevalence and patterns of mental illnesses to assess the current mental health services and systems in the Madhya Pradesh. Material and Methods: Multi-stage, stratified, random cluster sampling technique, with random selection based on probability proportionate to size at each stage. A total of 3240 individuals aged 18 years and above were interviewed. Both quantitative and qualitative methods were employed. A set of 10 instruments including Mini International Neuro-psychiatric Interview were utilized. Results: The overall weighted prevalence for any mental illness was 16.7% lifetime and 13.9% current. Treatment-gap for all mental health problems is as high as 91% in the state along with huge socioeconomic impact of mental illness. Conclusions: This huge burden of mental, behavioural and substance use disorders, in Madhya Pradesh, calls for immediate attention of political leaders, policy makers, health professionals, opinion-makers and society at large. It is hoped that the data from the study will inform mental health policy and legislation and help shape mental health care delivery systems in the country.

In this paper, firstly, some questionable formulas and conceptual oversights of previous reduced sigma set unscented transformation (UT) methods are revised through theoretical analysis. Then the revised UT methods based Kalman filters are used in a GPS/INS tightly-coupled system. The Kalman filter flows are the kind of square-root, since the square-root unscented Kalman filters (SRUKFs) can guarantee the stability of the system. By using the reduced sigma set SRUKFs (which contain simplex sigma set square-root unscented Kalman filter (S-SRUKF), spherical simplex sigma set square-root unscented Kalman filter (SS-SRUKF) and minimum sigma set square-root unscented Kalman filter (M-SRUKF)), the computation cost is greatly saved compared with the standard SRUKF, while the accuracy of the GPS/INS tightly-coupled system still maintained. The structure of the GPS/INS tightly-coupled system is in the form of error state, and the time updates of the state and the state covariance of SRUKFs are directly estimated without using UT, thus the computational time is also greatly saved. The pseudo-satellite is introduced to aid the system when the observation information is deficient, for example, when the GPS signal is deficient in the maneuver environment. By using the pseudo-satellite, the optimal performance of the system is guaranteed. Experiment of unmanned aerial vehicle (UAV) showed that the pseudo-satellite aided mechanism worked well.