Background: COVID-19 pandemic has pulled us all a few steps back, were we never shake hands or hug each other when we meet our friends and family after a gap, but instead we greet them by saying Namaste and joining our hands together. As we all know, COVID-19 spreads through air and the only way to shield ourselves is by maintaining a safe distance from one another. Methodology: In order to conduct a meta-analysis on the number of COVID-19 cases in Kerala and India, the data was retrieved from various sites hosted by the government bodies. The data for analysis was collected from May 2020 to July 2020. The average number of days required to reach every 5000 fresh cases were also calculated using this data. COVID-19 has affected all the economy holistically regardless of financial, behavioral, or societal aspects. Conclusion: Lifting of the lockdown in a step by step process keeping in mind the necessities for the nation was a thoughtful act, but the people who mistook this opportunity and did not remain in quarantine after coming from abroad was recognized as the reasons behind the sudden and uncontrolled rise in the number of COVID-19 cases in Kerala, India. The government authorities had no other option but to lift the restrictions to reduce the economic burdens that had already affected the daily wage worker and farmers prompting them to give up their lives.

Organ-on-Chip is a game-changing technology born from the convergence of tissue engineering and microfluidic technology. Organ-on-Chip devices (OoCs) are expected to offer effective solutions to persisting problems in drug development and personalized disease treatments. This opinion paper surveys the current landscape in research, development, application and market opportunities for OoCs to help establishing a global and multi-stakeholder OoC ecosystem. Based on a bibliometric study, a market analysis, expert interviews, and panel discussions held at the ORCHID Vision Workshop (Stuttgart, 23 May 2018), we outline presently unmet needs, key challenges, barriers and perspectives of the field, and finally propose recommendations towards the definition of a comprehensive roadmap that could render OoCs realistic models of human (patho)physiology in the near future.

The prevalence of corona virus and the novel COVID-19 disease in the entire globe has exacerbated different impact on socioeconomic spectrum in the world, including water use pattern. Thus a research was conducted to examine the comparative use of water during pre- and post-COVID-19 lockdown pattern among post-primary schools in Iwo, Osun State, Nigeria. A survey was conducted among fifteen schools which were randomly selected, but with eight public and seven private schools for the investigation. Both descriptive and inferential statistical techniques were used in data analysis. The results revealed that the major source of water to the schools investigated is ground water which is obtained through hand-dug wells and boreholes. It was further discovered that there was increase in water use during post-COVID-19 lockdown era as a result of the directive by the government that clean water should be provided for hand-washing by all schools regardless of the owner to curtail the spread of COVID-19 disease in the country. One sample t-test also revealed that there was a significant difference in water use at (p<0.01) level. It is recommended that the government and other stakeholders in water sector to ensure that all-time and non-seasonal dependent source of water be provided rather than ground water source which is susceptible to variations in water yields from seasonal variations. This will enable continuous clean water supply, for all purposes, including COVID-19 protocols.

World is now experiencing a major health calamity due to the coronavirus disease (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV- 2). The foremost challenge facing the scientific community is to explore the growth and transmission capability of the virus. Use of artificial intelligence (AI), such as, deep learning, in (i) rapid disease detection from x-ray/computerized tomography (CT)/ high-resolution computed tomography (HRCT) images, (ii) accurate prediction of the epidemic patterns and their saturation throughout the globe, (iii) identification of the epicenter in each country/state and forecasting the disease from social networking data, (iv) prediction of drug-protein interactions for repurposing the drugs, and (v) socio-economic impact and prediction of future relapses, has attracted much attention. In the present manuscript, we describe the role of various AI-based technologies for rapid and efficient detection from CT images complementing quantitative real time polymerase chain reaction (qRT-PCR) and immunodiagnostic assays. AI-based technologies to anticipate the current pandemic pattern, possibility of future relapses and socio-economic impact are also discussed. We inspect how the virus transmits depending on different factors, such as, population density and mobility among others. We depict how AI-based mobile app for contact tracing and surveys can prevent the transmission. A modified deep learning technique can assess affinity of the most probable drugs to treat COVID-19. Here a few effective antiviral drugs, such as, Geneticin, Avermectin B1, and Ancriviroc among others, have been reported with their appropriate validation from previous investigations.

This study was aimed to investigate the effect of capital structure on firms’ profitability with special emphasis on Ethiopian Large Private Manufacturing Firms using panel data of five consecutive years (2006/07-2010/11G.C). The secondary data sources (audited financial statements) have been collected from the randomly selected thirty three large private manufacturing firms in Ethiopia. Linear regression model has been employed to analyze the relationship between firms’ profitability and capital structure. Specifically, Random-effect Generalized Least Square of panel data regression model has been selected to empirically test the literature driven hypotheses. Finally, the findings of this study revealed that a significant positive relationship between firms’ profitability and total debt ratio which indicate firm’s capital structure.

Despite their limited spatial extent, freshwater ecosystems host remarkable biodiversity, including one third of all vertebrate species. This biodiversity is declining dramatically: globally, wetlands are vanishing three times faster than forests and freshwater vertebrate populations have fallen more than twice as steeply as terrestrial or marine populations. Threats to freshwater biodiversity are well- documented but co-ordinated action to reverse this decline is lacking. We present an Emergency Recovery Plan to “bend the curve” of freshwater biodiversity loss. Priorities for action include: 1) accelerating implementation of environmental flows, 2) improving water quality, 3) protecting and restoring critical habitats, 4) managing exploitation of freshwater species and riverine aggregates, 5) preventing and controlling non-native species invasions, and 6) safeguarding and restoring river connectivity. We recommend revised targets and indicators for the Convention on Biological Diversity and the Sustainable Development Goals, and investment in enabling conditions at national, river basin and local scales.

We show the tensor properties of joint probabilities on a Riemannian parametric manifold. Initially we develop a binary data matrix for parameter measurements of a large number of particles confining in a closed system in order to retrieve the joint probability densities of related parameters. By introducing a new generalized inner product as a multilinear operation on dual basis of parametric space, we extract the set of joint probabilities and prove them to meet contravariant tensor properties in a general Riemannian parametric space. We show these contravariant tensors reduce to classical ordinary partial derivatives definition in ordinary Euclidean parametric space. Finally we prove by a theorem that symmetrized iterative contravariant derivative of cumulative probability function on Riemannian manifold gives the set of joint probabilities in those manifold. We bring some examples for compatibility with physical tensors.

Publicly-funded scientists have a responsibility to engage with the public on scientific information, but are lacking a standardized framework and assessment strategy to do it well. The PEPS (Public Engagement Practices for Scientists) Method is an outcomes-centered framework employing standardized pedagogical methods with quantifiable outcomes. This approach reveals that scientists often have unrealistic expectations for achieving affective learning outcomes (i.e. changing views from anti- to pro-vaccine) by solely cognitive learning strategies (i.e. supplying data). The PEPS Method can serve as a roadmap for standardized science communication assessments, complementing existing science communication training programs for the next generation of scientists.

In this paper, quasi-static axial compression tests are performed on the nodal Moso bamboos to study the size effect on energy absorption of the bamboos and the damage pattern of the multiple bamboo columns. Experimental results show that under the same moisture content, growth age and growing environment, the specific energy absorption (SEA) of the test samples increases with the increase of the out-diameter and thickness of the bamboo columns, indicating that size effect exists for energy absorption of the Moso Bamboos. For the multiple bamboo columns, there are mainly three failure modes for the constituent single bamboo columns: splitting above the node, splitting below the node and splitting through the node. Also, the tensile tests are conducted on three kinds of dog-bone shaped bamboo samples to investigate the macroscopic tensile fracture mode in the longitudinal direction of Moso bamboos. Results show that there is no direct relationship between the fracture pattern and moisture content of the bamboos, as well as the growth age of the bamboos. However, the tensile loading rate and the shape of the dog-bone shaped bamboo sample could affect the macroscopic fracture pattern of the bamboos in some cases.

There is a skin allergy epidemic in the western world, and the rate of deterioration has increased significantly in the past 5-10 years. It is probable that there are many environmental contributing factors, yet some studies have linked it primarily to the rise in the use of synthetic chemical ingredients in modern cosmetics. Our challenge, therefore, was to find a mechanism to determine the effect these substances have on skin health, and whether they really are a primary cause of long term damage to the skin. The first problem is the lack of any definitive way to measure skin health. Motivated by the overwhelming evidence for a link between deficient gut flora and ill health, we decided to look at whether our skin microbiota could similarly be used as an indicator of skin health. Our research illustrates how it is microbiota diversity alone that can predict whether skin is healthy or not, after we revealed a complete lack of conclusive findings linking the presence or abundance of particular species of microbe to skin problems. This phenomenon is replicated throughout nature, where high biodiversity always leads to healthy ecosystems. ‘Caveman’ skin, untouched by modern civilisation, was far different to ‘western’ skin and displayed unprecedented levels of bacterial diversity. The less exposed communities were to western practices, the higher the skin diversity, which is clear evidence of an environmental factor in the developed world damaging skin. For the first time we propose benchmark values of diversity against which we can measure skin to determine how healthy it is. This gives us the ability to be able to predict which people are more likely to be prone to skin ailments, and start to test whether cosmetic ingredients and products are a main cause of the skin allergy epidemic.

An approach employing ultrafast laser hybrid subtractive-additive microfabrication combining ablation, 3D nanolithography and welding is proposed for the realization of Lab-On-Chip (LOC) device. Single amplified Yb:KGW fs-pulsed laser source is shown to be suitable for fabricating microgrooves in glass slabs, polymerization of fine-meshes filter out of hybrid organic-inorganic photopolymer SZ2080 inside them, and, lastly, sealing the whole chip with cover glass into a single monolithic piece. The created microfluidic device proved its particle sorting function by separating 1 μm and 10 μm polystyrene spheres in a mixture. All together, this shows that fs-laser microfabrication technology is a flexible and versatile tool for the manufacturing of mesoscale multi-material LOC devices.

With the year 2020, the world faced a new threat that affects all areas of life, negatively affects production in all areas, and paralyzes social life. The measures and restrictions taken by the country's governments to prevent the epidemic from spreading rapidly in the society with the effect of the Covid-19 virus, which first appeared in China and spread all over the world, brought a new lifestyle. Covid-19 has been much the impact on electricity use and electricity production in the period in Turkey as in other countries. There was a sharp decline in commercial and industrial electricity use. The coronavirus effect has also been reflected in the electricity demand and the consumption amount has undergone a great negative change. Due to the enactment of measures against the new type of coronavirus (COVID-19) epidemic and the partial or full-time curfews, electricity consumption was moved to homes, supermarkets, and hospitals in April 2020 from places where mass consumption is intense, such as industry, workplaces, and educational institutions. In this study, Covid-19 period, the first cases were examined electricity production and consumption in Turkey as of the date it is seen throughout, in comparison with electricity consumption data in the same month of the previous years corresponding to this period, the effects on electricity generation and consumption habits of this period were examined.

Real-time estimation of the parameters characterising infectious disease transmission is important for optimization quarantine interventions during outbreaks. One of the most significant parameters is the effective reproduction number - number of secondary cases produced by a single infection. The current study presents an approach for estimating the effective reproduction number and its application to COVID-19 outbreak. The method is based on fitting SIR epidemic model to observation data in a sliding time window and allows to show real-time dynamics of reproduction number at any phase of epidemic for countries globally. Online data on COVID-19 daily cases of infections, recoveries, deaths are used.Finally, time-dependent reproduction number is explored in connection with dynamics of peoples mobility. The method allows to assess the disease transmission potential and understand the effect of interventions on epidemics spread. It also can be easily adapted to future outbreaks of different pathogens. The tool is available online as Python code from the Github repository.

Active ageing is defined as the process of optimizing opportunities for physical, social and mental health to enable older people to take an active part in society without discrimination and to enjoy an independent and good quality of life. The World Health Organization assumed this as a process for increasing and maintaining an individual’s participation in activities to enhance his/her quality of life. In this survey, the authors addressed the following question: “Is assistive technology (AT) for mobility contributing to enhancement of lifelong capacity and performance?”. From June 2015 until February 2016, 96 community dwelling adults, AT users for mobility (powered wheelchairs, manual wheelchairs, lower limb prostheses, walkers, crutches and canes), aged 45-97, mean 67.02 +/- 14.24 years old, 56.3% female, were interviewed using the Psychosocial Impact of Assistive Devices Scale (P-PIADS), the Activities and Participation Profile related to Mobility (APPM) and demographics, clinical and questions about AT use and training. The participants’ profiles revealed moderate limitation and restrictions in participation, measured by the APPM (2.03). Most participants showed positive impact of AT; average scores obtained from the P-PIADS subscales were: Self-esteem 0.62, Competency 1.11 and Adaptability 1.10. P-PIADS total was 0.96, with the powered wheelchair users scoring the highest (1.53) and the walker users scoring the lowest (0.73). All subscales and P-PIADS total were positively correlated with the activities and participation profile. There was no relation between age and the psychosocial impact of AT or activities and participation profile. These results encourage the authors to follow these participants up for a lifelong intervention. To accomplish that aim, currently, the protocol is implemented at the AT prescribing centers in Coimbra, Portugal in order to assess the impact of AT on participation in society, one of the domains of the Active Ageing Index, a new analytical tool to help policy makers in developing policies for active and healthy ageing.

A time-sequential working, spatially multiplexed autostereoscopic 3D display design consisting of a fast switchable RGB-color filter array is presented. The wavelength-selective filter barrier emits the light from a display over a larger active area than common autostereoscopic barrier displays. An optical modelling of wavelength-selective barriers has been used for instance to calculate the light ray distribution properties of that arrangement. To find well working display designs, automated searches by simulation and computational evaluation has been proceeded. Wavelength-selective filter barrier arrangements exhibit characteristics different from common barrier displays with similar barrier pitch and ascent. In particular, these constructions show strong angular luminance dependency under barrier inclination specified by correspondent slant angle. In time-sequential implementation it is important to avoid that quick eye or eyelid movement lead to visible color artifacts. In the millisecond regime tunable liquid crystal Fabry-Pérot color filters for the colors red, green and blue are presented. They consist of a sub-micrometer thick nematic layer sandwiched between dielectric mirrors and ITO-electrodes. These cells shall switch narrow-banding light of red, green or blue. An array for a glasses-free 3D display has to be equipped with several thousand switchable filter elements having different color apertures. The newly introduced design is usable as a multi user display as well as a single user system with user adaptive control.

Copper demand for a country's copper industry has a greater pull effect. China's copper consumption in 2015 has accounted for 50% of the world. The scientific forecast of China's copper demands trend is also an important basis for analyzing its future environmental impact. This paper assumes that China's economy will be developing high, medium and low scenarios, and forecasts economic and social indicators such as total GDP, population and per capita GDP in China from 2016 to 2030. Then, predicted the demand of copper resources in China from 2016 to 2030 by the combination of system dynamics model, vector autoregressive moving average model and inverted U-type empirical model. The results show that: (1) in 2020, 2025 and 2030, China's refined copper demand will be 13 Mt, 15 Mt and 15.5 Mt. (2) China's copper demand growth slowed down significantly from 2016-2030. (3) 2025-2030, China's copper resource demand is stable, into the platform of demand growth, the highest peak value in 2027 will be 15.5 Mt. (4) 2030 years later, China's copper resource demand will enter a slow decline.

The SARS-CoV-2 virus that causes the COVID-19 disease has wreaked havoc on the world community in terms of every imaginable parameter. The research output on COVID-19 has been nothing short of phenomenal, especially in the medical and biomedical sciences, where the search for a potential vaccine is being conducted in earnest. Much of the advanced research has been distributed in the leading medical journals, including the Journal of the American Medical Association (JAMA), where the latest research is distributed on a daily basis. The purpose of this paper is to provide some perspectives on 44 interesting and highly topical research papers that have been published in JAMA, at the time of writing, within the past two weeks. The diverse topics include public health, general medicine, internal medicine, oncology, paediatrics, geriatrics, and biostatistics.

An on-site, ultra-sensitive, and quantitative sensing method was developed based on quantum dot nanobeads (QDNBs) and test strip for the determination of total aflatoxins (AFTs) in rice and peanut. The monoclonal antibody against AFT (mAbAFT) was home-made and labeled with QDNB. After the pre-coating of the AFT antigen on the test line (T line), the competitive immunoreactions were conducted between AFT and AFT antigen on the T line with QDNBs-mAbAFT. Under optimal conditions, this approach allowed a rapid response towards AFT with a considerable sensitivity of 1.4 pg/mL and 2.9 pg/mL in rice and peanut matrices, respectively. The put-in and put-out duration were within 10 min. The recoveries for AFT in rice and peanut samples matrices were recorded from 86.25–118.0% with the relative deviations (RSD) below 12%. The assay was further validated via the comparison between this QDNB strip and the conventional HPLC method using spiked samples. Thus, the design provided a potential alternative for on-site, ultra-sensitive, and quantitative sensing of AFT that could also be expanded to other chemical contaminants for food safety.

We report magnetic silver nanoshells (M AgNSs) that have both magnetic and SERS properties for SERS-based detection. The M AgNSs are composed of hundreds of Fe3O4 nanoparticles for rapid accumulation and bumpy silver shell for sensitive SERS detection by near-infrared laser excitation. The intensity of the SERS signal from the M AgNSs was strong enough to provide single particle-level detection. We obtained much stronger SERS signal intensity from the aggregated M AgNSs than from the non-aggregated AgNSs. 4-Fluorothiophenol was detected at concentrations as low as 1 nM, which corresponds to 0.16 ppb. The limit of detection for tetramethylthiuram disulfide was 10 μM, which corresponds to 3 ppm. The M AgNSs can be used to detect trace amounts of organic molecules using a portable Raman system.

In this study, the efficiency of an anaerobic treatment system for wastewater from a South African poultry slaughterhouse was evaluated using a lab-scale static granular bed reactor (SGBR). The down-flow SGBR (2 L) was operated continuously for 138 days under mesophilic conditions (35-37 ˚C), at hydraulic retention times (HRTs) ranging from 24 to 96 h and average organic loading rates (OLRs) of 0.78 to 5.74 g COD/L.day. The SGBR achieved an average chemical oxygen demand (COD) removal efficiency of 80% and the maximum COD removal achieved was 95%, at an HRT of 24 h and average OLR of 5.74 g COD/L.day. The optimization of the SGBR, with regard to a suitable HRT and OLR, was determined using response surface methodology (RSM). The optimal SGBR performance with regard to the maximum COD removal efficiency was predicted for an OLR of 12.49 g COD/L.day and a HRT of 24 h, resulting in a 95.5% COD removal efficiency. The model R2 of 0.9638 indicated that the model is a good fit and is suitable to predict the COD removal efficiency for the SGBR.

In this paper, we present a complete, flexible and safe convex-optimization-based method to solve speed planning problems over a fixed path for autonomous driving in both static and dynamic environments. Our contributions are five fold. First, we summarize the most common constraints raised in various autonomous driving scenarios as the requirements for speed planner developments and metrics to measure the capacity of existing speed planners roughly for autonomous driving. Second, we introduce a more general, flexible and complete speed planning mathematical model including all the summarized constraints compared to the state-of-the-art speed planners, which addresses limitations of existing methods and is able to provide smooth, safety-guaranteed, dynamic-feasible, and time-efficient speed profiles. Third, we emphasize comfort while guaranteeing fundamental motion safety without sacrificing the mobility of cars by treating the comfort box constraint as a semi-hard constraint in optimization via slack variables and penalty functions, which distinguishes our method from existing ones. Fourth, we demonstrate that our problem preserves convexity with the added constraints, thus global optimality of solutions is guaranteed. Fifth, we showcase how our formulation can be used in various autonomous driving scenarios by providing several challenging case studies in both static and dynamic environments. A range of numerical experiments and challenging realistic speed planning case studies have depicted that the proposed method outperforms existing speed planners for autonomous driving in terms of constraint type covered, optimality, safety, mobility and flexibility.

In this paper, we study the performance of various deep reinforcement learning algorithms to enhance the energy management system of a microgrid. We propose a novel microgrid model that consists of a wind turbine generator, an energy storage system, a set of thermostatically controlled loads, a set of price-responsive loads, and a connection to the main grid. The proposed energy management system is designed to coordinate among the different flexible sources by defining the priority resources, direct demand control signals, and electricity prices. Seven deep reinforcement learning algorithms were implemented and are empirically compared in this paper. The numerical results show that the deep reinforcement learning algorithms differ widely in their ability to converge to optimal policies. By adding an experience replay and a semi-deterministic training phase to the well-known asynchronous advantage actor-critic algorithm, we achieved the highest model performance as well as convergence to near-optimal policies.

Affordable Housing, the basic human necessity has now become a critical problem in global cities with direct impacts on people's well-being. While a well-functioning housing market may augment the economic efficiency and productivity of a city, it may trigger housing affordability issues leading crucial economic and political crises side by side if not handled properly. In global cities e.g. Singapore and Hong Kong where affordable housing for all has become one of the greatest concerns of the Government, this issue can be tackled capably by the provision of public housing. In Singapore, nearly 90% of the total population lives in public housing including public rental and subsidized ownership, whereas the figure tally only about 45% in Hong Kong. Hence this study is an effort to scrutinizing the key drivers of success in affordable public housing through following a qualitative case study based research methodological approach to present successful experience and insight from different socio-economic and geo-political context. As a major intervention, this research has clinched that, housing affordability should be backed up by demand-side policies aiming to help occupants and proprietors to grow financial capacity e.g. subsidized rental and subsidized ownership can be an integral part of the public housing system to improve housing affordability.

Collision avoidance is an important feature in advanced driver-assistance systems, aiming at providing correct, timely and reliable warnings before an imminent collision (objects, vehicles, pedestrians, etc.). A co-simulation framework is proposed in this paper to address the design and evaluation of collision avoidances in a cyber-physical system. The co-simulation framework is supported on the interaction between SCANeR and Matlab/Simulink. From the best of authors’ knowledge, two main contributions are reported in this paper. Firstly, the modelling and simulation of virtual on-chip LIDAR sensors in a cyber-physical system (CPS) considering traffic scenarios is presented. The CPS is designed and implemented in SCANeR. Secondly, an obstacle recognition library with three specific Artificial Intelligence-based methods is also designed based on sensory information database provided by SCANeR. Three methods for collision avoidance detection are considered, i.e.; a multi-layer perceptron neural network, a self-organization map and a support vector machine. Finally, a comparison among these methods for detecting obstacles before different weather conditions is done with very promising results in terms of accuracy. The best results are achieved using the multi-layer perceptron in sunny and fog conditions, the support vector machine in rainy and self-organized map in snowy conditions.

This study was conducted generally by aiming assessment of the hydraulic performance of water distribution systems of Addis Ababa Science and Technology University (AASTU). In line with the main objective, this study addressed, (1) pinpointing problems of existing water supply versus demand deficit (2) evaluating the hydraulic performance of water distribution system using water GEMS and (3) recommended alternative methods for improving water demand scenarios. The University’s water supply distribution network layout was a looped system and the flow of water derived by both gravity and pressurized system. The gravity flow served for the academic and administrative staffs whereas the pressurized system of the network fed the students dormitories, cafeteria’s etc. The study revealed the existence of unmet minimum pressure requirement around the student dormitories which accounts 25.64% below the country’s building code standard during the peak hour consumption. The result of the water demand projection showed an increment of 2.5 liter per capita demand (LPCD) in every five years. Hence, first, the university’s water demand was projected and then hydraulic parameters such as; pressure, head loss and velocity were modeled for both the existing and the improved water supply distribution. The finding of the study was recommended to the university’s water supply project and institutional development offices for its future modification and rehabilitation works.

Demand response flexible loads can provide fast regulation and ancillary services as reserve capacity in power systems. This paper proposes a joint optimization dispatch control strategy for source-load system with stochastic renewable power injection and flexible thermostatically controlled loads (TCLs) and plug-in electric vehicles (PEVs). Specifically, the optimization model is characterized by a chance constraint look-ahead programming to maximal the social welfare of both units and load agents. By solving the chance constraint optimization with sample average approximation (SAA) method, the optimal power scheduling for units and TCL/PEV agents can be obtained. Secondly, two demand response control algorithms for TCLs and PEVs are proposed respectively based on the aggregate control models of the load agents. The TCLs are controlled by its temperature setpoints and PEVs are controlled by its charging power such that the DR control objective can be fulfilled. The effectiveness of the proposed dispatch and control algorithm has been demonstrated by the simulation studies on a modified IEEE 39 bus system with a wind farm, a photovoltaic power station, two TCL agents and two PEV agents.

Opportunistic Routing is a promising paradigm that has been proposed for efficient and reliable transfer of data packets in mobile ad hoc networks. This routing strategy takes advantage of the broadcasting nature of the wireless medium to increase the number of probable forwarding devices and improves the reliability of data transfer in the network. Opportunistic Routing utilizes the reception of the same broadcasted packet at multiple devices in the network and selects one best forwarder dynamically from the set of multiple receivers. A number of opportunistic routing protocols have been proposed over these years for effective data delivery in ad hoc networks. But as the speed and mobility of devices increases in the network, performances of the existing opportunistic routing protocols degrade considerably, leading to reduced Quality of Service and poor transmission efficiency. The exceptional potential of opportunistic routing is thus underutilized. In this research we introduce Optimized Opportunistic Routing (OOR) strategy that guarantee excellent Quality of Service and high transmission efficiency to the latest applications using opportunistic routing for communication in highly dynamic ad hoc networks. Simulation results show that our method achieve significant performance improvements compared to all other existing opportunistic routing protocols in highly dynamic mobile ad hoc networks.

This paper reports 2 × 4 hybrid Mach-zehnder interferometer (MZI) - multi-mode interferometers (MMI) based compact thermo-optical switch consisting of slab waveguides on silicon-on-insulator, SOI, platform. The device consists of two identical MMIs, each of 6 μm wide and 140 μm long connected with two phase shifters MMIs each with 2 μm wide and 8 μm long and linear tappers each 4 μm long, connected at both ends of the MMIs to minimize the power coupling loss. The loss for linear taper is found to be below 0.02dB. The footprint of the whole device is six 6 μm × 324 μm. This structure is based on unique multimode region shape, which leads optical switch to have less coupling loss and reduced cross-talk. The average thermo-optical switching power consumption is 1.4 mW, the excess losses are 0.8 dB, and the imbalances are 0.1 dB. Aluminum is used as a heating pad, and a trench is created around this pad to prevent from spreading of heat and reduce power loss almost by a factor of 2 to the adjacent phase shifter. Our new heating method has advantages of compact size and ease of fabrication with the current CMOS technology.

Electric mobility begins to enter East-African markets. This paper aims to investigate what policy level solutions and stakeholder constellations are established in the context of e-mobility in Dar es Salaam, Kigali, Kisumu and Nairobi and in which ways they attempt to tackle implementation of electric mobility solutions. The study employs two key methods including content analysis of policy and programmatic documents as well as interviews based on purposive sampling ap-proach with stakeholders involved in mobility transitions. The study findings point out that transport operators and their representative associations are less recognized as major players in the transition, far behind new e-mobility players (start-ups) and public authorities. The study further indicates that a set of financial and technical barriers persist such as high upfront invest-ment costs in vehicles and infrastructure, or anxieties regarding competitiveness with fossil fuel vehicles, that constrain the uptake of such private e-mobility initiatives. This study concludes by identifying current gaps that need to be tackled by policy makers and stakeholders in order to implement inclusive electric mobility in East-African cities, considering modalities that include transport providers and address their financial constraints.

Identifying appropriate animal models is critical in developing translatable in vitro and in vivo systems for therapeutic development and investigating disease pathophysiology. These animal models should have direct biological and translational relevance to the underlying disease they are supposed to mimic. Aging dogs naturally develop a cognitive decline in many aspects including learning and memory, but also exhibit human-like individual variability in the aging process. Neurodegenerative processes that can be observed in both human and canine brains include the progressive accumulation of β-amyloid (Aβ) found as diffuse plaques in the prefrontal cortex, including the gyrus proreus, the hippocampus, and in the cerebral vasculature. A growing body of epidemiological data shows that human patients with neurodegenerative diseases have concurrent intestinal lesions, and histopathological changes in the gastrointestinal (GI) tract occurs decades that evolve before neurodegenerative changes. Gut microbiome alterations also have been observed in many neurodegenerative diseases including Alzheimer’s and Parkinson’s diseases, and inflammatory CNS diseases. Interestingly, only recently has the dog gut microbiome been recognized to more closely resemble in composition and in functional overlap with the human gut microbiome as compared to rodent models. This article aims to review the physiology of the gut-brain axis (GBA), and its involvement with neurodegenerative diseases in dogs and humans. Additionally, we outline the advantages and disadvantages of traditional in vitro and in vivo models and discuss future research directions investigating major human neurodegenerative diseases such as Alzheimer’s and Parkinson’s diseases using dogs.

The ongoing COVID-19 global pandemic is affecting every facet of human lives (e.g., public health, education, economy, transportation, and the environment). This novel pandemic and citywide implemented lockdown measures are affecting virus transmission, people’s travel patterns, and air quality. Many studies have been conducted to predict the COVID-19 diffusion, assess the impacts of the pandemic on human mobility and air quality, and assess the impacts of lockdown measures on viral spread with a range of Machine Learning (ML) techniques. This review study aims to analyze results from past research to understand the interactions among the COVID-19 pandemic, lockdown measures, human mobility, and air quality. The critical review of prior studies indicates that urban form, people's socioeconomic and physical conditions, social cohesion, and social distancing measures significantly affect human mobility and COVID-19 transmission. During the COVID-19 pandemic, many people are inclined to use private transportation for necessary travel purposes to mitigate coronavirus-related health problems. This review study also noticed that COVID-19 related lockdown measures significantly improve air quality by reducing the concentration of air pollutants, which in turn improves the COVID-19 situation by reducing respiratory-related sickness and deaths of the people. It is argued that ML is a powerful, effective, and robust analytic paradigm to handle complex and wicked problems such as a global pandemic. This study also discusses policy implications, which will be helpful for policymakers to take prompt actions to moderate the severity of the pandemic and improve urban environments by adopting data-driven analytic methods.

The objective of this work was to investigate the use of remotely sensed vegetation indices to improve the quality of yield maps. The method was applied to the yield data of twelve cornfields from the Data Intensive Farm Management project. The results revealed the need to time shift the yield values up to three seconds to better match the sensor readings with the geographic coordinates. The residuals of the yield prediction model were used to identify points with unlikely yield values for that location, as an alternative to traditional approaches using local spatial statistics, without any assumption of spatial dependence or stationarity. The temporal and spatial distribution of the standardized coefficients for each experimental unit highlighted the presence of trends in the data. At least five out of the twelve fields presented trends that could have been induced by data collection.

Cropland area per capita and pressure index on cropland is the important parameters measuring the social vulnerability and sustainability in the perspective of food security in a certain region in China during the historical periods. This study reconstructs the spatial distribution change of cropland area per labor/household and pressure index on cropland during the 17th-20th century by the methods of historical documents, regression analysis, pressure index model and GIS. Then it analyzed the impacting process of climate change and sustainability of cropland use during the different periods. It draws conclusions: (i) the spatial difference of labor/household density was obvious which had the same pattern as cropland distribution during the same periods, which is higher density in three agricultural areas. (ii) Cropland area per capita was relatively higher during the 17th-18th century, which were above 0.4 ha/person in majority counties and distributed homogenously. Till the 19th century and the beginning of 20th century, cropland area per capita in large amount of regions decreased below 0.2 ha/person embodying the increase of social vulnerability and unsustainability at that time. (iii) Pressure index on cropland also showed the similar spatial pattern as cropland area per capita which presented lower threshold than nowadays. During the 17th-18th century there was no pressure on cropland. While, in the 19th century and at the beginning of 20th century, two high value centers of pressure index on cropland appeared in Middle Shandong and the Jiaodong region, pressure on sustainable cropland use increased obviously and a food crisis is probably created. (iv) Higher sustainable extent of cropland use corresponds to cold period, and lower sustainable extent of cropland use corresponds to warm period in Shandong over the past 300 years. The turning point of 1680s from dry to wet attributes to the decrease of sustainable extent of cropland use in Shandong not very distinctively. More and more pressure on sustainability of cropland use finally since the beginning of 20th century would intensify the social conflict and increase the probability of social revolts.

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.

The creation of a Europe-wide electricity market combined with the increased intermittency of supply from renewable sources calls for an investigation into the risk of aggregate peak demand. This paper makes use of a risk model to assess differences in time-use data from residential end-users in five different European electricity markets. Drawing on the Multinational Time-Use Survey database, it assesses risk in relation to the probability of electrical appliance use within households for five European countries. Findings highlight in which countries and for which activities the risk of aggregate peak demand is higher and link smart home solutions (automated load control, dynamic pricing and smart appliances) to different levels of peak demand risk.

Abstract: Cardiovascular Disease (CVD) is the most prevalent disease worldwide, which has been linked to work stress because of poor job design as explained by the Job Demand-Control (JDC) and the Effort-Reward Imbalance (ERI) models. In this paper we explore for the first time relative impact of a specific aspect of organisational climate, Psychosocial Safety Climate (PSC), on any CVD including angina, myocardial infarction, hypertension, and stroke. We used two waves of interview data from Australia, with an average lag of 5 years (excluding baseline CVD, final n = 1223). Logistic regression was conducted to estimate the prospective associations between PSC at baseline on incident CVD at follow-up. It was found that participants in low PSC environments were 59% more likely to develop new CVD than those in high PSC environments. Logistic regression showed that PSC at baseline predicts lower CVD risk at follow-up (OR = 0.98, 95% CI 0.96-1.00), and this risk remained unchanged even after joint adjustment for measures of ERI and JDC. These results suggest that PSC is an independent risk factor for CVD in Australia. Beyond job design this study implicates organisational climate and prevailing management values regarding worker psychological health as the genesis of CVD.

In the present study, a hybrid optimizing algorithm has been proposed using Genetic Algorithm (GA)and Particle Swarm Optimization (PSO) for Artificial Neural Network (ANN) to improve the estimation of electricity demand of the state of Tamil Nadu in India. The GA-PSO model optimizes the coefficients of factors of gross state domestic product (GSDP) , electricity consumption per capita, income growth rate and consumer price index (CPI) that affect the electricity demand. Based on historical data of 25 years from 1991 till 2015 , the simulation results of GA-PSO models are having greater accuracy and reliability than single optimization methods based on either PSO or GA. The forecasting results of ANN-GA-PSO are better than models based on single optimization such as ANN-BP, ANN-GA, ANN-PSO models. Further the paper also forecasts the electricity demand of Tamil Nadu based on two scenarios. First scenario is the "as-it-is" scenario , the second scenario is based on milestones set for achieving goals of "Vision 2023" document for the state. The present research also explores the causality between the economic growth and electricity demand in case of Tamil Nadu. The research indicates that a direct causality exists between GSDP and the electricity demand of the state.

Human-Computer Interaction, including technology-aided instruction, is beginning to focus on virtual reality (VR) technology due to its ability to support immersive learning, teaching through simulation, and gamification of learning. These systems can deliver high-level multisensory learning experiences that are important in the teaching of many subjects, especially those involving abstract concepts or requiring spatial skills, such as organic chemistry. Haptic experiences with VR, however, remain a challenge. In addition, development have focused on general entertainment/gaming; VR systems in chemistry implement simulations of the chemistry laboratory and other advanced systems whereas those that support safe, game-like, immersive and multisensory learning of organic chemistry with haptics at pre-university education levels are scarce. We developed the VR Multisensory Classroom (VRMC) as an immersive learning environment within a VR head-mounted display, where learners employ hand movements to build hydrocarbon molecules and experience haptic feedback through gloves with built-in sensors and hand-tracking with the Leap Motion system. We report here the evaluation of the first prototype by learners from diverse backgrounds who reported on the ability of the VRMC to support high engagement, motivation, interest and organic chemistry learning as well as diverse learning styles. The VRMC is a novel VR classroom that supports immersive learning in molecular organic chemistry with haptics for multisensory learning.

Besides the Lagrangian and the Eulerian descriptions, the motion of a body can also be expressed relative to the present configuration of the body, known as the relative motion description. It is interesting to consider such a relative motion description in general to formulate the basic system of field equations for solid bodies. In doing so, when the time increment from the present state is small enough, the nonlinear constitutive equations can be linearized relative to the present state so that the resulting system becomes linear. This will be done for thermoelastic materials with a brief comment on the exploitation of entropy principle in general. Relative Lagrangian formulation is based on the well-known ``small-on-large'' idea, and can be implemented for solving problems with large deformation in successive incremental manner. Some applications of such a formulation in numerical simulations are briefly reviewed.

In the present study, a hybrid optimizing algorithm has been proposed using Genetic Algorithm (GA)and Particle Swarm Optimization (PSO) for Artificial Neural Network (ANN) to improve the estimation of electricity demand of the state of Tamil Nadu in India. The GA-PSO model optimizes the coefficients of factors of gross state domestic product (GSDP) , electricity consumption per capita, income growth rate and consumer price index (CPI) that affect the electricity demand. Based on historical data of 25 years from 1991 till 2015, the simulation results of GA-PSO models are having greater accuracy and reliability than single optimization methods based on either PSO or GA. The forecasting results of ANN-GA-PSO are better than models based on single optimization such as ANN-BP, ANN-GA, ANN-PSO models. Further the paper also forecasts the electricity demand of Tamil Nadu based on two scenarios. First scenario is the “as-it-is” scenario, the second scenario is based on milestones set for achieving goals of “Vision 2023” document for the state. The present research also explores the causality between the economic growth and electricity demand in case of Tamil Nadu. The research indicates that a direct causality exists between GSDP and the electricity demand of the state.

Background: Schizophrenia is a severe psychiatric disorder that has a large impact on patients’ lives. In addition to Western medicine, the use of additional treatments, such as acupuncture, in treating the positive, negative, and cognitive symptoms is increasing. Methods: We conducted a systematic review on the use of acupuncture as an add-on treatment for patients with schizophrenia that are in regular care, with a special focus on the treatment of the often accompanying co-morbid sleep disorders. In this study, we searched the Medline, ScienceDirect, Cochrane Library, Scopus, and ERIC databases with a cut-off date of December 31, 2017, thereby following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) protocol. Results: Our search resulted in 26 eligible studies. Most studies showed limited evidence for the use of acupuncture as add-on therapy in the treatment of clinical symptoms, but beneficial effects have been reported in the treatment of co-morbid sleep disorders. Conclusions: Limited evidence was found for the use of acupuncture as add-on therapy in the treatment of patients with schizophrenia; however, positive results were found in the treatment of sleep disorders, but this result needs to be confirmed in large, randomized, controlled trials.

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

The Smart Energy Research Lab (SERL) Observatory dataset described here comprises half-hourly and daily electricity and gas data, SERL survey data, Energy Performance Certificate (EPC) input data and 24 local hourly climate reanalysis variables from the European Centre for Medium-Range Weather Forecasts (ECMWF) for over 13,000 households in Great Britain (GB). Participants were recruited in September 2019, September 2020 and January 2021 and their smart meter data are collected from up to one year prior to sign up. Data collection will continue until at least August 2022, and longer if funding allows. Survey data relating to the dwelling, appliances, household demographics and attitudes was collected at sign up. Data are linked at the household level and UK-based academic researchers can apply for access within a secure virtual environment for research projects in the public interest. This is a data descriptor paper describing how the data was collected, the variables available and the representativeness of the sample compared to national estimates. It is intended as a guide for researchers working with or considering using the SERL Observatory dataset, or simply looking to learn more about it.

Climate change has become the greatest threat to the survival of world and its ecosystem. With the irreversible impact on the ecosystem, problems like rise in sea level, food-insecurity, natural resources scarcity, seasonal disorders have increased over the past few years. Among these problems, the issue of water scarcity due to the lack of water resources and global warming has plagued several nations. Owing to the rising concerns over water scarcity United Nations (UN) has acknowledged water as a primary resource to the development of societies under the ‘Water Goal’ of the sustainable development goals. As the changing climate and intermittent availability of water resources pose major challenges to forecast demand, especially in countries like the United Arab Emirates (UAE) which has one of the highest per capita residential water consumption rates in the world. Therefore, the aim of this study is to propose an accurate water demand forecasting technique that incorporates all significant factors to predict the future water demands of the UAE. The forecasting model used is the Long Short Term Memory (LSTM), with the factors considered are mean temperature, mean rainfall, relative humidity, Gross Domestic Product (GDP), Consumer Price Index (CPI) and population growth. The LSTM model predicts the water demand forecasting in the UAE showing that the future demand will decrease from 1821 million m3 in 2018 to 1809.9 million m3 in 2027.

The African bush and forest elephants, Loxodonta Africana and Loxodonta cyclotis, and the Asian elephant, Elephas maximus, are the largest land-dwelling animals. Elephants need to be highly mobile and active in order to find fresh food and water, and in the case of males, to locate females in estrus for breeding. Asian elephants walk up to 21 km each day and African elephants can walk up to 28 km per day. This high level of mobility in the wild is also important for maintaining an optimum musculoskeletal health. However, most zoo elephants live in restricted spaces and cold climates that require extended periods of indoor confinement are therefore unable to be as physically active. Zoo enclosures for elephants are relatively small with hard surfaces (i.e. concrete, tarmac and hard packed dirt), so they cannot exercise and are forced to stand on unnaturally hard surfaces continually. Physical inactivity in captivity makes them more prone to gaining weight and developing bone and joint diseases such as osteomyelitis, joint ankylosis and osteoarthritis (OA). Many health and welfare problems in captive elephants are likely to be caused by the lack of mobility. This perspective article focuses on the possible link between captivity, mobility, physical inactivity and the development of OA in captive elephants.

This is a quantitative research based on secondary sources of data. The study examines the influence of Chief Executive Officer’s (CEO) compensation on a firm's performance. The objectives of the study were to determine if CEO compensation and firm size do significantly influence a firm’s performance. In other to elicit information to examine the relationship between the variables, the convenience sampling technique, with the combination of both the cross-sectional and time-series data (panel data) were used since they provide greater precision and guard against having an illusory sample. 10 banks quoted on the Nigerian Stock Exchange were sampled for easy accessibility of data. The least square regression technique was used to test the hypotheses of the study. Two hypotheses were tested using panel least square (EViews 8) and from the research work, we summarize the following results; there is a significant relationship between CEO compensation and firm performance in the Nigerian banking industry. In addition, firm size does significantly influence firm performance in the Nigerian banking industry. The study recommends that there should be proper compensation review as this will increase the productivity of the executives. Since increased pay is necessary for the efficiency of the workers, it is advised to ensure a considerable pay as this will ensure for efficiency in the organization. In addition, since the core goal of setting up any business is to make a profit, business organisations should sort out ways at maximising profit and this could include cutting down expenses such as cutting down excessive employees’ pay (CEOs pay especially) and setting apposite pay package for employees. Therefore, policymakers (board of directors) should make an effort to align CEO’s paywith the firm’s capability to pay.

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.

Underwater wireless sensor networks (UWSN) have recently been proposed as a way to monitor and explore the water depths' environments. Efficiently delivering the data is still a challenging problem in these networks because of the weaknesses in the acoustic transmission. To tackle such a problem, we propose a novel algorithm provides controlling mechanisms for critical long-term data forwarding underwater sensor networks, called Hop by Hop Power-Efficient Routing Protocol (Hⁿ-PERP). The proposed Hⁿ-PERP is a centralized full-control model that enhances the network's throughput and energy efficiency by a set of solutions depend on power monitoring in UWSN nodes. In particular, the model provides a guaranteed mechanism for scheduling and processing data transmission based on number of nodes, hops between the nodes, energy level and congestion within each node to minimize energy levels or power consumption by avoiding disconnected probability for any node, which in turn maximizing the network lifetime. Simulation results show that our proposed model is consistent with energy level and congestion, and is more accurate for enabling routing and data transmission. Therefore, the data packet delivery ratio and overall throughput also achieves robust scenarios of very sparse or/and weak networks, to keep on Performance stability in UWSN via adjusting hop-by-hop delay and energy consumption during packages delivery.

Peer-to-peer (P2P) energy trading could help address grid management challenges in a decentralizing electricity system, as well as providing other social and environmental benefits. Many existing and proposed trading schemes are enabled by blockchain, a distributed ledger technology (DLT) relying on cryptographic proof of ownership rather than human intermediaries to establish energy transactions. This study used an online survey experiment (n=2064) to investigate how consumer demand for blockchain-enabled peer-to-peer energy trading schemes in the United Kingdom varies depending on how the consumer proposition is designed and communicated. The analysis provides some evidence of a preference for schemes offering to meet a higher proportion of participants’ energy needs, and for those operating at the city/region (as compared to national or neighbourhood) level. People were more likely to say they would participate when the scheme was framed as being run by their local council, followed by an energy supplier, community energy organization, and social media company. Anonymity was the most valued DLT characteristic and mentioning blockchain’s association with Bitcoin led to a substantial decrease in intended uptake. We highlight a range of important questions and implications suggested by these findings for the introduction and operation of P2P trading schemes.

Mobility as a Service (MaaS) is an innovative transport concept, anticipated to provide travelers with different kinds of travel services, more sustainable than a private car, in a simpler, packaged way. It combines different transport modes to offer a tailored mobility package, like a monthly mobile phone contract. The rapid development of intelligent transportation system and the shared economy has speeded up the development of MaaS in these years. In this paper, we aim at classifying the existing research on MaaS and the characteristics of MaaS into different categories, in order to answer the following questions after reviewing the existing literature: What is MaaS? Who are the main actors in MaaS? How can MaaS be implemented? Why should it be implemented? Where will MaaS end up in this wave of disruption? When we talk about MaaS, what are we focusing on? What is the future leading frequency of MaaS? Finally, based on the existing literature, we envision the leading future of MaaS.

This study analyzes the relationship between the spread of the SARS-CoV-2 virus (COVID-19) and the mobility patterns of the Portuguese population. By reducing mobility, the idea is that contacts are reduced, countering the spread of the virus in the community. As an indicator of the spread of the virus, the reproduction number (Rt) was used. Data from Google's Community Mobility Reports was used to evaluate changes in mobility patterns. This report uses location data from Android mobile phone users. The locations are divided into retail and recreation, grocery and pharmacy, parks, transit stations, workplaces and residential. In this year of the COVID-19 crisis in Portugal, population mobility patterns have changed over the various phases of the pandemic. At first, all mobility was affected uniformly, with the population avoiding much of the activity outside the home. In a second phase, there was some adaptation, and the areas considered to be of lower risk had less impact, emphasizing the changes in the relationship between daily life and the workplace.

Prior to lockdown the spread of COVID-19 in UK is found to be exponential, with an exponent α=0.207 In case of COVID-19 this spreading patterns is quantitatively better described with mobility-driven SIR-SEIR model [2] rather than the homogenous mixing models Lockdown has dramatically slowed down the spread of COVID-19 in UK, and even more significantly has changed the growth in the total number of infected from exponential to quadratic. This significant change is due a transition from a mobility-driven epidemic spreading to a spatial epidemic which is dominated by slow growth of spatially isolated clusters of infected population. Our results strongly indicated that, to avoid a return to exponential growth of COVID-19 (also known as “second wave”) mobility restrictions should not be prematurely lifted. Instead mobility should be kept restricted while new measures, such as wearing mask and contact tracing, get implemented in order to allow a safe exit from lockdown.

The COVID-19 pandemic has put lifestyles in question, changed daily routines and limited citizen freedoms that seemed inalienable before. A human activity that was greatly affected since the beginning of the health crisis is mobility. Focusing on mobility, we aim to discuss the transformational impact that the pandemic brought on this specific urban domain, especially with regards to the promotion of the smart growth agenda and the acceleration towards the smart city paradigm. We collect 60 initial policy responses related to urban mobility from 86 cities around the world and analyse them based on the challenge they aim to address, the exact principles of smart growth and sustainable mobility that they encapsulate and the level of ICT penetration. Our findings suggest that emerging strategies, although mainly temporary, are transformational, in line with the principles of smart growth. As a result the pandemic becomes an opportunity for shifting towards more sustainable urban planning and mobility practices. However, most policy responses adopted during the first months of the pandemic fail to leverage advancements made in the field of smart cities, and to adopt off-the-shelf solutions such as in monitoring, alerting and operations management.

The illegal use of explosives by terrorists and other criminals is an increasing issue in public spaces, such as airports, railway stations, highways, sports arenas, theaters, and other large buildings. Security in these environments can be achieved by a set of different means, including the installation of scanners and other analytical devices to detect ultra-small traces of explosives in a very short time-frame to be able to take action as early as possible to prevent the detonation of such devices. Unfortunately, an ideal explosive detection system still does not exist, which means that a compromise is needed in practice. Most detection devices lack the extreme analytical sensitivity, which is nevertheless necessary due to the low vapor pressure of nearly all explosives. In addition, the rate of false positives needs to be virtually zero, which is also very difficult to achieve. Here we present an immunosensor system based on kinetic competition, which is known to be very fast and may even overcome affinity limitation, which impairs the performance of many traditional competitive assays. This immunosensor consists of a monolithic glass column with a vast excess of immobilized hapten, which traps the fluorescently labeled antibody as long as no explosive is present. In the case of TNT occurring, some binding sites of the antibody will be blocked, which leads to an immediate breakthrough of the labeled protein, detectable by highly sensitive laser-induced fluorescence with the help of a Peltier-cooled CMOS camera. Liquid handling is performed with high-precision syringe pumps and chip-based mixing-devices and flow-cells. The system achieved limits of detection of 1 pM (1 ppt) of the fluorescent label and around 100 pM (20 ppt) of the explosive 2,4,6-trinitrotoluene (TNT). The total assay time is less than 8 min. A cross-reactivity test with 5000 pM solutions showed no signal by PETN, RDX, and HMX. This immunosensor belongs to the most sensitive and fastest detectors for TNT with no significant cross-reactivity by non-related compounds.

Since its inception in the late 2000s, blockchain has emerged as a powerful tool for creating trust without intermediaries to incentivize global communities for working for a common goal, such as the improvement of its very ecosystem, its applications and community adoption. While first blockchains were mainly devised for confirming transactions of their innate cryptocurrencies like Bitcoin, smart-contract blockchains like Ethereum can interface with the real-world through so-called “oracles”, which feed trustful off-chain information. This paper introduces digital twins of physical objects and processes as computational oracles to effectively unleash the tremendous opportunity offered by blockchain to the realm of fundamental science, research and technology development (RTD). The crowdsourcing concept is illustrated with the example of centrifugal flow control in microfluidic “Lab-on-a-Disc” (LoaD) systems.

The Smart Park (well-known as Taman Pintar) as a major educational tourism destination in Jogjakarta offers a variety of tourism attractions that are very interesting for tourists. The main purpose of tourists visiting Smart Park is to get an educational tourism experience. A subjective experience raises a specific challenge for Smart Park toward the status of competitive destination. The purpose of this study is to analyze the aspects of educational tourism experience that are affected by tourism demand and supply. Data were collected by survey technique to 150 respondents and analyzed using path analysis. The results of analysis show that tourism demand and supply contributed to the variation of tourism activities by 45.1%, while the remaining was explained by other variables, such as National Budget, Local Budget, ticket sale, and cooperation with some stakeholders. Tourism supply had a higher effect than tourism demand. Tourism demand did not partially affect tourism experience. However, the results of the path analysis indicate that tourism supply had direct and indirect effects on tourism experience through the variation of tourism activities with indirect effect being dominant. In the management of Smart Park, there is still a gap between tourism demand and supply, so that the environment of tourism experience has not been created maximally.

Electricity demand prediction is vital for energy production management and proper exploitation of the present resources. Recently, several novel machine learning (ML) models have been employed for electricity demand prediction to estimate the future prospects of the energy requirements. The main objective of this study is to review the various ML models applied for electricity demand prediction. Through a novel search and taxonomy, the most relevant original research articles in the field are identified and further classified according to the ML modeling technique, perdition type, and the application area. A comprehensive review of the literature identifies the major ML models, their applications and a discussion on the evaluation of their performance. This paper further makes a discussion on the trend and the performance of the ML models. As the result, this research reports an outstanding rise in the accuracy, robustness, precision and the generalization ability of the prediction models using the hybrid and ensemble ML algorithms.

In this work modeling and analysis of an integrated opto-fluidic sensor, with a focus on achievement of single mode optical confinement and continuous flow of micro particles in the microfluidic channel for Lab-on-a Chip (LOC) sensing application is presented. This sensor consists of integrated optical waveguides, microfluidic channel among other integrated optical components. A continuous flow of micro particles in a narrow fluidic channel is achieved by maintaining the two sealed chambers at different temperatures and by maintaining a constant pressure of 1Pa at the centroid of narrow fluidic channel geometry. The analysis of silicon on insulator (SOI) integrated optical waveguide at an infrared wavelength of 1550nm for single mode sensing operation is presented. The optical loss is found to be 0.0005719dB/cm with an effective index of 2.2963. The model presented in this work can be effectively used to detect the nature of micro particles and continuous monitoring of pathological parameters for sensing applications.

In this work we identify combinations of technological activities that signal the presence local capabilities in a country to successfully export a product. We use country-level patent and trade data to generate a multi-layer network, and we apply maximization of entropy to generate synthetic data to effectively divide signal from noise. We show that in several sectors the signal far exceed the noise. Our exercise provides robust evidence of the presence of synergies between technologies to explain trade performances in specific markets. This can be highly useful for policy makers, to inform industrial and innovation policies.

A Demand-side management technique are deployed along with battery energy-storage systems (BESSs) to lower the electricity cost by mitigating the peak load of a building. Most of the existing methods rely on manual operation of the BESS, or even an elaborate building energy-management system resorting to a deterministic method that is susceptible to unforeseen growth in demand. In this study we propose a real-time optimal operating strategy for BESS based on density demand forecast and stochastic optimization. This method takes into consideration uncertainties in demand when accounting for an optimal BESS schedule, making it robust compared to the deterministic case. The proposed method is verified and tested against existing algorithms. Data obtained from a real site in South Korea is used for verification and testing. The results show that the proposed method is effective, even for the cases where the forecasted demand deviates from the observed demand

Relationships between drought and fire danger indices are examined to 1) incorporate fire risk information into the National Integrated Drought Information System California-Nevada Drought Early Warning System and 2) provide a baseline analysis for application of drought indices into a fire risk management framework. We analyzed four drought indices that incorporate precipitation and evaporative demand (E₀) and three fire indices that reflect fuel moisture and potential fire intensity. Seasonally averaged fire danger indices were most strongly correlated to multi-scalar drought indices that use E₀ (the Evaporative Demand Drought Index [EDDI] and Standardized Precipitation Evapotranspiration Index [SPEI]) at approximately annual time scales that reflect buildup of antecedent drought conditions. Results indicate that EDDI and SPEI can inform seasonal fire potential outlooks at the beginning of summer. An E₀ decomposition case study of conditions prior to the Tubbs Fire in Northern California indicate high E₀ (97th percentile) driven predominantly by low humidity signaled increased fire potential several days before the start of the fire. Initial use of EDDI by fire management groups during summer and fall 2018 highlights several value-added applications, including seasonal fire potential outlooks, funding fire severity level requests, and assessing set-up conditions prior to large, explosive fire cases.

Surface Plasmon Resonance (SPR) based sensors have the advantage of being label-free, enzyme-free and real-time. However, their spreading in multidisciplinary research is still limited and almost confined to prism-coupled devices. Plasmonic gratings, combined with a simple and cost-effective instrumentation, have been poorly developed compared to prism-coupled system mainly due to their lower sensitivity. Here we describe the optimization and signal enhancement of a sensing platform based on phase-interrogation method, which entails the exploitation of a nanostructured sensor. This technique is particularly suitable for integration of the plasmonic sensor in a lab-on-a-chip platform and can be used in a microfluidic circuit to ease the sensing procedures and limit the injected volume. The careful optimization of most suitable experimental parameters by numerical simulations leads to a 30 to 50% enhancement of SPR response, opening new possibilities for applications in the biomedical research field while maintaining the ease and versatility of the configuration.

It is difficult to capture the real-time online measurement data for biochemical oxygen demand (BOD) in wastewater treatment processes. An optimized extreme learning machine (ELM) based on an improved cuckoo search algorithm (ICS) is proposed in this paper for the design of soft BOD measurement model. In ICS-ELM, the input weights matrices of the extreme learning machine (ELM) and the threshold of the hidden layer are encoded as the cuckoo's nest locations. The best input weights matrices and threshold are obtained by using the strong global search ability of improved cuckoo search (ICS) algorithm. The optimal results can be used to improve the precision of forecasting based on less number of neurons of the hidden layer in ELM. Simulation results show that the soft sensor model has good real-time performance, high prediction accuracy and stronger generalization performance for BOD measurement of the effluent quality compared to other modeling methods such as back propagation (BP) network in most cases.

The investigation of the electromagnetic properties of biological particles in microfluidic platforms may enable wireless monitoring and interaction with functional activity of microorganisms. Of high relevance is the membrane potential as it is one of the most important parameters of living cells. In particular, the complex mechanisms of the cell’s membrane potential are comparable to the dynamics of bacteria membranes, providing a simplified platform for advancing the current techniques and knowledge of general bio-particle dynamics. In this work, we provide a theoretical analysis and experimental results on the microwave detection of bacteria on a microfluidic-based framework for sensing the membrane potential of bacteria. The results enable to further advance the state-of-the-art of electromagnetic bacteria sensing and microfluidic control, and their implication for measuring and interacting with the cell and its membrane potentials, which is of great importance for developing new biotechnological engineered systems and solutions.

This paper aims to describe the geometrical structure and explicit expressions of family of finitely parametrized probability densities over smooth manifold $M$. The geometry of family of probability densities on $M$ are inherited from probability densities on Euclidean spaces $\left\{U_\alpha \right\}$ via bundle morphisms, induced by an orientation-preserving diffeomorphisms $\rho_\alpha:U_\alpha \rightarrow M$. Current literature inherits densities on $M$ from tangent spaces via Riemannian exponential map $\exp: T_x M \rightarrow M$; densities on $M$ are defined locally on region where the exponential map is a diffeomorphism. We generalize this approach with an arbitrary orientation-preserving bundle morphism; we show that the dualistic geometry of family of densities on $U_\alpha$ can be inherited to family of densities on $M$. Furthermore, we provide explicit expressions for parametrized probability densities on $\rho_\alpha(U_\alpha) \subset M$. Finally, using the component densities on $\rho_\alpha(U_\alpha)$, we construct parametrized mixture densities on totally bounded subsets of $M$. We provide a description of inherited mixture product dualistic geometry of the family of mixture densities.

Magnetic nanoparticles are met across many biological species ranging from magnetosensitive bacteria, fishes, bees, bats, rats, birds, to humans. They can be both of biogenetic origin and due to environmental contamination, being either in paramagnetic or ferromagnetic state. The energy of such naturally occurring single-domain magnetic nanoparticles can reach up to 10-20 room k_BT in the magnetic field of the Earth, which naturally led to supposition that they can serve as sensory elements in various animals. This work explores within a stochastic modeling framework a fascinating hypothesis of magnetosensitive ion channels with magnetic nanoparticles serving as sensory elements, especially, how realistic it is given a highly dissipative viscoelastic interior of living cells and typical sizes of nanoparticles possibly involved.

We introduce a modelling framework for the investigation of on-line machine learning processes in non-stationary environments. We exemplify the approach in terms of two specific model situations: In the first, we consider the learning of a classification scheme from clustered data by means of prototype-based Learning Vector Quantization (LVQ). In the second, we study the training of layered neural networks with sigmoidal activations for the purpose of regression. In both cases, the target, i.e. the classification or regression scheme, is considered to change continuously while the system is trained from a stream of labeled data. We extend and apply methods borrowed from statistical physics which have been used frequently for the exact description of training dynamics in stationary environments. Extensions of the approach allow for the computation of typical learning curves in the presence of concept drift in a variety of model situations. First results are presented and discussed for stochastic drift processes in classification and regression problems. They indicate that LVQ is capable of tracking a classification scheme under drift to a non-trivial extent. Furthermore, we show that concept drift can cause the persistence of sub-optimal plateau states in gradient based training of layered neural networks for regression.

This paper generalizes the actuator disc theory to the application of crosswind kite power systems. For simplicity, it is assumed that the kite sweeps an annulus in the air, perpendicular to the wind direction (i.e. straight downwind configuration with tether parallel to the wind). It is further assumed that the wind flow has a uniform distribution. Expressions for power harvested by the kite is obtained, where the effect of the kite on slowing down the wind (i.e. the induction factor) is taken into account. It is shown that although the induction factor may be small for a crosswind kite (of the order of a few percentage points), neglecting it in calculations may result in noticeable overestimation of the amount of power harvestable by a crosswind kite system.

The ongoing revolution in Deep Learning is redefining the nature of computing that is driven by the increasing amount of pattern classification and cognitive tasks. Specialized digital hardware for deep learning still holds its predominance due to the flexibility offered by the software implementation and maturity of algorithms. However, it is being increasingly desired that cognitive computing occurs at the edge, i.e. on hand-held devices that are energy constrained, which is energy prohibitive when employing digital von Neumann architectures. Recent explorations in digital neuromorphic hardware have shown promise, but offer low neurosynaptic density needed for scaling to applications such as intelligent cognitive assistants (ICA). Large-scale integration of nanoscale emerging memory devices with Complementary Metal Oxide Semiconductor (CMOS) mixed-signal integrated circuits can herald a new generation of Neuromorphic computers that will transcend the von Neumann bottleneck for cognitive computing tasks. Such hybrid Neuromorphic System-on-a-chip (NeuSoC) architectures promise machine learning capability at chip-scale form factor, and several orders of magnitude improvement in energy efficiency. Practical demonstration of such architectures has been limited as performance of emerging memory devices falls short of the expected behavior from the idealized memristor-based analog synapses, or weights, and novel machine learning algorithms are needed to take advantage of the device behavior. In this work, we review the challenges involved and present a pathway to realize ultra-low-power mixed-signal NeuSoC, from device arrays and circuits to spike-based deep learning algorithms, with ‘brain-like’ energy-efficiency.

The purpose of this work is to define optimal growth conditions for batch culture of the cyanobacterium Arthrospira maxima and the microalgae Chlorella vulgaris, Isochrysis galbana and Nannochloropsis gaditana. Thus, we study the effect of three variables on algae growth: i.e., inoculum:culture medium ratio, light:darkness photoperiod and type of culture medium, including both synthetic media and wastewaters. The results showed that the initial inoculum volume did not affect the amount of biomass at the end of the growth (14 days), whereas an excess (18 h) or defect (6 h) in the number of hours of light is determinant for its development. The contribution of nutrients from different culture media modified the growth of the different species. A. maxima was favoured in seawater enriched with Guillard's F/2 as well as C. vulgaris and N. gaditana but in fresh water medium. I. galbana had the greatest growth in the marine environment enriched with Walne’s media. Nitrate was the limiting growth reagent at the end of the exponential phase of growth for C. vulgaris and N. gaditana, while iron was for A. maxima and I. galbana. All species demonstrated their capability to grow in effluents from a wastewater treatment plant and they efficiently consume nitrogen, especially the three microalgae species.

The air is not the same as thousands and hundreds of years ago. In the air, suspended particles originate from natural phenomena like dust storms or volcanic activities, as well as anthropogenic pollutants such as fuel engine exhaust and everyday activities at home. The total particles in the air can be classified by sizes, such as PM10, PM2.5 or ultrafine particles. However, there are many other important factors in addition to the particle size, influencing the particle behavior and affecting our health. The surface area, chemical and biological composition, aspect ratio, and the charge are all factors characteristic of particles. OoC microfluidic chips are very useful for the pollutant toxicity measurements on various body tissues. A better understanding of pollutants will help to trace these to the potential sources. The data from the on-the-ground and satellite monitoring can be integrated into models, helping to predict and prevent pollution exposure.

Energy storage systems will play a key role for individual users in the future smart grid. They serve two purposes: (i) handling the intermittent nature of renewable energy resources for a more reliable and efficient system, and (ii) preventing the impact of blackouts on users and allowing for more independence from the grid, while saving money through load-shifting. In this paper we investigate the latter scenario by looking at a neighbourhood of 25 households whose demand is satisfied by one utility company. Assuming the users possess lithium-ion batteries, we answer the question of how each household can make the best use of their individual storage system given a real-time pricing policy. To this end, each user is modelled as a player of a non-cooperative scheduling game. The novelty of the game lies in the advanced battery model, which incorporates charging and discharging characteristics of lithium-ion batteries. The action set for each player comprises day-ahead schedules of their respective battery usage. We analyse different user behavior and are able to obtain a realistic and applicable understanding of the potential of these systems. As a result, we show the correlation between the efficiency of the battery and the outcome of the game.

In this work, an algorithm for the scheduling of household appliances to reduce the energy cost and the peak-power consumption is proposed. The system architecture of a home energy management system (HEMS) is presented to operate the appliances. The dynamics of thermal and non-thermal appliances is represented into state-space model to formulate the scheduling task into a mixed-integer-linear-programming (MILP) optimization problem. Model predictive control (MPC) strategy is used to operate the appliances in real-time. The HEMS schedules the appliances in a dynamic manner without any a priori knowledge of the load-consumption pattern. At the same time, HEMS responds to the real-time electricity market and the external environmental conditions (solar radiation, ambient temperature etc). Simulation results exhibit the benefits of proposed HEMS by showing the reduction of up to 47% in electricity cost and up to 48% in peak power consumption.

Assisted reproductive technology has been developed significantly throughout the past few years, particularly diagnosing and treating male infertility. Many studies have been performed showing that Intracytoplasmic Sperm Injection (ICSI) is a successful method to attain clinical pregnancy and live birth through impaired spermatozoa characteristics or low sperm count, such as severe oligospermia. Severe oligospermia indicates low sperm count, which in some cases leads to azoospermia. Severe oligospermia can be caused by several factors such as genetics or medication. In search of efficient treatment for couples with Severe oligospermia, numerous retrospective and prospective researches have reported high pregnancy and live birth rates through testicular sperm for men with severe oligospermia and cryptozoospermia with or without high sperm DNA damage. The research showed that the use of testicular sperm in combination with ICSI yielded a high pregnancy rate and live births over another source of sperm, such as ejaculated sperms. Moreover, the use of ICSI in severe oligospermia has shown successful fertilization and pregnancy.

Thanks to advanced semiconductor microfabrication technology, chip-scale integration and miniaturization of lab-on-a-chip components, silicon-based optical biosensors have made significant progress for the purpose of point-of-care diagnosis. In this review, we provide an overview of the state-of-the-art in evanescent field biosensing technologies including interferometer, microcavity, photonic crystal, and Bragg grating waveguide-based sensors. Their sensing mechanisms and sensor performances, as well as real biomarkers for label-free detection, are exhibited and compared. We also review the development of chip-level integration for lab-on-a-chip photonic sensing platforms, which consist of the optical sensing device, flow delivery system, optical input and readout equipment. At last, some advanced system-level CMOS-chip packaging examples are presented, indicating the commercialization potential for the low cost, high yield, portable biosensing platform leveraging CMOS processes.

This paper examines an important problem in smart grid energy scheduling; peaks in power demand are proportionally more expensive to generate and provision for. The issue is exacerbated in local microgrids that do not benefit from the aggregate smoothing experienced by large grids. Demand-side scheduling can reduce these peaks by taking advantage of the fact that there is often flexibility in job start times. We focus attention on the case where the jobs are non-preemptible, meaning once started, they run to completion. The associated optimization problem is called the Peak Demand Minimization problem and has been previously shown to be NP-hard. Our results include an optimal fixed-parameter tractable algorithm, a polynomial-time approximation algorithm, as well as an effective heuristic that can also be used in an online setting of the problem. Simulation results show these methods can reduce peak demand by up to 50% versus on-demand scheduling for household power jobs.

Recent developments in optical biosensors based on integrated photonic devices are reviewed with a special emphasis on silicon-on-insulator ring resonator. The review is mainly devoted to the following aspects: (1) Principles of sensing mechanism, (2) sensor design, (3) biofunctionalization procedures for specific molecule detection and (4) measurement set-ups and advances in chip-integration. The inherent challenges of implementing photonics-based biosensors to meet specific requirements of applications in medicine, food analysis, and environmental monitoring are discussed.

Cold chain logistics of Agricultural Products demand forecasting can provide the scientific basis for the country to formulate logistics strategy, which further promotes the development of social economy and the improvement of living standards in China. In this paper, a new mathematical combined model is proposed to Agricultural Products Demand. Shandong, one of a China’s province, serves as the main producer and distributor of agricultural products. Based on the index system created from multiple related factors influencing cold chain logistics demand of agricultural products in Shandong, this paper employs principal component analysis to reduce the dimension of various indexes and predicts principal components with time series. Thereafter, multiple linear regression model and neural network model were constructed to forecast the cold chain logistics demand of agricultural products in Shandong, and their combined forecast models were compared. What's more, the paper provides insight for reference and decision-making concerning the development of cold chain logistics industry of agricultural products in Shandong province.

If the world’s countries seriously tackle the climate targets agreed in Paris, their citizens are likely to experience substantial changes in production, consumption and employment. We present a long-run post-Keynesian model for studying the potential implications of a major transition on macroeconomic stability and employment. It is a demand-led model in which firms have considerable but not absolute freedom to administer prices, while household consumption exhibits inertia. Firms continually seek input-saving technological improvements that, in the aggregate, tie technological progress to firms' cost structure. Together with firm pricing strategies and wage setting, the productivities of different inputs determine the functional income distribution. Saving and investment, and production and purchase of consumption goods, are undertaken by different economic actors, driven by income and capacity utilization, with the possibility that productive capacity exceeds, or falls short of, effective demand. The model produces business cycles and long waves driven by technological change. We present results for a “downshifting” scenario in which households voluntarily withdraw labor and discuss the implications of downshifting for stability, growth, and employment. We contrast the downshifting scenario with ones in which households reduce consumption without withdrawing from the labor pool.

Wind turbine plants are complex dynamic and uncertain processes driven by stochastic inputs and disturbances, as well as different loads represented by gyroscopic, centrifugal, and gravitational forces. Moreover, as their aerodynamic models are nonlinear, both modelling and control become challenging problems. On one hand, high-fidelity simulators should contain different parameters and variables in order to accurately describe the main dynamic system behaviour. Therefore, the development of modelling and control for wind turbine systems should consider these complexity aspects. On the other hand, these control solutions have to include the main wind turbine dynamic characteristics without becoming too complicated. The main point of this paper is thus to provide two practical examples of development of robust control strategies when applied to a simulated wind turbine plant. Experiments with the wind turbine simulator and the Monte–Carlo tools represent the instruments for assessing the robustness and reliability aspects of the developed control methodologies when the model-reality mismatch and measurement errors are also considered. Advantages and drawbacks of these regulation methods are also highlighted with respect to different control strategies via proper performance metrics.

Various type of soils have been identified based on their electrical and magnetic properties, especially with regards to peat soils. Peat soils are commonly considered as partly decomposed vegetation. In this study, electrical and magnetic properties have been used in K-means clustering to identify layers of peat soils. K-means clustering is a partitioning method that treats observations in the data. Data cores were obtained at every centimeter and examined for their electrical conductivity (σ) and magnetic susceptibility (χ_m) properties. A 291 cm core was obtained at Tegal Arum Village in South Kalimantan, Indonesia. The K-means clustering results indicate two different layers at 148 cm, and this is supported by loss on ignition (LOI) measurements. In the first layers, a 87.65% LOI was found associated with peat soils (above 248 cm). Whereas, in the second layers, there was a 26.11% LOI associated with mineral soils (below 248 cm). The results of this study using K-means clustering can be used to delineate soil layers.

In Bangladesh, microcredit program has been in function for almost half a century. Though popularly termed as a tool for poverty alleviation and women empowerment, criticism about microcredit program’s actual effectiveness isn’t new. This study attempts to evaluate the impact of such programs by measuring borrowers’ social mobility. Using a multidimensional approach, indicators like household income, economic susceptibility, living conditions, consumption pattern, children’s education, healthcare facilities, women’s participation in decision-making were taken into account for measurement. Quantitative method was used. Based on a non-probability sampling, 107 microcredit borrowers were selected for conducting interview schedules who live in a rural, a semi-urban and an urban area. Statistical analysis of data reveals that those who have been taking loans for several number of years have all managed to increase their income level to a variety of extent, but only those have gained some sort of mobility who have been taking loans for more than 4-5 years and have taken 5 times or more. Few of them have actually moved from microcredit to become a microfinance client by taking bigger amount of loans and having savings. Positive responses about the indicators being used in the study were found among the handful of those who have mobilized significantly. In true sense, most of the borrowers are stuck at the bottom end of socio-economic ladder and are struggling to manage a better living standard.

The COVID-19 pandemic starting in the first half of 2020 has changed the lives of everyone across the world. Reduced mobility was essential due to it being the largest impact possible against the spread of the little understood SARS-CoV-2 virus. To understand the spread, a comprehension of human mobility patterns is needed. The use of mobility data in modelling is thus essential to capture the intrinsic spread through the population. It is necessary to determine to what extent mobility data convey the same message of mobility within a region. This paper compares different mobility data sources by constructing spatial weight matrices and further compares the results through hierarchical clustering. This provides insight for the user into which data provides what type of information and in what situations a particular source is most useful.

Microfluidic lab-on-chip devices are widely being developed for chemical and biological studies. One of the most commonly used types of these chips is perfusion microwells for culturing multicellular spheroids. The main challenge in such systems is the formation of substantial necrotic and hypoxic zones within the cultured spheroids. Herein, we propose a novel acoustofluidic integrated platform to tackle this bottleneck problem. We show that such an approach enhances cell viability and shrinks necrotic and hypoxic zones in these spheroid-on-a-chip platforms without the need to increase the flow rate, leading to a significant reduction in costly reagents' consumption. Proof-of-concept, designing procedures, and finite element numerical simulation are discussed in details. Also, the effects of acoustic and hydrodynamic parameters on the cultured cells are investigated. The results show that by increasing acoustic boundary displacement amplitude (d0), the spheroid’s proliferating zone enlarges greatly. Moreover, it is shown that by implementing d0=0.5 nm, the required flow rate to maintain the necrotic zone below 13% will be decreased 12 times compared to non-acoustic chips.

The reliable online analysis of volatile compounds in exhaled breath remains a challenge as a plethora of molecules occur in different concentration ranges (i.e. ppt to %), and need to be detected against an extremely complex background matrix. While this complexity is commonly addressed by hyphenating a specific analytical technique with appropriate preconcentration and/or preseparation strategies prior to detection, we herein propose the combination of three analytical tools based on truly orthogonal measurement principles as an alternative solution: field-asymmetric ion mobility spectrometry (FAIMS), Fourier-transform infrared (FTIR) spectroscopy-based sensors utilizing substrate-integrated hollow waveguides (iHWG), and luminescence sensing (LS). These three tools have been integrated into a single compact analytical platform suitable for online exhaled breath analysis. The analytical performance of this prototype system was tested via artificial breath samples containing nitrogen (N2), oxygen (O2), carbon dioxide (CO2) and acetone as a model volatile organic compound (VOC) commonly present and detected in breath. Functionality of the combined system was demonstrated by detecting these analytes in their respectively breath-relevant concentration range and mutually independent of each other generating orthogonal yet correlated analytical signals. Finally, adaptation of the system towards the analysis of real breath samples during future studies is discussed.

This study empirically investigates the complex interplay between the severity of the coronavirus pandemic, mobility changes in retail and recreation, transit stations, workplaces, and residential areas, and lockdown measures in 88 countries of the word. To conduct the study, data on mobility patterns, socioeconomic and demographic characteristics of people, lockdown measures, and coronavirus pandemic were collected from multiple sources (e.g., Google, UNDP, UN, BBC, Oxford University, Worldometer). A Structural Equation Modeling (SEM) technique is used to investigate the direct and indirect effects of independent variables on dependent variables considering the intervening effects of mediators. Results show that lockdown measures have significant effects to encourage people to maintain social distancing. However, pandemic severity and socioeconomic and institutional factors have limited effects to sustain social distancing practice. The results also explain that socioeconomic and institutional factors of urbanity and modernity have significant effects on pandemic severity. Countries with a higher number of elderly people, employment in the service sector, and higher globalization trend are the worst victims of the coronavirus pandemic (e.g., USA, UK, Italy, and Spain). Social distancing measures are reasonably effective at tempering the severity of the pandemic.

A numerical approach for generating a limited number of water demand scenarios and estimating their occurrence probabilities in a Water Distribution Network (WDN) is proposed. This approach makes use of the demand scaling laws in order to consider the natural variability and spatial correlation of nodal consumptions. The scaling laws are employed to determine the statistics of nodal consumption as a function of the number of users and the main statistical features of the unitary user's demand. Besides, consumption at each node is considered to follow a Gamma probability distribution. A high number of groups of cross-correlated demands, i.e., scenarios, for the entire network were generated using Latin Hypercube Sampling (LHS) and the numerical procedure proposed by Iman and Conover. The Kantorovich distance is used to reduce the number of scenarios and estimate their corresponding probabilities, while keeping the statistical information on nodal consumptions. By hydraulic simulation, the whole number of generated demand scenarios was used to obtain a corresponding number of pressure scenarios on which the same reduction procedure was applied. The probabilities of the reduced scenarios of pressure were compared with the corresponding probabilities of demand.

The information on the land use and soil conservation practice based on year 2006, 2010 and 2014, hence offering an opportunity to model the impacts of land use change on erosion, deposition and surface water runoff. Limitation in the use of hydrological models had been their inability to handle the large amount of input data that describe the heterogeneity of the natural system. In this study, a procedure that takes into account soil conservation practice based on the land use change, the response of soil erosion and sediment export from the George Town Conurbation catchment area, and average annual sediment yields were estimated for each grid cell of the watershed to identify the critical erosion areas of rural and urban planning proposes. Average annual sediment yield and data on a grid basis estimated using Universal Soil Loss Equation (USLE) and an emerging technology represented by Geographic Information System (GIS) used as a tool to produce a map for erosion rate. The changing of the land use from forest to agriculture and then to an urban area is a challenging task to research on land use demand for population, and environmental impact assessment is important for the planning of natural resources management, allowing research the modification of land use properly and implement more sustainable for long term management strategies. The challenge is to formulate strategies that would promote an integrated approach to the land use planning at an appropriate level as to address the issues that arose. Modelling for creating urban growth boundary for the George Town Conurbation must have to be controlled surface runoff and soil loss and sediment export from land use of the George Town Conurbation catchment.

In this paper a full state feedback control of a double inverted pendulum on a cart (DIPC) are designed and compared. Modeling is based on Euler-Lagrange equations derived by specifying a Lagrangian, difference between kinetic and potential energy of the DIPC system. A full state feedback control with H infinity and H 2 is addressed. Two approaches are tested: open loop impulse response and a double inverted pendulum on a cart with full state feedback H infinity and H 2 controllers. Simulations reveal superior performance of the double inverted pendulum on a cart with full state feedback H infinity controller.

Opportunistic routing uses the broadcasting nature of the wireless medium to increase the number of potential forwarding nodes in the network. This, in turn improves the delivery rate and reliability of data transmission in the network. Compared to all previous classes of protocols, opportunistic routing offers numerous advantages which is exploited by the latest applications for efficient communication and resource sharing in dynamic ad hoc networks. The objective of this research work is to review and classify all the major opportunistic routing protocols proposed for dynamic ad hoc networks. Further the issues and challenges with each of these existing protocol is discussed and future research directions are put forward.

The construction of different roads, such as freeways, highways, major roads or minor roads must be accompanied by constant monitoring and evaluation of service delivery. Pavements are generally assessed by engineers in terms of the smoothness, surface condition, structural condition and surface safety. Pavement assessment is often conducted using the qualitative indices such as international roughness index (IRI), pavement condition index (PCI), structural condition index (SCI) and skid resistance value (SRV), which are used for smoothness assessment, surface condition assessment, structural condition assessment, and surface safety assessment, respectively. In this paper, Tehran-Qom Freeway in Iran has been selected as the case study and its smoothness and pavement surface conditions are assessed. At 2-km intervals, a 100-meter sample unit is selected in the slow-speed lane (totally, 118 sample units). In these sample units, the PCI is calculated after a visual inspection of the pavement and the recording of distresses. Then, in each sample unit, the average IRI is computed. The purpose of this study is to provide a method for estimating PCI based on IRI. The proposed theory was developed by Random Forest (RF), and Random Forest optimized by Genetic Algorithm (RF-GA) methods and these methods were validated using correlation coefficient (CC), scattered index (SI), and Willmott’s index of agreement (WI) criteria. The proposed method reduces costs, saves time and eliminates the safety risks.

A novel drive current model covering the effects of source/drain voltage (VDS) and gate voltage (VGS) and incorporating drift and diffusion current on the surface channel at the nano-node level, especially beyond 28nm node is presented. The effect of the diffusion current added is more satisfactory to describe the behavior of the drive current in nano-node MOSFETs, fabricated with the atomic-layer-deposition (ALD) technology. This breakthrough in model establishment can expose the long and short channel devices together. Introducing the variables of VDS and VGS, the mixed current model more effectively and meaningfully demonstrates the drive current of MOSFETs under the operation of horizontal, vertical, or mixed electrical field. In comparison between the simulation and experimental consequences, the electrical performance is impressive. The error between both is less than 1%, better than the empirical adjustment to issue a set of drive current models.

Three-dimensional (3D) cell culture systems can be regarded as suitable platforms to bridge the huge gap between animal studies and two-dimensional (2D) monolayer cell culture to study chronic diseases such as cancer. In particular, the preclinical platforms for multicellular spheroid formation and culture can be regarded as ideal in vitro tumor models. The complex tumor microenvironment such as hypoxic region and necrotic core can be recapitulated in 3D spheroid configuration. Cells aggregated in spheroid structures can better illustrate the performance of anti-cancer drugs as well. Various methods have been proposed so far to create such 3D spheroid aggregations. Both conventional techniques and microfluidic methods can be used for generation of multicellular spheroids. In this review paper, we first discuss various spheroid formation phases. Then, the conventional spheroid formation techniques such as bioreactor flasks, liquid overlay and hanging droplet technique are explained. Next, a particular topic of the hydrogel in spheroid formation and culture is explored. This topic has received less attention in the literature. Hydrogels entail some advantages to the spheroid formation and culture such as size uniformity, the formation of porous spheroids or hetero-spheroids as well as chemosensitivity and invasion assays and protecting from shear stress. Finally, microfluidic methods for spheroid formation and culture are briefly reviewed.

According to the changed operative conditions due to lockdown and successive reopening a number of facts can be analysed. The main effects have been detected on: mobility, environment, social media and people flows. While in this first report only mobility, transport and environment are reported. The analysis performed identified a strong reduction of the mobility and transport activities, and in the pollutants. The mobility reduction has been assessed to be quite coherent with respect to what has been described by Google Global mobility report. On the other hand, in this paper a number of additional aspects have been put in evidence providing detailed aspects on mobility and parking that allowed us to better analyze the impact of the reopening on an eventual revamping of the infection. To this end, the collected data from the field have been compared from those of google and some considerations with respect to the Imperial college Report 20 have been derived. For the pollutant aspects, a relevant reduction on most of them has been measured and rationales are reported.

The use of naturally available materials not conforming to traditional specifications or standards, in the base and sub-base layers of road pavement structures, stabilised with New-age (Nano) Modified Emulsions (NME), have been tested, implemented and successfully verified through Accelerated Pavement Testing (APT) in South Africa. This was made possible through the development and use of a design procedure addressing fundamental principles and based on scientific concepts, which are universally applicable. The understanding of and incorporation of the chemical interaction between the mineralogy of the materials and a NME stabilising agent (compatibility between the chemistry of the reactive agents and material mineralogy) into the design approach is key to achieving the required engineering properties. Stabilised materials evaluation is done using tests indicative of the basic engineering properties (physics) of compressive strengths, tensile strengths and durability. This article describes the basic materials design approach developed to ensure that organofunctional nano-silane modified emulsions can successfully be used for pavement layer construction utilising naturally available materials, at a low risk. The enablement of the use of naturally available materials in all pavement layers can have a considerable impact on the unit cost and life-cycle costs of road transportation infrastructure. TRANSLATE with x English ArabicHebrewPolish BulgarianHindiPortuguese CatalanHmong DawRomanian Chinese SimplifiedHungarianRussian Chinese TraditionalIndonesianSlovak CzechItalianSlovenian DanishJapaneseSpanish DutchKlingonSwedish EnglishKoreanThai EstonianLatvianTurkish FinnishLithuanianUkrainian FrenchMalayUrdu GermanMalteseVietnamese GreekNorwegianWelsh Haitian CreolePersian TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster Portal Back

Access and Benefit Sharing (ABS) is a framework which refers to a relatively recent type of legal requirements for access to and/or use of “genetic resources”. They are based on diverse national and regional laws and regulations, which mostly result from the implementation of the United Nations’ Convention on Biological Diversity (CBD) and its supplementary agreement, the Nagoya Protocol. Their ambition is to achieve fair and equitable sharing of benefits arising out of the use of genetic resources as an incentive to conserve and sustainably use them. This paper describes the experiences, practical constraints and complexities encountered by users of genetic resources when dealing with ABS legislation, with a focus on users from the private sector. We provide insights on how ABS laws have fundamentally changed the way of working with genetic resources in the hope that it inspires re-thinking of the ABS framework, to better support the overall objectives of the CBD.

Anthropogenic change is associated with population growth, land use change, and changing economies. However, internal migration patterns and armed conflicts are also key drivers behind anthropogenic and demographic processes. To better understand this sort of change, we explore the spatial relationship between forced migration due to armed conflict and changing demographic factors in Colombia, a country which has a recent history of 7 million internal migrants. In addition, we use remote sensing, Google Earth Engine, as well as spatial statistical analyses of demographic data in order to measure anthropogenic change between 1984 and 2008; and we look into spatiotemporal relationships between both demographic and anthropogenic changes, which are caused by forced migration. We find, thus, that the latter is significantly and positively related to an increasing rural-urban kind of migration which originates in armed conflict, and results show that it is also negatively associated with interregional expulsion. Indeed, anthropogenic prints (term hereafter used to denote changes in nighttime satellite imagery) pertaining to different regions have had different sensitivities towards forced migration, and across different time periods. Finally we discuss how social and political phenomena such as Colombia’s armed conflict can have significant effects on the dynamics and motions of humans and territories in countries of the Global South.

As the emission regulations get more and more stringent in the different fields of energy and environmental systems, the electric and fuel cell vehicles (FCV) have attracted growing attention by automakers, governments, and customers. Research and development efforts have been focused on devising novel concepts, low-cost systems, and reliable electric/fuel cell powertrain. In fact, electric and fuel cell vehicles coupled with low-carbon electricity sources offer the potential for reducing greenhouse gas emissions and exposure to tailpipe emissions from personal transportation. In particular, Pedal Assisted Bicycles (PAB) popularity is rising in urban areas due to their low energy consumption and environmental impact. In fact, when electrically moved, they are zero emission vehicles with very low noise emissions, as well. These positive characteristics could be even improved by coupling a PAB with a fuel cell based power generation system, thus increasing the vehicle autonomy without influencing their emissions and consumption performances. In this paper, four types of vehicles are compared from an environmental and accessibility point of view: conventional car, bus, electric PAB and hydrogen fuel cell PAB; for such vehicles, the respective utilization stages are accounted for, i.e. without considering the manufacturing process. The analysis has been carried out comparing different vehicles performance along different routes of an Italian middle-size city, Viterbo, which represents a very good pilot case as its Municipality is adopting many solutions suggested by European Union (EU) through the planning tool called Sustainable Energy Action Plan (SEAP). The comparison is based on an ad-hoc developed mathematical procedure, which includes environmental (greenhouse gas and air pollution emissions), health (pollutants toxicity levels) and accessibility time (waiting times) indicators. According to this analysis, electric and fuel cell PAB exhibit interesting advantages over the other vehicles. However, the global economic efficiency of electric or fuel cell apparatus depends substantially on the exploited source of electrical energy.

Separation and interpretation of rebellious Circulating Tumor Cells (CTCs) originating from the primary tumor or cancer tissue plays a significant role in diagnostics, cancer progression analyses, suitable medicine exploration, and treatment proficiency examination. Cancer metastasis occurs when CTCs spread throughout the body and invade healthy tissues, leading to new tumors in that area. Although a dramatic rate of deaths begins from spreading CTCs around the body, valuable measures have been made to control their development. However, the first step is separating these harmful cells from the bloodstream and investigating their features. Having examined the characteristics of CTCs as cancer’s main strength, researchers can introduce complementary treatments that can affect cancerous cells without damaging the healthy cells. Therefore, according to their unique characteristics, numerous techniques have been established for continuous and fast separation and sorting of CTCs. Nevertheless, few separators enjoy the efficient performance and appropriate accuracy and can be produced in mass numbers due to the available fabrication equipment. Microfabrication advancements enable separators to combine the advantages of active and passive methods in a small-scale platform for probing individual cells and separation purposes. Reduction in reagents, sample volume, analysis time, and less harmfulness to patients are some of the motivations that encourage researchers to employ microfluidic instruments for CTCs separation from other blood cells over the last two decades. However, microfabrication limitations mean effective separators, and the diagnostic option they provide, are not readily available. Addressing these limitations requires optimizing the design and fabrication of separators such that they are reduced in their size and fabrication cost, while also maintaining high-throughput separating capability. The emergence of the Lab-On-a-Chip (LOC) and then Lab-On-a-CD (LOCD) technologies, having more inherent benefits than conventional microfluidic devices, has created new opportunities and become increasingly widespread in recent years. Evidence suggests that employing single methodologies or integrating approaches without sufficient understanding of potential outcomes is unlikely to result in successful diagnostic results. This paper contributes an extensive review of several separation systems, including fundamental theories and experimental details, and describes detailed operating principles and device performance.

Aneuploidy should compromise cellular proliferation but paradoxically favours tumour progression and poor prognosis. Here, we consider this paradox in terms of our most recent observations of chemo/radio-resistant cells undergoing reversible polyploidy. The latter perform segregation of two parental groups of end-to-end linked dyads by pseudo-mitosis creating tetraploid cells through a dysfunctional spindle. This is followed by autokaryogamy and homologous pairing preceding a bi-looped endo-prophase. The associated RAD51 and DMC1/γ-H2AX double-strand break repair foci are tandemly situated on the AURKB/REC8/kinetochore doublets along replicated chromosome loops, indicative of recombination events. MOS-associated REC8-positive peri-nucleolar centromere cluster organises a monopolar spindle. The process is completed by reduction divisions (bi-polar or with radial cytotomy including pedogamic exchanges) and release of secondary cells and/or formation of an embryoid. Together this process preserves genomic integrity and chromosome pairing, while tolerating aneuploidy by by-passing the mitotic spindle and meiotic SC checkpoints. Concurrently, it reduces the chromosome number and facilitates recombination that decreases the mutation load of aneuploidy and lethality in the chemo-resistant tumour cells. This cancer life-cycle has parallels both within the cycling polyploidy of the asexual life cycles of ancient unicellular protists and cleavage embryos of early multicellulars, supporting the atavistic theory of cancer.