In Korea, rice is a major staple grain and is mainly cultivated using various agricultural machinery. Air pollutants emitted from agricultural machinery have their origins mainly from the exhaustion of internal combustion engines. In this study, emission characteristics of five main air pollutants by European Environment Agency's Tier 1 method for rice cultivation was analyzed. Diesel is a main fuel for agricultural machinery and gasoline is generally used only for rice transplanters as a fuel in Korea. Tractors consume 46% of total fuel consumption and 56% of diesel fuel consumption. Gasoline used for rice transplanters accounts for 17% of total fuel consumption each year. Tractors and rice transplanters are emitting 82% of all total pollutants. From 2011 to 2019, the total amount of air pollutant emissions was decrease by 15%. That accounted for the reduction of rice cultivation fields in those periods. Rice transplanting operation was in charge of 42% of total emissions. Then, harrowing, harvesting, tilling, leveling, and pest control operations generated 10%, 10%, 8%, 8% and 7% of total emissions, respectively. The contribution of each air pollutant held 54% of CO, 39% of NOx, 5% of NMVOC, and 2% of TSP from the total emission inventory. The three major regions emitting air pollutants from mechanized agricultural practices were Jeollanam-do, Chungcheongnam-do, and Jeollabuk-do, which consume 55% of total fuel usage in rice farming. The total amount of air pollutant emissions from rice cultivation practices in 2019 was calculated as 8,448 Mg in Korea.

Microgrid is one of the promising green transition technologies that will provide enormous benefit to the seaport, as a solution to the major concerns in this sector, namely energy crisis, economical and environmental pollution. However, finest design of the microgrid is a challenging task considering different objectives, constraints and uncertainties involved. To ensure the optimal operation of the system, determining the right configuration framework and size for each component in the seaport microgrid at the minimum cost is a vital decision at the design stage. This paper aims to design a hybrid system of seaport microgrid with optimally sized component .The selected case study is the Port of Aalborg, Denmark. The proposed grid-connected structure consists of renewable energy sources (photovoltaic system and wind turbines), an energy storage system and cold ironing as seaport’ loads. The architecture is then optimized by utilizing HOMER to meet the maximum load demand by considering a few parameters such as solar global horizontal irradiance, temperature and wind resources. Then, the best configuration framework is analyzed in terms of economic feasibility, energy reliability and environmental impact.

As a consequence of the increasing share of renewable energies and sector coupling technologies, new approaches are needed for the study, planning, and control of modern energy systems. Such new structures may add extra stress to the electric grid, as is the case with heat pumps and electrical vehicles. Therefore, the optimal performance of the system must be estimated considering the constraints imposed by the different sectors. In this research, a dispatch optimization method with an iterative grid constraint generation, decoupled from the linear unit commitment problem, is employed. From the considered scenarios, it was found that in a typical German neighborhood with 150 households, PV penetration of ∼5kWp per household can lead to curtailment of ∼60MWh per year due to line loading. Furthermore, the proposed method eliminates grid violations due to the addition of new sectors reducing the curtailment up to 60%. With the optimization of the heat pump operation, an increase of 7% of the self-consumption was achieved with similar results for the combination of battery systems and electrical vehicles. In conclusion, a safer and optimal operation of a complex energy system is fulfilled. Safer control strategies and more accurate plant sizing could be derived from this work.

The aim of this research is to expand recent developments in the mapping of pasture yield with remotely piloted aircraft systems to that of satellite-borne imagery. Up to date, spatially explicit and accurate information of the pasture resource base is needed for improved climate-adapted livestock rangeland grazing. This study developed deep learning predictive models of pasture yield, as total standing dry matter in tonnes per hectare (TSDM(tha−1)), from field measurements and both remotely piloted aircraft systems and satellite imagery. Repeated remotely piloted aircraft system structure measurements derived from structure from motion photogrammetry, provided measures of pasture biomass from many overlapping high-resolution images. Repeated remotely piloted aircraft system measures throughout a growing season, were modelled with persistent photosynthetic pasture responses from various Planet Dove high spatial resolution satellite image-derived vegetation indices. Pasture height modelling as an input to the modelling of yield was assessed against terrestrial laser scanning and reported correlation coefficients (R2) from 0.3 to 0.8 for both a coastal grassland and inland woodland pasture. Accuracy of the predictive modelling from both the remotely piloted aircraft system and the Planet Dove satellite image estimates of pasture yield ranged from 0.8 to 1.8 TSDM(tha−1). These results indicated that the practical application of repeated remotely piloted aircraft system derived measures of pasture yield can, with some limitations, be scaled-up to satellite-borne imagery to provide more temporally and spatially explicit measures of the pasture resource base.

Objectives: In order to solve the problem that most of the existing research focuses on the binary tactile attributes of objects,which ignores the tactile attribute strength and category recognition,an attribute strength and category recognition method based on convolutional neural network matrix-label is proposed. Methods:Firstly,in the data preparation stage,we preprocess the raw data and determine the matrix labels to build the haptic dataset.Secondly,in the feature extraction stage,we fuse the haptic data of two fingers and use the convolutional neural network to extract the attribute strength features.Finally,in the attribute strength and category recognition stage,all channel haptic data is fused to predict the attribute strength and category.Results:We compared with the multi-label convolutional neural network method in terms of elastic strength,hardness strength and category,and compared the attribute strength recognition capabilities of the two methods using novel objects outside the haptic dataset.The results show that the accuracy of the last 20 iterations of the matrix-label method has an average elastic strength of 96.73%,hardness strength of 97.34%,and category of 96.67%.The performance is better.When the Euclidean distance between the prediction of the novel object and the real label is less than 1,the accuracy of the elastic strength is best to reach 100%,and the hardness strength is best to reach 100%.The performance is better. Conclusions:The effectiveness of the method has been verified.Comparing with the convolutional neural network method,our method can effectively recognize the attribute strength and category of objects.

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

Volcanism powered by tidal forces inside celestial bodies can provide enough energy to keep important solvents for living systems in the liquid phase. Moreover, tidal forces and their environmental consequences may strongly influence habitability of planets and other celestial bodies and may result in special forms of live and living conditions. A prerequisite to calculate such tidal interactions and consequences is depending on simulations for tidal accelerations in a multi-body system. Unfortunately, from measurements in many extrasolar planetary systems only few physical and orbital parameters are well enough known for investigated celestial bodies. For calculating tidal acceleration vectors under missing most orbital parameter exactly, a simulation method is developed that is only based on a few basic parameters, easily measurable even in extrasolar planetary systems. Such a method as being presented here, allows finding a relation between the tidal acceleration vectors and potential heating inside celestial objects. Using values and results of our model approach to our solar system as a “gold standard” for feasibility allowed us to classify this heating in relation to different forms of volcanism. This “gold standard” approach gave us a classification measure for the relevance of tidal heating in other extrasolar systems with a reduced availability of exact physical parameters. We would help to estimate conditions for the identification of potential candidates for further sophisticated investigations by more complex established methods like viscoelastic multi-body theories. As a first example, we applied the procedures developed here to the extrasolar planetary system TRAPPIST-1 as an example to check our working hypothesis.

The purpose of this paper is to find the time optimal control to a target set for semilinear stochastic functional differential equations involving time delays or memories under general conditions on a target set and nonlinear terms even though the equations contain unbounded principal operators. Our research approach is construct a fundamental solution for corresponding linear systems and establish variations of constant formula of solutions for given stochastic equations. The existence result of time optimal controls for one point target set governed by the given semilinear stochastic equation is also established.

The study is an attempt to enquire into the preference of undergraduate students, after a considerable and over a year-long experience with Online Teaching and Learning (OTL), under the emergency preventive measure of switching from the traditional face-to-face classes to online. The study followed an exploratory approach, with a quantitative survey followed by a qualitative one, and a convenient sampling method to collect responses from a substantial sample size. The study is positioned after more than a year of remote classes by the undergraduate student, and hence represents highly experienced reflections and preferences from these students, as compared to other studies conducted last year. The study has profound implications in considering, and questioning, the importance of on-campus classes, and significance of the physical presence of a tutor in the class, and its effect on the learning experience of undergraduate students.

Education is the bedrock of any nation. It is essential for individual and societal growth and development. This triggers the demand for quality education which simultaneously increases with a growing demand for quality assurance. This study investigated the role of quality assurance in achieving the expected outcome of education in Bosnia and Herzegovina’s higher education system. Comparative development is looked at by examining other National Qualification Frameworks. The study was based on textual reviews and descriptive analysis on student enrollment and programs of studies. Unlike the global trend, it can be seen that student enrollment is on the decline in Bosnia and Herzegovina. It was found that quality assurance is an indispensable tool for strengthening the higher education system and for achieving the desired change and outcomes, that education offers. It was also found that study programs and curricula are pivotal for teaching and learning processes, and that a unified qualification framework is essential for achieving set objectives of education at all levels. The study recommended, among other things, that sound quality assurance systems as well as appropriate structure for monitoring and accreditation be put in place and be judiciously followed in order to achieve the desired outcomes in conjunction with the set objectives of higher education. Moreover, vital thoroughly analyzed pressures (i.e., democratic, economic, and systemic) barriers show signs of epistemological, political, and institutional barriers in the country.

Remote sensing and Geographical Information System (GIS) have played an important role in exploration and management of groundwater resources. In this study, we present modeling of groundwater potential zone in Khoyrasol block in Birbhum district, West Bengal by using remote sensing and GIS techniques. The objective of the study is to explore groundwater as well as surface water availability in different geomorphic units. Different thematic maps of geology, hydro-geomorphology, lineament, slope, land use/land cover (LULC), depth to water level and soil maps are prepared and groundwater potential zones are obtained by overlaying all thematic maps in terms of Weightage Index Overlay (WIO) method. All the thematic map classes have been assigned weightage according to their role in groundwater occurrence. Finally, groundwater potential zones are classified into four categories viz., excellent, good to medium, medium to poor and poor. The outcome of the present research work will help the local farmers, decision-maker, researchers and planners for exploration, monitoring, and management of groundwater resources for this study area.

Scientists observe, discover, justify and eventually share their findings with the scientific community. Dissemination is an integral aspect of scientific discovery since discoveries which go unnoticed have no or little impact on science. Today, peer-review is part of this process of scientific dissemination as it contributes proactively to the quality of a scientific article. As the numbers of scientific journals and scientific articles published therein are increasing steady, processes such as the single-blind or double-blind peer review are facing a near collapse situation. In fact, these traditional forms of reviewing have reached their limits and, because of this, are also increasingly considered as unfair, sloppy, superficial and even biased. In this manuscript we propose forms of Post Publication Public Peer Review (P4R) as valuable alternatives to the traditional blind peer review system. We describe how the journal Sci has explored such an approach and provide first empirical evidence of the benefits and also challenges such a P4R approach is facing.

This paper aims at presenting an energy management strategy (EMS) based upon optimal control theory for a battery-supercapacitor hybrid power system. The hybrid power system consists of a Lithium-ion battery and a supercapacitor with associated bidirectional DC/DC converters. The proposed EMS aims at computing adaptive gains using salp swarm algorithm and load following control technique to assign the power reference for both the supercapacitor and the battery while achieving optimal performance and stable voltage. The DC-DC converter model is derived utilizing the first-principles method and compute the required gains to achieve the desired power. The fact that the developed algorithm takes disturbances into account increases the power ele-ments’ life expectancies and supplies the power system with the required power

An exploding demand for processing capabilities related to the emergence of the IoT, AI and big data, has led to the quest for increasingly efficient ways to expeditiously process the rapidly increasing amount of data. These ways include different approaches like improved devices capable of going further in the more Moore path, but also new devices and architectures capable of going beyond Moore and getting more than Moore. Among the solutions being proposed, Stochastic Computing has positioned itself as a very reasonable alternative for low-power, low-area, low-speed, and adjustable precision calculations; four key-points beneficial to edge computing. On the other hand, chaotic circuits and systems appear to be an attractive solution for (low-power, green) secure data transmission in the frame of edge computing and IoT in general. Classical implementations of this class of circuits require intensive and precise calculations. This paper discusses the use of the SC framework for the implementation of nonlinear systems, showing that it can provide results comparable to those of classical integration, with much simpler hardware, paving the way for relevant applications.

Internet of things (IoT) is a network of smart things. This indicates the ability of these physical things to transfer information with other physical things. The characteristics of these networks, such as topology dynamicity and energy constraint, challenges the routing problem in these networks. Previous routing methods could not achieve the required performance in this type of network. Therefore, developers of this network designed and developed specific methods in order to satisfy the requirements of these networks. One of the routing methods is utilization of multi-path protocols which send data to its destination using routes with separate links. One of such protocols is AOMDV routing protocol. In this paper, this method is improved using gray system theory which chooses the best paths used for separate routes to send packets. To do this, AOMDV packet format is altered and some fields are added to it so that energy criteria, link expiration time, and signal to noise ratio can also be considered while selecting the best route. The proposed method named RMPGST-IoT is introduced which chooses the routes with highest rank for concurrent transmission of data, using a specific routine based on the gray system theory. In order to evaluate and report the results, the proposed RMPGST-IoT method is compared to the ERGID and ADRM-IoT approaches with regard to throughput, packet receiving rate, packet loss rate, average remaining energy, and network lifetime. The results demonstrate the superior performance of the proposed RMPGST-IoT compared to the ERGID and ADRM-IoT approaches.

Acceptance of SNAP benefits has increased in farmers’ markets (FM) over the years. However, getting SNAP recipients to take up the opportunity of extra dollars for healthy food options remains a challenge. Thus, we tracked the use of the Double-up Food Buck (DUFB) program among SNAP recipients who visit participating FMs in Utah to understand the unique characteristics of the program users. Our results identified varying types of program users (multiple and single market shoppers, more frequent and less frequent shoppers). While our findings indicate a higher DUFB transaction in token value among frequent users, larger markets are more likely to have higher DUFB transactions with a higher number of market visits compared to smaller ones. In conclusion, while all types of users may add to the total revenue generated by participating FM, additional initiatives are required to encourage frequent visits and uptake of the program for better access to healthy foods.

Staphylococcus aureus is a Gram-positive bacterium causing a range of mild to life-threatening infections including bone infections such as osteomyelitis. S. aureus is able to invade and persist within non-professional phagocytic cells such as osteoblasts. In the present study four different S. aureus strains, 2SA-ST239-III, 5SA-ST5-II, 10SA-ST228-I, and 14SA-ST22-IVh were tested for their ability to modulate cell viability in MG-63 osteoblast-like cells following a successful invasion and persistence. Methicillin-sensitive S. aureus (MSSA) ATCC-12598-ST30 was used as control. Despite the demonstrated similar abilities of internalization and persistence of ATCC-12598-ST30, 2SA-ST239-III, and 14SA-ST22-IVh strains in MG-63 osteoblast-like cells under our experimental conditions, we demonstrated that the decrease in cell viability was due to the different behavior of the considered strains, with the number of intracellular bacteria playing a limited role. We focused our attention on different cellular biochemical functions related to inflammation, cell metabolism, and oxidative stress during osteoblast infections. We were able to show that: 1) ATCC-12598-ST30 and 2SA-ST239-III were the only two clones able to persist and maintain their number into the cellular hostile environment during the entire period of infection; 2) 2SA-ST239-III was the only clone able to significantly increase the gene expression (3 and 24 h) and protein secretion (24 h) of both interleukin-6 (IL-6) and tumor necrosis factor alpha (TNF-α) in MG-63 osteoblast-like cells; 3) the same clone determined a significant up-regulation of transforming growth factor-β1 (TGF-β1) and the metabolic marker glyceraldehyde 3-phosphate dehydrogenase (GAPDH) mRNAs at 24 h post infection; 3) neither the MSSA nor the four MRSA strains induced oxidative stress phenomena in MG-63 cells, although a very different expression pattern towards nuclear factor E2-related factor 2 (Nrf2) and its downstream gene heme oxygenase 1 (HO-1) activation was observed among the different clones. Our results can open a new way of considering therapies, going in the direction of an individualized therapeutic strategy that should take into account the difference existing between MSSA and MRSA as well as the distinctive features of the different clones. Not only, therefore, a different antibiotic approach but also a starting point for considering different host factors, i.e. the modulation of specific cytokines such as IL-6, TNF-α, and TGF-β1.

The hydrodynamic behavior of the air-acetic acid system in a bubble column is studied using a differential pressure transmitter, double probe optical fiber probe, and the electrical resistance tomography (ERT) technique. The superficial gas velocity ranges from 0.016 to 0.094 m/s under ambient temperature and pressure. The influences of viscosity and surface tension on gas holdup, bubble rising velocity, and bubble chord distribution in the column are discussed with different mass fractions of an acetic acid solution. The results show that as the mass fraction of acetic acid increases, the surface tension of the liquid phase decreases, and the viscosity first increases and then decreases. This causes the gas holdup in the column to first increase and then decrease, and reaches the maximum value at an acetic acid mass fraction of 55% to 60%. The rising velocity of the bubbles in the column is high in the central region and has a low-value distribution near the wall. The bubble chord length distribution is concentrated, and the distribution of the bubble chord length in the column becomes narrow with any decrease in surface tension. Studying the hydrodynamic behavior of a bubble column with the air-acid system is of great significance considering the absence of data on air-organic acid systems.

Complex diseases such as cancer are caused by changes in the Gene Regulatory Networks. Systems that model the complex dynamics of these networks along with adapting to real gene expression data are closer to reality and can help understand the creation and treatment of cancer. In this paper, for the first time, modelling of gene regulatory networks is performed using delayed nonlinear variable order fractional systems in the state space by a new tool called GENAVOS. This tool uses gene expression time series data to identify and optimize system parameters. This software has several tools for analyzing system dynamics. The results show that the nonlinear variable order fractional systems have very good flexibility in adapting to real data. We found that regulatory networks in cancer cells actually have a larger delay parameter than in normal cells. It is also possible to create chaos, periodic and quasi-periodic oscillations by changing the delay, degradation and synthesis rates. Our findings indicate a profound effect of time-varying order on these networks, which may be related to a type of cellular memory due to epigenetic and environmental factors. We showed that by changing the delay parameter and the variable order function for a normal cell system, its behavior changes and becomes quite similar to the behavior of a cancer cell. This work also confirms the effective role of the miR-17-92 cluster in the cancer cell cycle. GENAVOS is available at https://github.com/hanif-y/GENAVOS with its user guide and MATLAB codes.

As the Autonomous Vehicle (AV) industry is rapidly advancing, classification of non-motorized (vulnerable) road users (VRUs) becomes essential to ensure their safety and to smooth operation of road applications. The typical practice of non-motorized road users’ classification usually takes numerous training time and ignores the temporal evolution and behavior of the signal. In this research effort, we attempt to detect VRUs with high accuracy be proposing a novel framework that includes using Deep Transfer Learning, which saves training time and cost, to classify images constructed from Recurrence Quantification Analysis (RQA) that reflect the temporal dynamics and behavior of the signal. Recurrence Plots (RPs) were constructed from low-power smartphone sensors without using GPS data. The resulted RPs were used as inputs for different pre-trained Convolutional Neural Network (CNN) classifiers including constructing 227×227 images to be used for AlexNet and SqueezeNet; and constructing 224×224 images to be used for VGG16 and VGG19. Results show that the classification accuracy of Convolutional Neural Network Transfer Learning (CNN-TL) reaches 98.70%, 98.62%, 98.71%, and 98.71% for AlexNet, SqueezeNet, VGG16, and VGG19, respectively. The results of the proposed framework outperform other results in the literature (to the best of our knowledge) and show that using CNN-TL is promising for VRUs classification. Because of its relative straightforwardness, ability to be generalized and transferred, and potential high accuracy, we anticipate that this framework might be able to solve various problems related to signal classification.

Reconfigurable computing is becoming ubiquitous in the form of consumer-based Internet of Things (IoT) devices. Reconfigurable computing architectures have found their place in safety-critical infrastructures such as the automotive industry. As the target architecture evolves, it also needs to be updated remotely on the target platform. This process is susceptible to remote hijacking, where the attacker can maliciously update the reconfigurable hardware target with tainted hardware configuration. This paper proposes an architecture of establishing Root of Trust at the hardware level using cryptographic co-processors and Trusted Platform Modules (TPMs) and enable over the air updates. The proposed framework implements secure boot protocol on Xilinx based FPGAs. The project demonstrates the configuration of the bitstream, boot process integration with TPM and secure over-the-air updates for the hardware reconfiguration.

Land suitability analysis is a basic premise for allocating specific land for specific purpose. The objective of this study was to predict the suitable sites for cultivating Moringa oleifera tree in Ethiopia using Spatial Analytic Hierarchy Process. Findings of this study will have paramount significance in supporting decision making in the agroforestry development sector. This study employs Spatial Analytic Hierarchy Process and Geographic Information System to generate valuable information in land allocation for moringa oleifera tree production. Climate, topography, soil type and land use parameters were evaluated for suitability analysis. The results of the study revealed that most of the central part of the country are categorized as moderately suitable for the production of moringa oleifera tree. Areas classified as highly suitable are distributed along the borders of southern and western part of the country. However, some of the central part was classified as not suitable for Moringa oleifera tree production. This paper tried to investigate analysis of spatial data to predict suitable site for moringa tree production at national level. At national level, highly suitable, moderately suitable, and not suitable class covers an area of 308,508.2, 1,628,930.8 and 59891.3 Square Kilometer respectively.

This paper shares some new information on the ambient temperature profile and the heat stress occurrences directly underneath ground-mounted Solar Photovoltaic (PV) Arrays (monocrystalline-based) focusing on different temperature levels. A common ground for this work lies on the fact that 10C increase of PV cell temperature results in reduction of 0.5% energy conversion efficiency thus any means of natural cooling mechanism would gain much benefit especially to the Solar Farm operators. Transpiration process plays an important role in the cooling of green plants where in average it could dissipate around 32.9% of the total solar energy absorbed by the leaf making it a good natural cooling mechanism. This condition is relatively applied for herbs specifically for this project, Orthosiphon Stamineus or generally known as Java Tea are used as the high value crops. The thermal process via convective heat and mass exchange of leaves with the environment is relevant for a better understanding of plant physiological processes in response to environmental conversion factors for a wide range of applications. An important fact for plant heat stress with respect to the Ambient temperature is that the range lies between 10 C to 15 C above the surrounding value. This heat stress condition is relatively important and should be modelled in crops-energy integration. Agrivoltaic concept is a system that combines commercial agriculture and photovoltaic electricity generation in the same space. The concept is in line with the Kyoto Protocol and the United Nation Sustainable Development Goals (UN-SDG) which highlights the clean energy and sustainable urban living. The integration of agrivoltaic systems would optimize the yield, improving clean system efficiency and solving the issue of land resource sustainability. The PV bottom surface temperature are the main source of dissipated heat as shown in the thermal images recorded at 5 minutes interval at 3 sampling time. Statistical analysis shows that the Thermal correlations for transpiration process and heat stress occurrences between PV bottom surface and plant height will be an important finding for large scale plant cultivation in agrivoltaic farms.

Thanks to rapid advances in the Internet of Things (IoT) and Edge-Fog-Cloud Computing technologies, Smart Public Safety (SPS) system has become feasible by integrating heterogeneous computing devices and different types of networks to collaboratively provide seamless public safety services. While SPS facilitates convenient exchanges of surveillance data streams among device owners and third-party applications, the existing monolithic service oriented architecture (SOA) architecture is unable to provide scalable and extensible services in a large-scale heterogeneous network environment. Moreover, traditional security solutions rely on centralized trusted third-party authority, which not only can be a performance bottleneck or the single point of failure, but it also incurs privacy concerns on improperly use of private information. Inspired by blockchain and microservices technologies, this paper proposed a BLockchain-ENabled Decentralized Smart Public Safety (BlendSPS) system. Leveraging hybrid blockchain fabrics, a microservices based security mechanism is implemented to enable decentralized security architecture, and it supports immutability, auditability and traceability for secured data sharing and operations among participants of the SPS system. An extensive experimental study verified the feasibility of the proposed Blend-SPS that possesses security and privacy proprieties with limited overhead on IoT based edge networks.

. In today’s world, cyber attack is one of the major issues concerning the organizations that deal with technologies like cloud computing, big data, IoT etc. In the area of cyber security, intrusion detection system (IDS) plays a crucial role to identify suspicious activities in the network traffic. Over the past few years, a lot of research has been done in this area but in the current scenario, network attacks are diversifying in both volume and variety. In this regard, this research article proposes a novel IDS where a combination of information gain and decision tree algorithm has been used for the purpose of dimension reduction and classification. For experimental purpose the NSL-KDD dataset has been used. Initially out of 41 features present in the dataset only 5 high information gain valued features are selected for classification purpose. The applicability of the selected features are evaluated through various machine learning based algorithms. The experimental result shows that the decision tree based algorithm records highest recognition accuracy among all the classifiers. Based on the initial classification result a novel methodology based on decision tree has been further developed which is capable of identifying multiple attacks by analyzing the packets of various transactions in real time.

In this study, a motion control problem for the vessels towed by tugboats or towing ships on the sea is considered. The towed vessels including barge ships are need to have assistance of tugboats. Combining two vessels, some work purposes in the sea or harbor area can be completed. In this study, the authors give newly developed mathematical model and control system strategy. Especially, the system model fully presenting the physical characteristics of two vessels are derived. For controlling the system effectively, it is considered that the towed vessel has no power propulsion system but the rudder is activated to improve the maneuverability. Considering the strong nonlinearities included in the vessel dynamics, the modelled system is presented by nonlinear system without linearization of nonlinear parameters. Thus, the control system for the towed vessel is designed based on the nonlinear control scheme. Exactly, the back-stepping control method is applied to its motion control. Also, the PID control method is applied for comparing with the proposed control strategy.

COVID-19 is now a major global health crisis, can lead to severe food crisis unless proper measures are not taken. Though a number of scientific studies have addressed the possible impacts of COVID-19 in Bangladesh on variety of issues, problems and food crises associated with aquatic resources and communities are missing. Therefore, this study aimed at bridging the gap in the existing situation and challenges of COVID-19 by linking its impact on aquatic food sector and small-scale fisheries with dependent population. The study was conducted based on secondary data analysis and primary fieldwork. Secondary data focused on COVID-19 overview and number of confirmed, recovered and death cases in Bangladesh; at the same time its connection with small-scale fisheries, aquatic food production, demand and supply was analyzed. Community perceptions were elicited to present how the changes felt and how they affected aquatic food system and small-scale fisheries and found devastating impact. Sudden illness, reduced income, complication to start production and input collection, labor crisis, transportation abstraction, complexity in food supply, weak value chain, low consumer demand, rising commodity prices, creditor’s pressure were identified as the primary affecting drivers. Dependent people felt the measures taken by the Government should be based on protecting the health and food security, although it could be detrimental to economic growth in the short term. The study provides insight into policies adopted by the policy makers to mitigate the effects of the pandemic on aquatic food sector and small-scale fisheries.

The gastrointestinal (GI) tract is innervated by the enteric nervous system (ENS), an extensive neuronal network that traverses along its walls. Due to local reflex circuits, the ENS is capable of functioning with and without input from the central nervous system. The functions of the ENS range from the propulsion of food to nutrient handling, blood flow regulation and immunological defense. Records of it first being studied emerged in the early 19th century when the submucosal and myenteric plexuses were discovered. This was followed by extensive research and further delineation of its development, anatomy, and function during the next two centuries. The morbidity and mortality associated with the underdevelopment, infection or inflammation of the ENS highlights its importance and the need for us to completely understand its normal function. This review will provide a general overview of the ENS to date and connect specific GI disorders such as short bowel syndrome with neuronal pathophysiology. Exciting opportunities in which the ENS could be used as a therapeutic target for common GI diseases will also be highlighted, as the further unlocking of such mechanisms could open the door to more therapy-related advances, and ultimately change our approach to GI disorders.

While SARS-CoV-2 uses angiotensin converting enzyme 2 (ACE2) as the receptor for cell entry, it is important to examine for other potential interactions between the virus and other cell receptors. Based on the clinical observation of low smoking prevalence among hospitalized COVID-19 patients, we recently identified a “toxin-like” amino acid (aa) sequence on the receptor binding domain of the spike glycoprotein of SARS-CoV-2 (aa 375-390) with homology to a sequence of a snake venom toxin, which could interact with nicotinic acetylcholine receptors (nAChRs). We now present computational molecular modelling and docking experiments using 3D structures of the SARS-CoV-2 spike glycoprotein and the extracellular domain of the nAChR alpha9 subunit. We identified an interaction between the aa381-386 of the SARS-CoV-2 spike glycoprotein and the aa189-192 of the extracellular domain of the nAChR alpha9 subunit, a region which forms the core of the “toxin-binding site” of the nAChRs. The mode of interaction is very similar to the interaction between the alpha9 nAChR and alpha-bungarotoxin. A similar interaction was observed between the pentameric alpha7 AChR and the SARS-CoV-2 spike glycoprotein. Our findings support the hypothesis that severe COVID-19 may be associated with disruption of the nicotinic cholinergic system which could be caused by an interaction between SARS-CoV-2 and nAChRs.

The time series of daily new cases of Covid-19 infection in different zones of Italy, several European countries and New York City were analyzed in order to characterize the large fluctuations contained in them. The comparison shows that the time series share a similar evolution, although with delays of one or two weeks, with Italy that precedes the other countries. Correlation analysis indicates a strict resemblance between the time series of the first differences, which emphasize accelerations and slowdowns of the epidemic (e.g., correlation coefficient r=0.70 for Italian vs. worldwide data). According to Fourier analysis the signals have a dominant oscillatory component at a period of seven days, with minima located near the weekends. Some of the possible causes of this time modulation are discussed. In particular, it is hypothesized a delayed or missed recognition of a significant fraction of the new cases during the weekend, with possible consequences for the evolution of the epidemic.

Metastases cause high mortality in several cancers and immunotherapies are expected to be effective in the prevention and treatment of metastatic disease. However, only a minority of patients benefit from immunotherapies. This creates a need for novel therapies that are efficacious regardless of the cancer types and metastatic environments they are growing in. Preclinical immuno-oncology models for studying metastases have long been limited to syngeneic or carcinogenesis-inducible models that have murine cancer and immune cells. However, the translational power of these models has been questioned. Interactions between tumor and immune cells are often species-specific and regulated by different cytokines in mice and humans. For increased translational power, mice engrafted with functional parts of human immune system have been developed. These humanized mice are utilized to advance understanding the role of immune cells in the metastatic process, but increasingly also to study the efficacy and safety of novel immunotherapies. From these aspects, this review will discuss the role of immune cells in the metastatic process and the utility of humanized mouse models in immuno-oncology research for metastatic cancers, covering several models from the perspective of efficacy and safety of immunotherapies.

Managing epidemics need to unite each individual of the nations. Lockdown is an essential strategy to fatal and threatening epidemic. All of the citizens should realize that each of people has a responsibility to support the public health crisis. How to organize individual to fight against the epidemic plaque depends each of them. This essay discussed the COVID-19 pandemic relevant self-governance of community from a historical perspective in China. Self-governance of neighborhood committee and community residential office in urban and rural helped to control the COVID-19 pandemic in the communities in China. Co-operation and collective responsibility of citizens and community support is a critical condition to prevent epidemic. The community self-governance can track back Qin Dynasty in China history. It established baojia system to maintain social control for thousand years. Now, the community-based system, baojia system is becoming more autonomous for local citizenship and socialized governance in China. We reviewed a historical pneumonia plaque in North China in 1911. Chinese doctor, Wu Lien-teh (伍連德) confronted the epidemic crisis with many measures such as lockdown, quarantine, the wearing of mask, setup mobile hospitals, travel restriction, the cremation of victims, and border control. Dr. Wu made effort to establish the modern public health service in China. We think that the combination of culture background of China community with modern public healthcare system determinedly played important role to control the COVID-19 pandemic.

The hybrid bond graph has been studied in depth for scalar bond graphs, but how does this translate to the multi-bond graph? Here, the controlled junction – used to model structural switching such as contact – is extended to the multi-bond case. This is a simple process, assuming that all bonds switch simultaneously (which makes physical sense). A controlled 0-junction is applied to multi-bond graph of a car, which can lose contact with the ground in cornering. Dynamic causality features, but this can be accommodated using an equational submodel in 20-Sim (in a manner similar to that used with scalar bond graphs). The junction is proposed for subsequent work to develop a validated multi-body dynamics car model in cornering.

This study suggests a novel cooperative multi-simplex algorithm that generalizes a local search optimizer to design a novel global search heuristic algorithm. The proposed algorithm exploits the vertex sharing strategy to enhance the search abilities of the working simplexes. The vertex sharing among the simplexes is carried out through cooperative step that is based on fitness of the underlying simplex. The proposed algorithm is applied to solve some systems of nonlinear equations by transforming them to optimization problems. Comparative analysis of results shows that the proposed method is practical and effective.

The hybridization of machine learning methods with soft computing techniques is an essential approach to improve the performance of the prediction models. Hybrid machine learning models, particularly, have gained popularity in the advancement of the high-performance control systems. Higher accuracy and better performance for prediction models of exergy destruction and energy consumption used in the control circuit of heating, ventilation, and air conditioning (HVAC) systems can be highly economical in the industrial scale to save energy. This research proposes two hybrid models of adaptive neuro-fuzzy inference system-particle swarm optimization (ANFIS-PSO), and adaptive neuro-fuzzy inference system-genetic algorithm (ANFIS-GA) for HVAC. The results are further compared with the single ANFIS model. The ANFIS-PSO model with the RMSE of 0.0065, MAE of 0.0028, and R² equal to 0.9999, with a minimum deviation of 0.0691 (KJ/s), outperforms the ANFIS-GA and single ANFIS models.

This study aimed to evaluate the hazards posed by foodborne bacteria of the Listeria genus by analyzing prevalence, diversity and virulence of Listeria spp. in food and food manufacturing plants. Seventy five isolates obtained from the routine analysis of 653 samples by three diagnostic laboratories in Northern Italy were genotipically differentiated by Repetitive Extragenic Palindrome (rep) PCR with the GTG5 primer, identified by sequencing the 16S rRNA gene and examined by specific PCR tests for the presence of L. monocytogenes virulence determinants occasionally found to occur in other species of the genus. The identity of the amplification products was confirmed by sequencing. Fifty seven isolates were identified as L. innocua, 12 as L. monocytogenes, 5 as L. welshimeri and one as L. seeligeri. All L. monocytogenes isolates belonged to the serotype 1/2a and were predicted to be virulent for the presence of the inlJ internalin gene. Potentially virulent strains of L. innocua, L. seeligeri and L. welshimeri, carrying the L. monocytogenes inlA gene and/or hly gene, were identified, and most isolates were found to possess the toxin-antitoxin system mazEF for efficient adaptation to heat shock. Results indicated the need to reinforce food contamination prevention measures against all Listeria species by efficiently defining their environmental distribution.

This research aims to determine the similarities in groups of people to build a film recommender system for users. Users often have difficulty in finding suitable movies due to the increasing amount of movie information. The recommender system is very useful for helping customers choose a preferred movie with the existing features. In this study, the recommender system development is established by using several algorithms to obtain groupings, such as the K-Means algorithm, birch algorithm, mini-batch K-Means algorithm, mean-shift algorithm, affinity propagation algorithm, agglomerative clustering algorithm, and spectral clustering algorithm. We propose methods optimizing K so that each cluster may not significantly increase variance. We are limited to using groupings based on Genre and, Tags for movies. This research can discover better methods for evaluating clustering algorithms. To verify the quality of the recommender system, we adopted the mean square error (MSE), such as the Dunn Matrix and Cluster Validity Indices, and social network analysis (SNA), such as Degree Centrality, Closeness Centrality, and Betweenness Centrality. We also used Average Similarity, Computational Time, Association Rule with Apriori algorithm, and Clustering Performance Evaluation as evaluation measures to compare method performance of recommender systems using Silhouette Coefficient, Calinski-Harabaz Index, and Davies-Bouldin Index.

Over the past few years, we have experienced great technological advancements in the information and communication field, which has significantly contributed to reshaping the Intelligent Transportation System (ITS) concept. Evolving from the platform of a collection of sensors aiming to collect data, the data exchanged paradigm among vehicles is shifted from the local network to the cloud. With the introduction of cloud and edge computing along with ubiquitous 5G mobile network, it is expected to see the role of Artificial Intelligence (AI) in data processing and smart decision imminent. So as to fully understand the future automobile scenario in this verge of industrial revolution 4.0, it is necessary first of all to get a clear understanding of the cutting-edge technologies that going to take place in the automotive ecosystem so that the cyber-physical impact on transportation system can be measured. CIoV, which is abbreviated from Cognitive Internet of Vehicle, is one of the recently proposed architectures of the technological evolution in transportation, and it has amassed great attention. It introduces cloud-based artificial intelligence and machine learning into transportation system. What are the future expectations of CIoV? To fully contemplate this architecture’s future potentials, and milestones set to achieve, it is crucial to understand all the technologies that leaned into it. Also, the security issues to meet the security requirements of its practical implementation. Aiming to that, this paper presents the evolution of CIoV along with the layer abstractions to outline the distinctive functional parts of the proposed architecture. It also gives an investigation of the prime security and privacy issues associated with technological evolution to take measures.

Climacteric fruits are characterized by a dramatic increase in autocatalytic ethylene production, which is accompanied by a spike in respiration, at the onset of ripening. The change in the mode of ethylene production from autoinhibitory to auto-stimulatory is known as the system 1 (S1) to system 2 (S2) transition. Existing physiological models explain the basic and overarching genetic, hormonal, and transcriptional regulatory mechanisms governing the S1 to S2 transition of climacteric fruit. However, the links between ethylene and respiration, the two main factors that characterize the respiratory climacteric, have been largely understudied at the molecular level. Results of recent studies indicate that the AOX respiratory pathway may play an important role in mediating cross talk between ethylene response, carbon metabolism, ATP production, and ROS signaling during climacteric ripening. New genomic, metabolic, and epigenetic information sheds light on the interconnectedness of ripening-associated metabolic pathways, necessitating expanding the current, ethylene-centric physiological models. Understanding points at which ripening responses can be manipulated may reveal key, speciesand cultivar-specific targets for regulation of ripening enabling superior strategies for reducing postharvest wastage.

Concept Knowledge (C-K) theory has been used in engineering and science-based research for more than a decade. Design of an Information Technology (IT) artefact is mostly pragmatic in nature. Design Science Research (DSR) methodology applied and studied in many Information Systems (IS) research. Many sub design decisions involved through the design of an IT artefact from a concept (idea) to a working prototype. A DSR artefact is based on a combination of decisions made during several sub-design stages. Artefacts are built based on the selection of elements in each sub-design space. Recording the design decisions on each sub-design space would be beneficial for future researchers. By knowing the design decisions on each sub-design space, researchers would be able to try different combinations of the design. C-K theory provides the ability to capture the design processes’ several sub-design spaces. In this paper, we discuss the DSR research methodology by looking at the stages proposed in the literature, and the application of C-K theory in an IT-based DSR. This paper also proposed a C-K theory-based protocol called Concept Tree for tracking and reporting artefact design steps. The application of C-K theory in DSR is exhibited using the implementation of the Concept Tree for a prototype design IT artefact.

Visualization is an essential tool for analysis of data and communication of findings in the sciences, and the Earth System Science (ESS) are no exception. However, within ESS specialized visualization requirements and data models --- particularly for those data arising from numerical models --- often make general-purpose visualization packages difficult, if not impossible, to effectively use. This paper presents VAPOR: a domain-specific visualization package that targets the specialized needs of ESS modelers, particularly those working in research settings where highly interactive exploratory visualization is beneficial. We specifically describe VAPOR’s ability to handle ESS simulation data from a wide variety of numerical models, as well as a multi-resolution representation that enables interactive visualization on very large data while using only commodity computing resources. We also describe VAPOR’s visualization capabilities, paying particular attention to features for geo-referenced data and advanced rendering algorithms suitable for time-varying, 3D data. Finally, we illustrate VAPOR's utility in the study of a numerically simulated tornado. Our results demonstrate both ease-of-use and the rich capabilities of VAPOR in such a use case.

Objective: This study investigated the effect of the hierarchical medical system under the national health insurance program on resident’s healthcare-seeking behavior in Taiwan. Background: Healthcare authorities in Taiwan initiated the allowance reduction of outpatient visits at regional hospitals and higher hierarchical hospitals from 2018. The ultimate goal is to implement a hierarchical medical system and provide the residents accessible as well as consistent medical services. Methods: This research was conducted through the questionnaire survey method and data were collected between August and December 2018 from the records of subjects who had recently sought medical attention. A total of 1,340 valid questionnaires were returned. Results: Regarding the effect on healthcare-seeking behavior, the following factors were significant: being aged between 40 to 49 (p＜.1), subjects with an educational background of junior high school (p＜.05), those who were not aware of the policy (p＜.001), and an awareness about both the hierarchical medical system and the policy to reduce outpatient visits to large hospitals (p＜.001). Conclusion: The public should be made aware about the hierarchical medical system to improve healthcare.

Cloud-based CNC is an emerging paradigm of Industry 4.0 where computer numerical control (CNC) functionalities are moved to the cloud and provided to manufacturing machines as a service. Among many benefits, C-CNC allows manufacturing machines to leverage advanced control algorithms running on cloud computers to boost their performance at low cost, without need for major hardware upgrades. However, a fundamental challenge of C-CNC is how to guarantee safety and reliability of machine control given variable Internet quality of service, especially on public Internet networks. We propose a three-tier redundant architecture to address this challenge. We then prototype tier one of the architecture on a 3D printer successfully controlled via C-CNC over public Internet connections, and discuss follow-on research opportunities.

Cyanobacterial blooms occur with increasing frequency in freshwater ecosystems, posing a hazard to human and environmental health. Exposure of human to cyanobacterial metabolites occurs mostly via accidental ingestion through contaminated drinking water or during recreational activities and, most frequently, results in gastrointestinal symptoms. Despite the clinical manifestation, cyanobacterial metabolites are rather investigated for their toxicity towards specific organs or tissues, especially hepato-, nephro- and neurotoxicity, then for effects on the gastrointestinal tract and the associated lymphoid tissue. The aim of this review was to systematically summarize available literature on the effects on the gastrointestinal tract and the mucosal innate immune system and compile the data from both, in vitro and in vivo studies, focusing on human-health relevant models. Our systematic literature review revealed significant data gaps in the understanding on metabolites breaching the gastrointestinal barrier and the role of the immune system in the establishment of clinical symptoms. Microcystins and cylindrospermopsin were linked to gastrointestinal symptoms, immune system effects or both. Furthermore, implications for cyanobacterial bloom lipopolysaccharides in gastrointestinal inflammation were reported in several cases, while other metabolites received only minor attention. The collected data indicate the need for a reassessment of potential enterotoxicity of microcystins and cylindrospermopsin. Additionally, the carcinogenic potential of cyanotoxins, especially microcystins, has to be clarified, as an increasing amount of epidemiological studies show correlations between cyanobacterial blooms and gastrointestinal cancer incidence. Furthermore, other, often highly abundant bioactive metabolites like aeruginosins, have to be toxicologically evaluated at levels also accounting for (sub-)chronic exposure to low concentrations and in combination with naturally co-occurring metabolites, as can be expected in drinking water supplies. studies, focusing on human-health relevant models. Our systematic literature review revealed significant data gaps in the understanding on metabolites breaching the gastrointestinal barrier and the role of the immune system in the establishment of clinical symptoms. Microcystins and cylindrospermopsin were linked to gastrointestinal symptoms, immune system effects or both. Furthermore, implications for cyanobacterial bloom lipopolysaccharides in gastrointestinal inflammation were reported in several cases, while other metabolites received only minor attention. The collected data indicate the need for a reassessment of potential enterotoxicity of microcystins and cylindrospermopsin. Additionally, the carcinogenic potential of cyanotoxins, especially microcystins, has to be clarified, as an increasing amount of epidemiological studies show correlations between cyanobacterial blooms and gastrointestinal cancer incidence. Furthermore, other, often highly abundant bioactive metabolites like aeruginosins, have to be toxicologically evaluated at levels also accounting for (sub-)chronic exposure to low concentrations and in combination with naturally co-occurring metabolites, as can be expected in drinking water supplies.

The management and proper use of the Urban Public Transport Systems (UPTS) constitutes a critical field that has not been investigated in accordance to its relevance and urgent idiosyncrasy within smart cities realm. Swarm Intelligence is a very promising paradigm to deal with such complex and dynamic systems. It presents robust, scalable and self-organized behaviour to deal with dynamic and fast changing systems. The intelligence of cities can be modelled as a swarm of digital telecommunication networks (the nerves), ubiquitously embedded intelligence, sensors and tags, and software. In this paper, a new approach based on the use of the Natural Computing paradigm and Collective Computation is shown, more concretely taking advantage of an Ant Colony Optimization algorithm variation and Fireworks algorithms in order to build a system that makes the complete control of the UPTS a tangible reality.

This paper presents the robotic platform, PiBot, that has been developed and that is aimed at improving the teaching of Robotics with vision to secondary students. Its computational core is the Raspberry Pi 3 controller board, and the greatest novelty of this prototype is the support developed for the powerful camera mounted on board, the PiCamera. An open software infrastructure written in Python language was implemented so that the student may use this camera, or even a WebCam, as the main sensor of this robotic platform. Also, higher level commands have been provided to enhance the learning outcome for beginners. In addition, a PiBot 3D printable model and the counterpart for the Gazebo simulator were also developed and fully supported. They are publicly available so that students and educational centers that do not have the physical robot or can not afford the costs of these, can nevertheless practice and learn or teach Robotics using these open platforms: DIY-PiBot and/or simulated-PiBot.

Biochar application to soil has been proposed as a means for reducing soil greenhouse gas emissions and mitigating climate change. The effects, however, of interactions between biochar, moisture and temperature on soil CO₂ and N₂O emissions, remain poorly understood. Furthermore, the applicability of lab-scale observations to field conditions in diverse agroecosystems remains uncertain. Here we investigate the impact of a mixed wood gasification biochar on CO₂ and N₂O emissions from loess-derived soils using: (1) controlled laboratory incubations at three moisture (27, 31 and 35%) and three temperature (10, 20 and 30°C) levels, and (2) a field study with four cropping systems (continuous corn, switchgrass, low diversity grass mix, and high diversity grass-forb mix). Biochar reduced N₂O emissions under specific temperatures and moistures in the laboratory and in the continuous corn cropping system in the field. However, the effect of biochar on N₂O emissions was only significant in the field, and no effect on cumulative CO₂ emissions was observed. Cropping system also had a significant effect in the field study, with soils in grass and grass-forb cropping systems emitting more CO₂ and less N₂O than corn cropping systems. Observed biochar effects were consistent with previous studies showing that biochar amendments can reduce soil N₂O emissions under specific, but not all, conditions. The disparity in N₂O emission responses at the lab and field scales suggests that laboratory incubation experiments are not reliable for predicting the impact of biochar at the field scale.

Technology advancements are evident in the healthcare field; numerous new opportunities and applications have emerged during the last years based on embedded intelligence and related to real-time diagnosis of medical issues, tele-care and telemedicine, remote monitoring of patients, computer-assisted “smart” transportation in case of emergencies, as well as new types of remotely controlled surgical operations. This paper aims to provide an overview of the application of embedded intelligence in the field of healthcare; it gathers the main critical requirements, related technologies and research advancements up to date, and it presents the most important challenges that are yet to be faced. The main focus is given in the development and optimization of Body Area Networks (BANs) based on advanced embedded sensing devices, the optimization of smart gateways in such networks, and the provision of holistic scalable and secure solutions in the healthcare domain. In addition, the paper presents two principal use cases that stem from the combination of novel information and communication technologies with classic healthcare practices, explaining the functional and non-functional requirements, as well as the mixed criticality characteristics of the associated systems.

Here we present an overview of speleothem δ18O records from South America, which mostly are available in the Speleothem Isotopes Synthesis and Analysis (SISAL_v1) database. South American tropical and subtropical δ18O time series are primarily interpreted as being driven by the amount effect and, consequently show the past history of the convection intensity of convergence zones such as the Intertropical Convergence Zone and the South America Monsoon System. We investigate past hydroclimate scenarios in South America related to the South American Monsoon System in three different time scales: Late Pleistocene, Holocene and the last two millennia. The precession driven insolation is the main driver of convective variability over the continent during the last 250 kyrs, including the Holocene period. However a dipole is observed between the west and east portions of the continent. Records located in the central region of Brazil appear to be weakly affected by insolation driven variability and more susceptible to the South Atlantic Convergence Zone. Cold episodic events in Northern Hemisphere increase the activity of the South American Monsoon System on all time scales, in turn increasing rainfall amounts in South America, as was documented during Heinrich events in the late Pleistocene and Bond events in the Holocene, as well as during the Little Ice Age.

Irrigation scheduling is used by growers to determine the right amount and timing of water application. In most parts of Mozambique, 90% of the total yearly precipitation occurs from November to March. The El Niño Southern Oscillation (ENSO) phenomenon influences the climate in Mozambique and affects the water demand for crop production. The objectives of this work were to quantify the effects of ENSO phenomenon on tomato crop water requirements, and to create the AgroClimate irrigation tool (http://mz.agroclimate.org/) to assist farmers in improving irrigation management. This study was based on daily grid-based climate information from 1983 to 2016 from the Climate Forecast System Reanalysis. Daily crop evapotranspiration was calculated by Hargreaves equation and crop coefficients. This tool is available online and considers different planting dates, ENSO phases, and crop growing season lengths. Irrigation needs varied from less than 250 mm per growing cycle during winter to 550 mm during spring. Both El Niño and La Niña influenced the irrigation scheduling, especially from November to March. El Niño periods were related with increased water demand due to drier and warmer conditions while the opposite was observed for La Niña. The ENSO information might be used to understand climate variability and improve tomato irrigation scheduling in Mozambique.

In contrast to physical sustainable measures carried out in homes, such as insulation, the installation of a Home Energy Management System (HEMS) has no direct and immediate energy-saving effect. A HEMS gives insight into resident behaviour regarding energy use. When this is linked to the appropriate feedback, the resident is in a position to change his or her behaviour. This should result in reduced gas and/or electricity consumption. The aim of our study is to contribute towards the effective use of home energy management systems (HEMS) by identifying types of homeowners in relation to the use of HEMS. The research methods used were a literature review and the Q-method. A survey using the Q-method was conducted among 39 owners of single-family homes in various Rotterdam neighbourhoods. In order to find shared views among respondents, a principal component analysis (PCA) was performed. Five different types of homeowner could be distinguished: the optimists, the privacy-conscious, the technicians, the sceptics, and the indifferent. Their opinions vary as regards the added value of a HEMS, what characteristics a HEMS should have, how much confidence they have in the energy-saving effect of such systems, and their views on the privacy and safety of HEMS. The target group classification can be used as input for a way in which local stakeholders, e.g. a municipality, can offer HEMS that is in line with the wishes of the homeowner.

Public participation is significant for the success of any urban planning project. However, most members of the general public are not planning professionals and may not understand the technical details of a 2D paper-based plan, which might hamper their participation. One way to expand the participation of citizens is to present plans in well-designed, user-friendly and interactive platforms that allow participation regardless of the technical skills of the participants. This paper investigates the impacts of the combined use of 3D visualization and E-participation on public participation in Kisumu, Kenya. A 3D City model, created with CityEngine2016, was exported into a web-based geo-portal and used as a Planning Support System in two stakeholder workshops in order to evaluate its usability. For e-participation, 300 questionnaires given out to planning practitioners. Five indicators were developed for evaluating the usability of the 3D model while the usability of e-participation was evaluated using communication, collaboration and learning as indicators. Results showed that effectiveness and efficiency varies within different professional groups while the questionnaires showed strong preference for e-participation methods, especially SMSs/USSDs and emails. The study concludes that the use of 3D visualization and E-participation has the potential for improving the quality and quantity of public participation and recommends further research on the subject.

The European system of indicators is the result of the European Union sustainable development strategy, based on the objectives of economic prosperity, social equity, cohesion and environmental protection. In 2010 the EC defined four strategic priorities: encourage competitiveness in the tourism sector, promote sustainable high-quality tourism, strengthen the image of Europe as a tourism destination, and finally, improve EU financial policies and instruments. This paper aims to analyze in a positive critical perspective the experience done during the period 2013-2014 in the Italian destination Visit South Sardinia. This experience is in one hand an important primacy in Italy and in Europe, but in the other hand has not reached the expected objectives. Many difficulties were encountered both for data collection and for stakeholder involvement. Also the complexity of the indicators tool kit is relevant. Even if the experience of VSS did not produce the expected results, we believe that the analysis carried out in this paper can help and support other countries and or other destination in Europe in the process of implementation of the ETIS indicators tool kit in order to adapt the system itself to the needs that are encountered locally.

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

Advanced electrode designs have made single-unit neural recordings commonplace among modern neuroscience research. However, single-unit resolution remains out of reach for the intrinsic neurons of the gastrointestinal system. Single-unit recordings of the enteric (gut) nervous system have been conducted in anesthetized animal models and excised tissue, but there is a large physiological gap between awake and anesthetized animals, particularly for the enteric nervous system. Here, we describe the opportunity for advancing enteric neuroscience offered by single-unit recording capabilities in awake animals. We highlight the primary challenges to microelectrodes in the gastrointestinal system including structural, physiological, and signal quality challenges.

Water quality monitoring was an important objective in the surroundings. In this study, we investigated the sensing characteristics of the arrayed flexible chloride sensor with XBee wireless sensing system. The sensitivity and linearity of the wireless chloride sensing devices were 91.6 mV/pCl and 0.988, respectively. The hysteresis voltages were 50.14 mV and 36.71 mV during the cycles of 1 M → 10⁻¹ M → 1 M → 10⁻³ M → 1 M and 1 M → 10⁻³ M → 1 M → 10⁻¹ M → 1 M, respectively. The selectivity coefficients of the ClO⁻ ion, ClO₄⁻ ion, NO₃⁻ ion and I⁻ ion for Cl⁻ ion were 5.0 × 10⁻², 1.0 × 10⁻¹, 5.9 × 10⁻³ and 5.6×10⁻¹, respectively. The sensing characteristics of real time measurement were investigated for dynamic microfluidic. The arrayed flexible chloride sensor was integrated with the microfluidic device, syringe pump and wireless sensing system. The sensitivity and linearity were 273.1 mV/pCl and 0.978 at 35 μL/min, respectively.

Rice production is important for global food security but given its large water footprint, efficient irrigation management strategies need to be developed. Expansion of rice growing area is larger than any other crop in Africa due to increasing demand for rice. Three rice irrigation management alternatives with the system of rice intensification (SRI) were field-evaluated against the conventional continuously flooded system (CF) in Tanzania. Production systems included: (1) CF (50 mm ponding depth for the entire season); (2) SRI (40 mm ponding for 3 days and no irrigation for next 5 days); (3) 80% SRI (80% of the SRI ponding); and (4) 50% SRI (50% of the SRI ponding). Experimental evaluation of the four systems was conducted for both wet and dry seasons. For the dry season, the SRI and 80% SRI produced higher yields of 9.68 tons/ha and 11.45 tons/ha and saved 26% and 35% of water, respectively compared to the CF (8.69 tons/ha). The yield advantage of the 80% SRI and SRI over the CF was less during the wet season with 6.01 tons/ha and 5.99 tons/ha of production, and water savings of 30% and 14%, respectively compared to the CF (5.64 tons/ha). The 50% SRI had lowest yield of all for both seasons, 7.48 tons/ha and 4.99 tons/ha for the dry and wet seasons, respectively. Statistically, the 80% SRI treatment outperformed all other treatments over the two seasons with an additional yield of 1.57 tons/ha and 33% (345 mm) water savings compared to the CF. Economic productivity of water (US$/ha-cm) over two seasons was highest for the 80% SRI ($20.27/ha-cm), while it was lowest for the CF ($12.89/ha-cm). Water saved by converting from the CF to the 80% SRI (1.98 million ha-cm) can support a 50% expansion in the current rice irrigated area in Tanzania. Even without irrigation expansion, the 80% SRI can increase rice production by 1.5 million tons annually while enhancing water availability for industrial and environmental uses (e.g., ecological preserves) and help achieve food security in Tanzania and the greater sub-Saharan Africa.

We construct a real square system related to a given over-determined real system. We prove that the simple real zeros of the over-determined system are the simple real zeros of the related square system and the real zeros of the two systems are one-to-one correspondence with the constraint that the value of the sum of squares of the polynomials in the over-determined system at the real zeros is identically zero. After certifying the simple real zeros of the related square system with the interval methods, we assert that the certified zero is a local minimum of the sum of squares of the input polynomials. If the value of the sum of the squares of the input polynomials at the certified zero is equal to zero, it is a zero of the input system. As an application, we also consider the heuristic verification of the isolated zeros of polynomial systems and their multiplicity structures. Notice that a complex system with complex zeros can be transformed into a real system with real zeros.

Generator automatic voltage control device (AVC) is an automatic voltage and reactive power optimization control terminal on the power plant side. Aiming at improving the performance of generator automatic voltage control device, this paper introduces a detection method of AVC substation performance based on virtual dispatching terminal environment. During the testing, the upper computer of redundant power plant is connected to the virtual dispatching terminal environment to simulate the sending instructions of AVC master station, the behavior of AVC sub stations and the excitation system of generators. The performance of AVC substation is evaluated according to the feedback adjustment results, which can effectively detect the abnormal behavior of AVC substation in power plant.

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

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

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

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

The purpose of this paper is to describe a new way of producing renewable energy based on fjords as a water heater. We will call this system the Water-thermal Energy Production System (WEPS), because the basic idea is to extract heating and cooling energy from water. Although a prototype of WEPS has existed in Norway for more than ten years, a WEPS currently in operation has not been financially analyzed in the literature. Coastal parts of Norway have a potential of 5 TWh of profitable WEPS-facilities [9], due to convenient access to fjords containing water with stable all-season temperatures of about 4–12C when the depth of the water is about 50 meters. This stability of the water temperature makes it possible to extract energy from the fjord in a very efficient way. The potential for economically-profitable WEPS in other parts of the world has not been estimated. In order to answer such a question, more research is required. We have conducted a case study of a WEPS located in the Norwegian municipality of Eid. This is the first full-scale Norwegian WEPS, and it has been operating since 2006. The nascent years have passed, and the technology has been in operation for some years. In this paper, we have made an estimate of the business profitability and the external effects based on past empirical evidence and some assumptions about future development in some key figures. The results suggests that WEPS-Eid has been a profitable investment carrying a positive internal rate of revenue, even if the present underutilization in production capacity will continue in the future. Stability in energy prices for heating purposes has also gained customers compared to the more volatile prices of alternative renewable energy, like hydropower or wind turbines. The negative, external effects in the operating phase from WEPS-Eid are insignificant. Despite the significant profitability of the WEPS facility in Eid, there are two main obstacles for new entrants. There is a lack of relevant operational information for potential investors due to few facilities. This leads to uncertainty, and investments in WEPS appear as a risky business. Secondly, construction of a WEPS requires both big financial investments in digging and facilitating long trenches for a pipeline system and time and effort spent on acquiring the licenses needed for doing this work. A coordinating unit is probably required in order to get the necessary public and private licenses and to reduce fixed costs by coordinating other tasks in the same trenches, like pipes for water and sewer, fiber cables and tele-cables. In Eid, the local municipal administration was the coordinating unit.

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

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

Recently emerging data-driven citizen sciences need to harness increasing amount of massive data with varying quality. This paper develops essential theoretical frameworks and example models and examine its computational complexity for interactive data-driven citizen science within the context of guided self-organization. We first define a conceptual model that incorporates quality of observation in terms of accuracy and reproducibility, ranging between subjectivity, inter-subjectivity, and objectivity. Next, we examine the database's algebraic and topological structure in relation to informational complexity measures, and evaluate its computational complexities with respect to exhaustive optimization. Conjectures of criticality are obtained on self-organizing processes of observation and dynamical model development. Example analysis is demonstrated with the use of biodiversity assessment database, the process that inevitably involves human subjectivity for the management in open complex systems.

Passive Global Navigation Satellite System (GNSS)-based Synthetic Aperture Radar (SAR), known as GNSS-SAR, is a new passive radar imaging system. However, compared with conventional SAR, range resolution of GNSS-SAR is significantly lower. To improve range resolution of GNSS-SAR is an interested topic for investigation. In this paper, a novel range compression algorithm for enhancing range resolutions of GNSS-SAR is proposed. In the proposed scheme, at first, range compression is conducted by correlating the received reflected GNSS signal of intermediate frequency (IF) with the synchronized direct baseband GNSS signal in range domain. Then spectrum equalization is applied to the compressed results to suppress side lobes. Both theoretical analysis and simulation results have demonstrated that significant range resolution improvement in GNSS-SAR can be obtained by the proposed range compression algorithm, compared to the conventional range compression algorithm.

In the case of long-stator linear drive, unlike rotative drives for which speed or position sensors are a single unit attached to the shaft, these sensors extend along the guideway. The position signals transmitted from maglev vehicle can’t meet the need of the real-time propulsion control in the on-ground inverter power substations. In this paper the design of the propulsion inverter control system with a position estimator for driving long-stator synchronous motor in high speed maglev train is proposed. The experiments have been carried out at the 150m long guideway in O-song test track. To investigate the performance of the position estimator, the propulsion control system with and without the position estimator are compared. The result confirms that the proposed strategy can meet the dynamic property need of propulsion inverter control system for driving long-stator linear synchronous motors

There is a growing interest among researchers in the creation of innovative power supply systems that incorporate a range of generators, with a specific emphasis on renewable energy sources, especially wind energy. Battery-operated independent Microgrids powered by wind energy are increasingly recognized worldwide as a standalone power source for consumers. The power produced by the wind power conversion system depends on the wind speed, which can change quickly and without warning. It is essential to establish a robust energy management system accompanied by a responsive control approach to attain optimal energy balance and guarantee the delivery of high-quality power. In order to facilitate a rapid and efficient reaction to unexpected variations in the power supply system, the control strategy for the inverter and bidirectional DC to DC converter employs super twisting sliding mode controllers. The voltage at the DC link is controlled to maintain energy equilibrium within the standalone Microgrid, while a distinctive control approach for the three-phase inverter guarantees the delivery of high-quality power to AC loads. The proposed control methodologies in this paper employ super twisting sliding mode controllers to achieve rapid and effective responses during rapid changes in the system, whether on the generation or load side. The Hippopotamus optimization method is developed to obtain accurate values of various parameters used in super twisting sliding mode controllers under various operating conditions. The paper emphasizes the diverse outcomes and knowledge acquired through the execution of Hardware-in-the-Loop simulations utilizing the OPAL-RT platform. By employing this cutting-edge technology, researchers successfully tested and validated various hardware components within a simulated environment, facilitating a more efficient and cost-effective approach to analyzing and optimizing system performance. Further, detailed small signal analysis for the proposed methodology is also included in this paper.

Odor Source Localization (OSL) technology allows autonomous agents like mobile robots to find an unknown odor source in a given environment. An effective navigation algorithm that guides the robot to approach the odor source is the key to successfully locating the odor source. Compared to traditional olfaction-only OSL method, our proposed method integrates vision and olfaction sensor modalities to localize odor sources even if olfaction sensing is disrupted by turbulent airflow or vision sensing is impaired by environmental complexities. The model leverages the zero-shot multi-modal reasoning capabilities of large language models (LLMs), negating the requirement of manual knowledge encoding or custom-trained supervised learning models. A key feature of the proposed algorithm is the `High-level Reasoning’ module, which encodes the olfaction and vision sensor data into a multi-modal prompt and instructs the LLM to employ a hierarchical reasoning process to select an appropriate high-level navigation behavior. Subsequently, the `Low-level Action’ module translates the selected high-level navigation behavior into low-level action commands that can be executed by the mobile robot. To validate our method, we implemented the proposed algorithm on a mobile robot in a complex, real-world search environment that presents challenges to both olfaction and vision-sensing modalities. We compared the performance of our proposed algorithm to single sensory modality-based olfaction-only and vision-only navigation algorithms, and a supervised learning-based vision and olfaction fusion navigation algorithm. Experimental results demonstrate that multi-sensory navigation algorithms are statistically superior to single sensory navigation algorithms. The proposed algorithm outperformed the other algorithms in both laminar and turbulent airflow environments. The code for this work can be found at: https://github.com/SunzidHassan/24_Vision-Olfaction-LLM.

The basis of orchard production are rootstocks that reduce the vigor of sweet cherry trees. However, not all rootstocks for this species can be easily propagated using traditional methods by stooling or cutting. Some of these must be propagated using the in vitro culture method. This results in their high price and, consequently, the price of maiden sweet cherry trees. The experiment assessed the growth of maiden trees of selected sweet cherry varieties ('Bellise', 'Earlise', 'Lapins', 'Vanda') depending on the method of the semi-dwarf GiSelA 5 rootstock propagation. Additionally the intensity of life processes taking place in the obtained maiden trees was also examined. One type of this rootstock, obtained by cheaper method from softwood cuttings, was compared with the another propagated using the in vitro method. There were no significant differences in the percentage of maiden trees obtained in both types of rootstocks propagation method and in the two years of study. The efficiency of maiden trees varied little and ranged from 77.43% to 87.74% depending on the factors considered. The vigor of maiden trees growth in the first year of the study was stronger than in the second one. Only the diameter of maiden trees stem varied (from 7% to 39%), depending on the variety considered. With the exception of one variety, maiden trees produced on rootstock propagated from stem cuttings were characterized by a much larger diameter for the three varieties, ranging from 23% to 29% compare to in vitro. Also with this method of propagation, the rootstocks had a better developed root system and the maiden trees had a greater fresh mass. However, most often, no significant differences were found between the methods of propagation, only when comparing the years of research. The activity of life processes of maiden sweet cherry trees varied and no constant regularities were found. The only regularity observed was that maiden trees in the second year of the experiment were more often characterized by lower levels of the tested life parameters (Pn, E, C and Int.CO2), which was associated with worse growth results.

Two gravel pit lakes in central Texas were examined over the course of two years with upgradient and downgradient piezometer installations. Groundwater and lake water were sampled bimonthly for nutrients, water levels, and groundwater chemistry, in addition, rain and lake gauges and mini-piezometers were installed, depth surveys were conducted, and a simple 2-D flow model was constructed. The project goal was evaluating and examining flow dynamics and chemical effects as groundwater flows to surface water and back to groundwater with the intent to understand the effects that gravel pit lake systems have on connected shallow groundwater. Both lake systems were shown to be flow-through systems that influence the water quality by decreasing the dissolved nutrients in the groundwater in their vicinity while oxygenating the water and altering the pH. However, the lakes are also prone to high levels of evaporation, rendering minor improvements to water quality at the cost of decreasing the quantity of water in storage within the aquifer. Similar groundwater and mine lake systems may show comparable tendencies, providing new information for water managers, regulators, and stake holders about the potential roles that non-remediated gravel pit lakes may play in local ecosystems and aquifer dynamics.

This review aims to describe the role of Limbic System-Associated Membrane Protein (LSAMP) in normal and pathophysiology and its potential implications in oncogenesis. We have summarized research articles reporting the role of LSAMP in the development of a variety of malignancies such as clear cell renal cell carcinoma, prostatic adenocarcinoma, lung adenocarcinoma, osteosarcoma, neuroblastoma, acute myeloid leukemia, and epithelial ovarian cancer. We also examine current understandings of how defects in LSAMP gene function may contribute to oncogenesis. Finally, this review discuss-es the implications of future LSAMP research and clinical applications. Recent Findings LSAMP has been originally described as a surface adhesion glycoprotein expressed on cortical and subcortical neuronal somas and dendrites during the development of the limbic system. It is categorized as part of the IgLON immunoglobulin superfamily of cell-adhesion molecules and is involved in regulating neurite outgrowth and neural synapse generation. LSAMP is both aberrantly expressed and implicated in the development of neuropsychiatric disorders due to its role in the formation of specific neuronal connections within the brain. Additionally, LSAMP has been shown to support brain plasticity via the formation of neuronal synapses and is involved in modulating the hypothalamus in anxiogenic environments. In murine studies, loss of LSAMP expression was associated with decreased sensitivity to amphetamine, increased sensitivity to benzodiazepines, increased hyperactivity in new environments, abnormal social behavior, decreased aggressive behavior, and decreased anxiety. Findings have suggest-ed that LSAMP plays a role in attuning serotonergic activity as well as GABA activity. Given its importance to limbic system development, LSAMP has also been studied in the context of suicide. In malignancies, LSAMP plays a significant role as a putative tumor suppressor, the loss of which leads to more aggressive phenotypes and mortality from metastatic disease. Loss of the LSAMP gene facilitates epithelial-mesenchymal transition, or EMT, where epithelial cells lose adhesion and gain the motile properties associated with mesenchymal cells. Additionally, LSAMP and the function of the RTK pathway have been implicated in tumorigenesis through the modulation of RTK expression in cell membranes and the activation of second messenger pathways and β-catenin.

The blood-brain barrier (BBB) strictly regulates the penetration of substances into the brain, which, although important for maintaining brain homeostasis, may delay drug development because of the difficulties in predicting pharmacokinetics/pharmacodynamics (PKPD), toxicokinetics/toxicodynamics (TKTD), toxicity, safety, and efficacy in the central nervous system (CNS). Moreover, BBB functional proteins show species differences; therefore, humanized in vitro BBB models are urgently needed to improve the predictability of preclinical studies. Recently, international trends in the 3Rs in animal experiments and the approval of the FDA Modernization Act 2.0 have accelerated the application of microphysiological systems (MPSs) in preclinical studies, and in vitro BBB models have become synonymous with BBB-MPSs. Recently, we developed an industrialized humanized BBB-MPS, BBB-NET. In our previous report, we reproduced transferrin receptor (TfR)-mediated transcytosis with high efficiency and robustness with hydrogels that include fibrin and collagen I microfibers (CMFs). Herein, we investigated how adding CMFs to the fibrin gel benefits BBB-NETs, showed that CMFs accelerated capillary network formation and maturation by promoting astrocyte (AC) survival, and clarified that integrin 1 is involved in the mechanism of CMFs. Our data suggest that quality control (QC) of CMFs is important for ensuring the stable production of BBB-NETs.

Sex differences in brain metabolism and their relationship to neurodegenerative diseases like Alzheimer's are an important emerging topic in neuroscience. Intrinsic anatomic and metabolic differences related to male and female physiology have been described, underscoring the importance of considering biological sex in studying brain metabolism and associated pathologies. The hippocampus is a key structure exhibiting sex differences in volume and connectivity. Adult neurogenesis in the dentate gyrus, dendritic spine density, and electrophysiological plasticity contribute to the hippocampus' remarkable plasticity. Glucose transporters GLUT3 and GLUT4 are expressed in human hippocampal neurons, with proper glucose metabolism being crucial for learning and memory. Sex hormones play a major role, with the aromatase enzyme that generates oestradiol increasing in neurons and astrocytes as an endogenous neuroprotective mechanism. Inhibition of aromatase increases gliosis and neurodegeneration after brain injury. Genetic variants of aromatase may confer higher Alzheimer's risk. Oestrogen replacement therapy in postmenopausal women prevents hippocampal hypometabolism and preserves memory. Insulin is also a key regulator of hippocampal glucose metabolism and cognitive processes. Dysregulation of the insulin-sensitive glucose transporter GLUT4 may explain the comorbidity between type II diabetes and Alzheimer's. GLUT4 colocalizes with the insulin-regulated aminopeptidase IRAP in neuronal vesicles, suggesting an activity-dependent glucose uptake mechanism. Sex differences in brain metabolism are an important factor in understanding neurodegenerative diseases and future research must elucidate the underlying mechanisms and potential therapeutic implications of these differences.

As a result of the low organic matter content of the sandy soils in Romania, for the success of most crops, large amounts of chemical fertilizers are needed, which can often lead to the pollution of groundwater with nitrates, given the poor hydrophysical properties in terms of retaining chemical elements by these soils. In this sense, the study carried out in the period 2020-2023 within the rotation cowpea-rye+cowpea successive crop-sorghum for grains, aimed to promote the system of sustainable agriculture, by incorporating into the soil, in the form of green manure, the cowpea sown in succession after the rye. The soil quality analyses, carried out the following year after harvesting sorghum for grains, showed that by incorporating 52.4 t/ha of cowpea biomass into the soil between October 15-20, the quality characteristics of the sandy soil improved significantly. Thus, compared to the initial state of soil fertility, there were annual increases of 0.017-0.029%, in total nitrogen, by 9.5-13.3 ppm, in extractable phosphorus, by 11.333-24 ppm, in exchangeable potassium and by 0.008-0.012%, to organic carbon, also depending on the sorghum fertilization system. Also, the incorporation of green cowpea manure into the soil contributed to reducing the acidity of the soil pH and improving the water holding capacity of the soil profile.

Thanks to the rapid advancements in Connected and Automated Vehicles (CAVs) and vehicular communication, the concept of the Internet of Vehicles (IoV) combined with Artificial Intelligence (AI) and big data technologies promotes the vision of an Intelligent Transportation System (ITS). By enabling a comprehensive data exchange platform, ITS is critical in enhancing road safety, traffic efficiency, and the overall driving experience. However, the open and dynamic nature of IoV networks brings significant performance and security challenges to IoV data acquisition, storage, and usage. To comprehensively tackle these challenges, this paper proposes a Decentralized Digital Watermarking framework for smart Vehicular networks (D2WaVe). Specifically, D2WaVe consists of two core components: FIAE-GAN, a novel feature-integrated and attention-enhanced robust image watermarking model based on a Generative Adversarial Network (GAN), and BloVA, a Blockchain-based Video frames Authentication scheme. By leveraging an encoder-noise-decoder framework, trained FIAE-GAN watermarking models can achieve the invisibility and robustness of watermarks that can be embedded in video frames to verify the authenticity of video data. Then, BloVA ensures the integrity and auditability of IoV data in the storing and sharing stages. Experimental results based on a proof-of-concept prototype implementation validate the feasibility and effectiveness of our D2WaVe scheme for securing and auditing video data exchange in smart vehicular networks.

This paper explores the application of the generalized double reduction method to the (1+1)-dimensional Kaup-Boussinesq (K-B) system, which models nonlinear wave propagation. Double reduction method is a structured and systematic approach in the analysis of partial differential equations (PDEs). We first identify the Lie point symmetries of the K-B system and construct four non-trivial conservation laws using the multiplier method. The association between the Lie point symmetries and the conservation laws is established, and the generalized double reduction method is then applied to transform the B-K system into second-order differential equations or algebraic equations. The reduction process allowed us to derive two exact solutions for the K-B system, illustrating the method’s effectiveness in handling nonlinear systems. This work highlights the effectiveness of the generalized double reduction method in simplifying and solving nonlinear nonlinear systems of PDEs, contributing to a deeper understanding of the systems.

For each measurement, at least three objects are needed: a solid, neutral object as a measuring instrument and two objects for comparison. This system of three spatial dimensions per object has a common time. All measurements are based on orbits relative to the firmament and coincidences of the spatial coordinates of the objects after rotations with π and provide the energy of the system. These results in the most suitable coordinate system with dimensions of powers of π in which the radius is curved, as are the longitude and latitude. Polynomials P(2π) correspond to neutral objects, in addition to P(π) for charge, spin, isospin. In addition, it is assumed that the cosmos consists of a single type of particle with a speed of light c. Normalizing to the rest mass of the electron, the neutrinos are ντ=π, νμ=1, and νe=1/π. The energy of an electron is: Ee=gfreq(π)+1−1/π. An algorithm is derived from a Christoffel symbol and similar to a lattice gauge calculation. It provides exact residual masses for neutrons, protons, muons, tauons, quarks u, d, and pions. The theory can be applied to the inner planetary system and the cosmos and explains the hierarchy problem.

Disruptions in power quality have a negative impact on many energy consumers. These include lighting, where interference manifests itself, among others, in the form of light flickering. The article presents phenomena accompanying the operation of modern light sources against the background of exemplary results of studies on the flicker of conventional light sources, such as incandescent or fluorescent lamps. To assess the effects of these phenomena, it is necessary to use measures other than those traditionally used. The results of measurements carried out in accordance with different recommendations take into account both power supply disturbances and the properties of modern light sources.

The current emphasis in the advancement of space-based synthetic aperture radar (SAR) is on lightweight payloads under 100kg with resolutions surpassing 1m. This focus is directed towards meeting the launch criteria for multiple satellites on a single rocket and cutting costs. This article discusses the creation and progress of a Ku-band SAR payload for the Taijing-4(03) Satellite, launched on January 23, 2024 accompanied by four other satellites. The SAR payload design was customized to meet the demands of a micro-nano satellite platform, resulting in a lightweight, flat design weighing less than 80kg, seamlessly integrated with the plate-shaped satellite platform. The article also introduces a beam optimization strategy for the phased array SAR antenna, significantly boosting the SAR system’s performance. The SAR payload provides various operating modes like slide-spot, strip, scan 1, scan 2, and others, with a maximum achievable resolution exceeding 1m. Extensive in-orbit testing of the payload produced numerous high-quality SAR images with potential uses in emergency disaster mitigation, safeguarding ecosystems, monitoring forests, managing crops, tracking sea ice, and more.

This paper discusses the application of artificial intelligence in imaging omics, especially in cancer research. Imaging omics enables detailed analysis of spatial and temporal heterogeneity of tumours through high-throughput extraction of quantitative features from medical images such as MRI, PET, and CT. This paper focuses on applying PARKS systems to automate the recognition, segmentation, and extraction of image features, significantly enhancing the capabilities of clinical decision support systems (CDSS). The future direction is to establish a robust network infrastructure for radiology Medication-led Health care (RLHC) to facilitate the development and application of personalised treatment protocols, and to improve diagnostic accuracy, prognosis assessment, and treatment recommendations by uploading quantitative image features to a shared database and comparing them with historical images.

With the increasing awareness of environmental protection, the rotary hearth furnace system has emerged as a key technology that facilitates a win-win situation for both environmental protection and enterprise economic benefits. This is attributed to its high flexibility in raw material utilization, capability of directly supplying blast furnaces, low energy consumption, and high zinc removal rate. However, the complexity of the raw material proportioning process, coupled with its reliance on manual labor, results in a time-consuming and inefficient process. This paper innovatively introduces an intelligent formula method for raw materials based on online clustering algorithms and develops an intelligent batching system for rotary hearth furnaces. Firstly, the ingredients of raw materials undergo data preprocessing, which involves using the Local Outlier Factor (LOF) method to detect any abnormal values, Kalman filtering to smooth the data, and performing one-hot encoding to represent the different kinds of raw materials. Afterwards, the affinity propagation (AP) clustering method is used to evaluate past data on the ingredients of raw materials and their ratios. This analysis aims to extract information based on human experience with ratios and create a library of machine learning formulas. The incremental AP clustering algorithm is utilized to learn new ratio data and continuously update the machine learning formula library. To ensure that the formula meets the actual production performance requirements of the rotary hearth furnace, the machine learning formula is fine-tuned based on expert experience. The integration of machine learning and expert experience has demonstrated good flexibility and satisfactory performance in the practical application of intelligent formulas for rotary hearth furnaces. An intelligent batching system is developed and executed at a steel plant in China. It shows an excellent user interface and has significantly enhanced batching efficiency and product quality.

The dissolution of CO₂ in seawater in the form of bicarbonate ions is an attractive alternative to the storage in geological formations, on the conditions that the storage is stable over long times and does not harm the marine environment. In this work, we focus on the long-term chemical stability of CO₂ absorbed in seawater as bicarbonate, by monitoring the physico-chemical properties of the solutions (pH, dissolved inorganic carbon and alkalinity), in six different sets of experiments on both natural and artificial seawater lasting up to three months. The bicarbonate treatment of natural seawater consists in pouring pre-equilibrated solutions obtained from the reaction of CO₂ and Ca(OH)₂. If the pre-equilibrated mixture does not exceed a critical threshold (ca. 1000-1500 mmol/L), the resulting bicarbonate-rich solutions can be stable for over three months.

Following the transfer of hospital services to provincial government oversight, it is essential to understand how such changes impact service quality and efficiency. This study aims to identify the factors affecting hospital service quality post-transfer and evaluate the role of professional nurses in maintaining service standards under new governance.We conducted the study at sub-district health-promoting hospitals using a mixed-methods design based on the Global Fund-HSS framework (2012), which categorizes factors into accessibility, processes, productivity, and outcomes. We collected data from 340 nurses and 400 patients using structured questionnaires. We divided the questionnaires into seven sections, which captured a range of metrics such as the personal data of nurses, hospital service activities, and leadership qualities. We employed a Likert scale to gauge perceptions and expectations of service quality. We used paired t-tests to compare performance metrics before and after the administrative transfer, one-way ANOVA to assess variability among different service units, and binary logistic regression to identify key determinants of service quality in our statistical analysis. The analysis revealed a significant correlation between the attitudes and competencies of healthcare teams and the levels of service quality. Notably, service units transferred less than 50% exhibited minimal changes, indicating that the degree of transfer significantly impacts service outcomes. Despite these variations, the fundamental mission of promoting health remained consistent. The study emphasizes the critical role of nurses and recommends further research to identify additional factors that could improve service quality in transferred healthcare facilities. This could contribute to improved healthcare delivery across restructured health systems.

Following the transfer of hospital services to provincial government oversight, it is essential to understand how such changes impact service quality and efficiency. This study, conducted from 1 March 2023 to 31 March 2024 at sub-district health-promoting hospitals in Thailand, aims to identify the factors affecting hospital service quality post-transfer and evaluate the role of profes-sional nurses in maintaining service standards under new governance. Utilizing a mixed-methods design based on the Global Fund-HSS framework (2012), which categorizes factors into accessibility, processes, productivity, and outcomes, we collected data from 340 nurses and 400 patients through structured questionnaires. These questionnaires were divided into seven sec-tions, capturing metrics such as the personal data of nurses, hospital service activities, and lead-ership qualities. We employed a Likert scale to gauge perceptions and expectations of service quality and conducted paired t-tests to compare performance metrics before and after the admin-istrative transfer. One-way ANOVA was used to assess variability among different service units, and binary logistic regression helped identify key determinants of service quality. The analysis revealed a significant correlation between the attitudes and competencies of healthcare teams and the levels of service quality. Notably, service units transferred less than 50% exhibited mini-mal changes, indicating that the degree of transfer significantly impacts service outcomes. Despite these variations, the fundamental mission of promoting health remained consistent. The study emphasizes the critical role of nurses and recommends further research to identify additional factors that could improve service quality in transferred healthcare facilities, potentially contributing to enhanced healthcare delivery across restructured health systems.

Background: Low back pain (LBP) is a major cause of disability globally, and the diagnosis of LBP is challenging for clinicians. Objective: This study aimed to assess the accuracy level of arti-ficial intelligence as a Clinical Decision Support System (CDSS) called Therapa compared to MRI in predicting lumbar disc herniated patients. Methods: One hundred low back pain patients aged ≥18 years old were included in the study. The study was conducted in 3 stages, at first, a case se-ries was conducted by matching MRI and Therapha diagnosis for 10 patients. Subsequently, Delphi methodology was employed to establish a clinical consensus. Lastly, to determine the ac-curacy of the newly developed software, a cross-sectional study was undertaken, involving 100 patients. Results: The software showed a significant diagnostic accuracy with the area under the curve in the ROC analysis determined as 0.84 with a sensitivity of 88% and a specificity of 80%. Conclusion: The study’s findings revealed that CDSS using Therapha has a reasonable level of ef-ficacy, and this can be utilized clinically to acquire faster and more accurate screening of patients with lumbar disc herniation.

Cancer cells are believed to be recognized and controlled by the immune system of the host, especially the adaptive immune system. Cancers may be initiated by a single gene defect in stem cells or regenerating cells during the replacement of old or injured tissue cells, and additional gene defect(s) then occur. To compensate for missing proteins intracellular homeostasis systems, such as proteostasis, may work with activation of new genes. The risk or predisposing factors in cancers may be related to the number of tissue cell renewals (the turnover time) and the probability of gene defect(s). Oncological symptoms may be associated with substances that are derived from antigens expressed on cancer cells and those derived from damaged cancer cells with corresponding immune reactions. In patients with cancers or other conditions, inadequate immune components to control these substances may result in chronic inflammatory or autoimmune diseases. Cancer cells communicate with host cells, including the immune cells such as T cells, for survival and proliferation and may need assistance from immune cells against intracellular infections or physicochemical insults. T cell-based immune therapeutics have been developed and clinically used in some cancers, especially in advanced cancers. We further discuss the oncogenesis, pathophysiology of cancers, and unresolved issues in tumor immunology and reinterpret the effectiveness of T cell-based therapies under the protein-homeostasis-system hypothesis.

This study discusses the application of deep learning technology in network intrusion detection systems (IDS) and focuses on a new model named CNN-Focal. First, through the review of traditional IDS technology, it analyzes its limitations in dealing with complex network traffic. Then, the design principle of the CNN-Focal model is described in detail, which uses threshold convolution and SoftMax multi-class classification technology to effectively improve abnormal traffic detection's accuracy and efficiency. The experimental results show that CNN-Focal performs well on the open data set, demonstrating the potential and advantages of its application in the natural network environment and providing a new perspective and method for further research of deep learning in the field of network security in the future.

The present study is to address performance of the dairy supply chain and emphasizes the significance of reliability of the chain as a fundamental and critical attribute for fulfilling the safety requirements. In order to evaluate the performance of the dairy supply chain, the present study utilizes the reliability attribute, which is an essential and informative system performance indicator. The study considers not only the inputs and outputs but also internal activities and relationships between customers, manufacturers, and suppliers. To minimize calculations and resolve uncertainties and vagueness that may be experienced in the performance evaluation process, this study proposes the use of the Fuzzy Inference System (FIS) approach and evaluates the reliability at each level of the supply chain: supply, manufacturing, and distribution. Combining the reliability of different levels, the study finally calculates the reliability of the entire supply chain. The proposed methodology offers a practical approach for evaluating the reliability performance of a dairy supply chain and can be easily extended to other industries as well. To verify the proposed methodology, a real case in the Iranian dairy industry is chosen to be studied. Results indicate that the proposed methodology can effectively identify bottlenecks or weaknesses in the dairy supply chain and help develop effective strategies to address them. Analyzing factors influencing supply chain reliability can optimize operations, reduce costs, and improve the overall quality of dairy product quality delivered to consumers.

Lithium-ion batteries have gained immense popularity in electric vehicles(EVs) due to their high energy density, offering an extended driving range. This has made them the preferred choice in the automotive industry, driving the shift towards EVs. However, alongside their popularity, safety concerns have arisen. The main issues include the potential for thermal runaway leading to fires or explosions, which can occur due to overcharging, physical damage, or manufacturing defects. While safety features and rigorous testing aim to mitigate these risks, ongoing research and development focus on enhancing safety measures to further ensure the reliability and safety of lithium-ion batteries in EVs. This paper focuses on the development and deployment of a real-time monitoring dashboard for Li-ion battery health monitoring using STM32 Microcontroller within the framework of Industry 4.0. Leveraging advanced sensor technology and data analytics, the dashboard provides continuous, comprehensive insights into battery performance, including state of charge and temperature. Integrating with Industry 4.0 principles, such as IoT, the system enables predictive maintenance, optimizing production processes and ensuring efficient utilization of resources. The implementation showcases the integration of real-time monitoring tools for Li-ion batteries, aligning with the transformative paradigms of Industry 4.0 to enhance operational efficiency and reliability within industrial settings.

Accurate measurement of water vapor is essential for researches and applications of meteorology, climatology and hydrology. Based on the BeiDou PPP-B2b service, real-time precipitable water vapor (PWV) is retrieved with the precise point positioning (PPP) software. The experiment was conducted in Beijing in January, 2023. Three solutions are designed with PPP using the BeiDou system only, the GPS system only and the BeiDou-GPS combined solution. Real-time PWVs for the 3 solutions are validated with the ERA5 reanalysis data. Between PWVs from single BeiDou and ERA, there are a bias of 0.7 mm and a RMSE of 1.8 mm. While for the GPS case, the bias is 0.73 mm and RMSE is 1.97 mm. The biases are less than 1 mm and RMSEs are less than 2mm. Both the BeiDou and the GPS processing performs very well. But little improvement is found for the BeiDou-GPS combined solution, comparing with the BeiDou system only and the GPS system only solution. This may be due to the poor handling of two different kinds of errors for the GPS and the BeiDou systems in our PPP software. A better PWV estimation with the two systems is to estimate PWV with single system at first step, and then to obtain the optimization by Bayesian model averaging.

The article aims to present the results of research on anaerobic digestion (AD) of waste wafers (WF - control) and co-substrate system - waste wafers and cheese (WFC - control), combined with digested sewage sludge. The aim of the study was to evaluate the physicochemical parameters of the chitosan/perlite (Ch/P; 3:1) carrier material and to verify its effect on the directions of change of the bacterial microbiome, removal kinetics of organic matter and AD process efficiency. The experiment was conducted in a laboratory, in a periodical mode of operation of bioreactors, under mesophilic conditions. The results of analyses of morphological-dispersive, spectroscopic, adsorption, thermal and microbiological properties confirmed that the tested carrier material can be an excellent option to implement in biotechnological processes, especially in anaerobic digestion. The microstructural properties of the carrier were influenced by both components: perlite determined the development of the specific surface area, while chitosan shaped the porosity of the system. The thermal properties were determined by the less heat-resistant component, present in a threefold higher weight proportion, i.e. chitosan. The evaluation of quantitative and qualitative changes in the genetic diversity of bacterial communities, carried out using Next Generation Sequencing (NGS), showed that the material has a modifying effect on the bacterial microbiome. Amount of bacteria from phyla Actinobacteria, Bacteroidetes, Campilobacterota, Chloroflexi, Euryarchaeota, Planctomycetes, and Proteobacteria decreased while Firmicutes, Synergistetes, and Thermotogae increased during the course of the experiment. The shapes of the FT-IR spectra indicated a dependence of the degradation rate on both the presence of the carrier and the cosubstrate system. Monitoring of the course of AD was carried out by measuring key parameters for the stability of the process: pH, VFA and VFA/TA ratio (volatile fatty acids/total alkalinity). As a result, an increase in the volume of biogas/methane produced, under the influence of the carrier, was recorded for WF-control by 12.05% and for WFC-control by 19.16%. The volume of methane for the WF-control increased from 351.72 m3 Mg-1 VS to 411.14 m3 Mg-1 VS, while for the cosubstrate sample it increased from 476.84 m3 Mg-1 VS to 518.08 m3 Mg-1 VS, confirming the validity of combining the respective cosubstrate with microbial carrier in anaerobic bioreactor.

Airborne distributed position and orientation system (ADPOS), which integrates multi-inertia measurement units(IMUs), data-processing computer and Global Navigation Satellite System (GNSS), serves as a key sensor in new higher-resolution airborne remote sensing, such as array SAR, multinode imaging loads and so on. ADPOS can provide multinode high accuracy spatio-temporal reference information to realize multinode motion compensation with the various state estimation methods such as Central Difference Kalman Filtering (CDKF), modifed CDKF. However, these methods focus on noise optimization based on known nonlinear models and its linear minimum variance estimation criterion, which cannot gain better estimation performance for high-dimensional nonlinear systems. For this reason, the variational optimization process based on conjugate gradient algorithm is proposed for the estimation of filtering update step, which is more efficient than linear minimum variance estimation criterion. Therefore, aiming at ADPOS with high-dimensional and nonlinearity, Central Difference Variational Filtering (CDVF) using variational optimization based on conjugate gradient algorithm for mean correction in the filtering update stage is devoted to advancing the estimation accuracy of multinode motion parameters. A real ADPOS flight test has been conducted, the experimental results show that the accuracy of the slave motion parameters has a distinct progress than the up to date CDKF, and the compensation performance is distinct.

The link between the world of communication and the world of racing is provided by telemetry systems in electric racing cars. These systems send real-time data about the vehicle’s behavior and systems to enable informed decisions during the race. The aim of this research was to integrate telemetry into the battery bank of a racing electric vehicle to find the optimal values of current and voltage that optimize the charging process, thus improving the vehicle’s performance in competition using Response Surface Analysis. Particularly, the telemetry system consisted of an Arduino Mega, a digital wattmeter, and temperature sensors, all installed in the vehicle. Once the telemetry data was obtained, a response surface design was adapted with current, voltage, and temperature as factors, varying from low to high values, with the objective function being to minimize the battery’s charging time. Applying the response surface methodology and the steepest descent algorithm, it was found that all factors significantly affect the charging time, with the minimum charge time being 6961 seconds, obtained with a current of 2.4 amps and voltages of 50.5 volts and 43.6 volts.

Heavy metals are dangerous contaminants constituting a threat to human health because they persist in soils and are easily transferred into the food chain, causing damages to human health. Among heavy metals, nickel appears to be one of the most dangerous responsible for different disorders. Public health protection requires nickel detection in environment and food chains; a simple, rapid and sensitive method to detect nickel contamination is represented by biosensors. In this paper we report the setup of a whole-cell based system, in which protoplasts, obtained from Nicotiana tabacum leaves, were used as transducers to detect the presence of heavy metal ions and, in particular, nickel ions. Protoplasts were genetically modified with a plasmid containing the GFP reporter gene under the promoter region of a sunflower small HSP gene control. By this device the presence of heavy metal ions was detected. Thus, the possibility to use this whole-cell system as a novel tool to detect the presence of nickel ions in food matrices was assessed.

An important issue concerning the inspection of the technical condition of electrical power components and systems is thermal imaging investigation. This paper presents how the thermograms obtained from these measurements should be interpreted correctly according different standards and how operators should react when a specific anomaly is detected. It is also a review article in which all currently applicable international standards are referred to. The motivation of the article relates to the fact that these standards seems to be too general and not cover all practical situations even though in many countries thermal imaging diagnostics of overhead lines or overhead outdoor and indoor power stations has been used for years on the basis of industry standards or good practices. The purpose of writing this article is precisely to encourage and provoke the global community of metrologists, scientists and engineers involved in thermographic measurements to discuss, strengthen efforts and establish relevant international standards for the interpretation of thermograms containing the relevant temperature anomalies.

Это исследование сосредоточено на разработке системы искусственного зрения для гибкого дельта-робота-манипулятора и ее интеграции с межмашинной связью для оптимизации взаимодействия устройств в реальном времени. Эта интеграция направлена ​​на увеличение скорости роботизированной системы и улучшение ее общей производительности. Предлагаемое сочетание системы искусственного зрения с межмашинной связью позволяет обнаруживать и распознавать цели с высокой точностью в режиме реального времени в ограниченном пространстве, предназначенном для позиционирования, дальнейшей локализации и выполнения производственных процессов, таких как сборка или сортировка деталей. . В данном исследовании RGB-изображения используются в качестве входных данных для алгоритма MASK-R-CNN, а результаты обрабатываются в соответствии с особенностями прототипа дельта-манипулятора робота. Данные, полученные от MASK-R-CNN, адаптированы для использования в системе управления дельта-роботом с учетом ее уникальных характеристик и требований к позиционированию. Технология M2M позволяет манипулятору робота быстро реагировать на изменения, например, на перемещение объектов или изменение их положения, что имеет решающее значение для задач сортировки и упаковки. Система была протестирована в условиях, близких к реальным, чтобы оценить ее производительность и надежность.

Wireless Sensor networks (WSNs) are gaining more and more research interest due to their ability to monitor large areas more or less independent. However, their reliability is an important issue, since their system reliability is influenced from a hardware, a data and an energetic point of view due to different factors like loading conditions, signal attenuation and battery lifetime. Regarding these issues, the positions of sensor nodes have to be chosen correctly to maximize the system reliability, which has to be considered during the development of new products equipped with WSNs. In this paper, an approach to estimate a WSNs system reliability during the development phase based on the analysis of the measurements on the example of strain measurements in finite elements (FE) models is presented. For that, the part under consideration is divided into regions with similar strains by the use of a Region Growing Algorithm (RGA). Afterwards, the WSN’s configuration is analyzed regarding its reliability in accordance to the data paths and measurement redundancy due to the sensor positions in the found measuring regions. This methodology is tested on an exemplary WSN configuration at an aircraft wing box under bending load and was found to work very well regarding the estimation of the hardware perspective on the system reliability. Therefore, the methodology and the developed algorithm show potentials for optimizations in sensor node positions for better reliability results.