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Taiwan has a sound solid waste recycling system, and waste-to-energy is attractive under the encouragement policy and economic feasibility, especially in central and southern regions with vast agricultural wastes. The four scenarios evaluated in this study relating to current use or under consideration for kitchen waste treatment strategy in Taiwan were incineration, landfill, composting, and anaerobic digestion. These scenarios were compared through life cycle assessment to obtain the most preferable treatment solution. The analysis was based on a functional unit, i.e., 1 metric ton of kitchen waste treated, and considered all impact categories through the CML_IA baseline 2000 method. It has shown that energy recovery had enormous effects on all scenarios with the anaerobic digestion having the highest environmental performance change. A comparison between actual electricity consumption and estimated electricity generation by kitchen waste treatment through anaerobic digestion indicates that decentralized electricity generation was suitable for central Taiwan and could be considered as the energy solution in a short-term context. This study provides an experience in selecting a proper waste-to-energy method with the most negligible environmental impact.

This article belongs to the Special Issue Novel Strategies for the Development of Healthier Meat and Meat Products and Determination of Their Quality Characteristics., There is growing interest in increasing omega-3 fatty acid (FA) contents in ruminant meat by means of dietary manipulation, but the effects of such manipulation on productive results and meat quality need to be ascertained. The aim of the present study was to assess the effects of supplementing lambs with competent reticular groove reflex (RGR) with marine algae as a source of omega-3 fatty acids on growth performance, carcass traits, and meat quality characteristics. Forty-eight feedlot lambs were distributed into three equal groups: the control group neither consumed marine algae nor had competent RGR, the second group received daily 2.5% of algae meal mixed in the concentrate, and the last group consumed the same amount of algae meal, but emulsified in a milk replacer and bottle-fed. Lambs in the second and third groups had competent RGR. There were not any negative effects on performance, carcass or meat quality parameters with algae supplementation. However, the results of the oxidative stability parameters were not conclusive. Ageing for 6 days improved meat tenderness and color, and increased lipid oxidation. In conclusion, algae meal inclusion in the diet of fattening lambs with competent RGR has no detrimental effects on animal performance, carcass traits or meat quality characteristics., This research was funded by the Spanish Ministry of Science and Innovation, project number AGL2016-75159-C2-2-R and the Spanish National Research Council, grant number 20217AT002.

In this paper, we propose a theoretical framework to explain how the transparent elastic grating structure can be employed to enhance the mechanical and optical properties for ultrasonic detection. Incident ultrasonic waves can compress the flexible material, where the change in thickness of the elastic film can be measured through an optical interferometer. Herein, the polydimethylsiloxane (PDMS) was employed in the design of a thin film grating pattern. The PDMS grating with the grating period shorter than the ultrasound wavelength allowed the ultrasound to be coupled into surface acoustic wave (SAW) mode. The grating gaps provided spaces for the PDMS grating to be compressed when the ultrasound illuminated on it. This grating pattern can provide an embedded thin film based optical interferometer through Fabry–Perot resonant modes. Several optical thin film-based technologies for ultrasonic detection were compared. The proposed elastic grating gave rise to higher sensitivity to ultrasonic detection than a surface plasmon resonance-based sensor, a uniform PDMS thin film, a PDMS sensor with shearing interference, and a conventional Fabry–Perot-based sensor. The PDMS grating achieved the enhancement of sensitivity up to 1.3 × 10−5 Pa−1 and figure of merit of 1.4 × 10−5 Pa−1 which were higher than those of conventional Fabry–Perot structure by 7 times and 4 times, respectively.

Recently, unmanned aerial vehicles (UAVs) have been applied to various applications. In order to perform repetitive and accurate tasks with a UAV, it is more efficient for the operator to perform the tasks through an integrated management program rather than controlling the UAVs one by one through a controller. In this environment, control packets must be reliably delivered to the UAV to perform missions stably. However, wireless communication is at risk of packet loss or packet delay. Typical network communications can respond to situations in which packets are lost by retransmitting lost packets. However, in the case of UAV control, delay due to retransmission is fatal, so control packet loss and delay should not occur. As UAVs move quickly, there is a high risk of accidents if control packets are lost or delayed. In order to stably control a UAV by transmitting control messages, we propose a control packet transmission scheme, ConClone. ConClone replicates control packets and then transmits them over multiple network connections to increase the probability of successful control packet transmission. We implemented ConClone using real equipment, and we verified its performance through experiments and theoretical analysis.

In this article, a wave propagation model is presented as the first step in the development of a new type of transluminal procedure for performing elastography. Elastography is a medical imaging modality for mapping the elastic properties of soft tissue. The wave propagation model is based on a Kelvin Voigt Fractional Derivative (KVFD) viscoelastic wave equation, and is numerically solved using a Finite Difference Time Domain (FDTD) method. Fractional rheological models, such as the KVFD, are particularly well suited to model the viscoelastic response of soft tissue in elastography. The transluminal procedure is based on the transmission and detection of shear waves through the luminal wall. Shear waves travelling through the tissue are perturbed after encountering areas of altered elasticity. These perturbations carry information of medical interest that can be extracted by solving the inverse problem. Scattering from prostate tumours is used as an example application to test the model. In silico results demonstrate that shear waves are satisfactorily transmitted through the luminal wall and that echoes, coming from reflected energy at the edges of an area of altered elasticity, which are feasibly detectable by using the transluminal approach. The model here presented provides a useful tool to establish the feasibility of transluminal procedures based on wave propagation and its interaction with the mechanical properties of the tissue outside the lumen., University College London, United Kingdom, Talentia scholarship (grant C2012H-75146405T-1) from the regional government of Andalusia, Spain, the Ministry of Education and Science, Spain, grants DPI2017-83859-R, EQC2018-004508-P and UNGR15-CE3664, Andalusia, Spain, grants SOMM17/6109/UGR, B-TEP-026-UGR18, IE2017-5537 and P18-RT-1653

Considering some unmeasurable states, a fuzzy static output control problem of nonlinear stochastic systems is discussed in this paper. Based on a modelling approach, a Takagi–Sugeno (T–S) fuzzy system, constructed by a family of stochastic differential equations and membership functions, is applied to represent nonlinear stochastic systems. Parallel distributed compensation (PDC) technology is used to construct the static output controller. A line-integral Lyapunov function (LILF) is used to derive some sufficient conditions for guaranteeing the asymptotical stability in the mean square. From the LILF, a potential conservatism produced by the derivative of the membership function is eliminated to increase the relaxation of sufficient conditions. Furthermore, those conditions are transferred into linear matrix inequality (LMI) form via projection lemma. According to the convex optimization algorithm, the feasible solutions are directly obtained to establish the static output fuzzy controller. Finally, a numerical example is applied to demonstrate the effectiveness and usefulness of the proposed design method.

With aging, cerebrovascular diseases can occur more often. Stroke cases involve hemiplegia, which causes difficulties in performing activities of daily living. Existing rehabilitation treatments are based on the subjective evaluation of the therapist as the need for non-contact care arises, it is necessary to develop a system that can self-rehabilitate and offer objective analysis. Therefore, we developed rehabilitation tools that enable self-rehabilitation exercises in a virtual space based on haptics. Thirty adults without neurological damage were trained five times in a virtual environment, and the time, number of collisions, and coordinates were digitized and stored in real time. An analysis of variance (ANOVA) of the time and distance similarity changes revealed that as the number of rounds increased, no changes or increases occurred (p ≥ 0.05), and the collisions and paths were stable as the training progressed (p &lt, 0.05). ANOVA showed a high correlation (0.90) with a decrease in the number of crashes and time required. It was meaningful to users when performing rehabilitation training more than four times and significantly impacted the analysis. This study analyzed the upper limb and cognitive rehabilitation of able-boded people in three-dimensional space in a virtual environment, the performance difficulty could be controlled through variations in rehabilitation models.

Because of extensive cultivation areas, various cultivars, nonstandard naming notations, and morphology similarity among relative cultivars, adulteration and associated business fraud may happen in the marketplaces of prickly ash pericarps due to higher financial gain and high-frequency trading. This study presents variations in the chemical components and contents of different prickly ash species from different plantations. Alkylamide profiling of pericarps derived from Zanthoxylum armatum, Z. bungeanum, and some relative Zanthoxylum species from 72 plantations across China were tested using ultra-performance liquid chromatography. Then, several chemometrics were applied to classify the prickly ash pericarps to reveal potential indicators that distinguish prickly ash pericarps and to identify the key factors that affect pericarp alkylamide profiling. The dominating alkylamides in the prickly ash pericarps were Z. piperitum (ZP)-amide C (0–20.64 mg/g) and ZP-amide D (0–30.43 mg/g). Alkylamide profiling of prickly ash pericarps varied significantly across species and geographical variations. ZP-amide D in prickly ash pericarps was identified as a potential indicator to distinguish prickly ash species. Longitude and aluminum content in soils were identified as key factors that affected alkylamide profiling of prickly ash pericarps. This study provides a useful tool to classify prickly ash species based on pericarp alkylamide profiling and to determine the key influence factors on pericarp alkylamide variations.

The antioxidant capacity (AC) and antioxidant activity (AA) of three flavonols (FLV), aglycones and their glycosylated derivatives were evaluated using 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) assays in various solvents. Findings confirmed that the glycosylation at the 3-position (3-glycosylation) always decreased the AC under most conditions due to substitution of the 3-position hydroxyl group and glycoside disruption in the molecular planarity. The 7-glycosylated derivatives did not have the above effects, thus generally exhibited ACs similar to their aglycones. Glycosylation decreased the AA of kaempferol and isorhamnetin for both assays in methanol, 3-glycosylation inhibited quercetin AA in the ABTS assay. In the DPPH assay, the AA of 3-glycosylated quercetin was significantly higher than quercetin. Using LC–MS/MS analysis, we found that quercetin and quercetin-7-glucoside underwent dimerization during the antioxidant reaction, potentially leading to a decline in AAs. However, 3-glycoside substitution may have hindered dimer formation, thereby allowing the FLVs to retain strong free radical scavenging abilities. National Key Research and Development Program of China | Ref. 2019YFC1606703

Recently, vast investments have been made worldwide in developing Cyber-Physical Systems (CPS) as solutions to key socio-economic challenges. The Internet-of-Things (IoT) has also enjoyed widespread adoption, mostly for its ability to add “sensing” and “actuation” capabilities to existing CPS infrastructures. However, attention must be paid to the impact of IoT protocols on the dependability of CPS infrastructures. We address the issues of CPS dependability by using an epidemic model of the underlying dynamics within the CPS’ IoT subsystem (CPS-IoT) and an interference-aware routing reconfiguration. These help to efficiently monitor CPS infrastructure—avoiding routing oscillation, while improving its safety. The contributions of this paper are threefold. Firstly, a CPS orchestration model is proposed that relies upon: (i) Inbound surveillance and outbound actuation to improve dependability and (ii) a novel information diffusion model that uses epidemic states and diffusion sets to produce diffusion patterns across the CPS-IoT. Secondly, the proposed CPS orchestration model is numerically analysed to show its dependability for both sensitive and non-sensitive applications. Finally, a novel interference-aware clustering protocol called “INMP”, which enables network reconfiguration through migration of nodes across clusters, is proposed. It is then bench-marked against prominent IoT protocols to assess its impact on the dependability of the CPS.

The fish-eye lens camera has a wide field of view that makes it effective for various applications and sensor systems. However, it incurs strong geometric distortion in the image due to compressive recording of the outer part of the image. Such distortion must be interpreted accurately through a self-calibration procedure. This paper proposes a new type of test-bed (the AV-type test-bed) that can effect a balanced distribution of image points and a low level of correlation between orientation parameters. The effectiveness of the proposed test-bed in the process of camera self-calibration was verified through the analysis of experimental results from both a simulation and real datasets. In the simulation experiments, the self-calibration procedures were performed using the proposed test-bed, four different projection models, and five different datasets. For all of the cases, the Root Mean Square residuals (RMS-residuals) of the experiments were lower than one-half pixel. The real experiments, meanwhile, were carried out using two different cameras and five different datasets. These results showed high levels of calibration accuracy (i.e., lower than the minimum value of RMS-residuals: 0.39 pixels). Based on the above analyses, we were able to verify the effectiveness of the proposed AV-type test-bed in the process of camera self-calibration.

To enable the full benefits from MU-MIMO (Multiuser-Multiple Input Multiple Output) and OFDMA (Orthogonal Frequency Division Multiple Access) to be achieved, the optimal use of these two technologies for a given set of network resources has been investigated in a rich body of literature. However, most of these studies have focused either on maximizing the performance of only one of these schemes, or have considered both but only for single-hop networks, in which the effect of the interference between nodes is relatively limited, thus causing the network performance to be overestimated. In addition, the heterogeneity of the nodes has not been sufficiently considered, and in particular, the joint use of OFDMA and MU-MIMO has been assumed to be always available at all nodes. In this paper, we propose a cross-layer optimization framework that considers both OFDMA and MU-MIMO for heterogeneous wireless networks. Not only does our model assume that the nodes have different capabilities, in terms of bandwidth and the number of antennas, but it also supports practical use cases in which nodes can support either OFDMA or MU-MIMO, or both at the same time. Our optimization model carefully takes into account the interactions between the key elements of the physical layer to the network layer. In addition, we consider multi-hop networks, and capture the complicated interference relationships between nodes as well as multi-path routing via multi-user transmissions. We formulate the proposed model as a Mixed Integer Linear Programming (MILP) problem, and initially model the case in which each node can selectively use either OFDMA or MU-MIMO; we then extend this to scenarios in which they are jointly used. As a case study, we apply the proposed model to sum-rate maximization and max–min fair allocation, and verify through MATLAB numerical evaluations that it can take appropriate advantage of each technology for a given set of network resources. Based on the optimization results, we also observe that when the two technologies are jointly used, more multi-user transmissions are enabled thanks to flexible resource allocation, meaning that greater use of the link capacity is achieved.

Maritime traffic and fishing activities have accelerated considerably over the last decade, with a consequent impact on the environment and marine resources. Meanwhile, a growing number of ship-reporting technologies and remote-sensing systems are generating an overwhelming amount of spatio-temporal and geographically distributed data related to large-scale vessels and their movements. Individual technologies have distinct limitations but, when combined, can provide a better view of what is happening at sea, lead to effectively monitor fishing activities, and help tackle the investigations of suspicious behaviors in close proximity of managed areas. The paper integrates non-cooperative Synthetic Aperture Radar (SAR) Sentinel-1 images and cooperative Automatic Identification System (AIS) data, by proposing two types of associations: (i) point-to-point and (ii) point-to-line. They allow the fusion of ship positions and highlight “suspicious” AIS data gaps in close proximity of managed areas that can be further investigated only once the vessel—and the gear it adopts—is known. This is addressed by a machine-learning approach based on the Fast Fourier Transform that classifies single sea trips. The approach is tested on a case study in the central Adriatic Sea, automatically reporting AIS-SAR associations and seeking ships that are not broadcasting their positions (intentionally or not). Results allow the discrimination of collaborative and non-collaborative ships, playing a key role in detecting potential suspect behaviors especially in close proximity of managed areas.

A non-contact heartbeat/respiratory rate monitoring system was designed using narrow beam millimeter wave radar. Equipped with a special low sidelobe and small-sized antenna lens at the front end of the receiving and transmitting antennas in the 120 GHz band of frequency-modulated continuous-wave (FMCW) system, this sensor system realizes the narrow beam control of radar, reduces the interference caused by the reflection of other objects in the measurement background, improves the signal-to-clutter ratio (SCR) of the intermediate frequency signal (IF), and reduces the complexity of the subsequent signal processing. In order to solve the problem that the accuracy of heart rate is easy to be interfered with by respiratory harmonics, an adaptive notch filter was applied to filter respiratory harmonics. Meanwhile, the heart rate obtained by fast Fourier transform (FFT) was modified by using the ratio of adjacent elements, which helped to improve the accuracy of heart rate detection. The experimental results show that when the monitoring system is 1 m away from the human body, the probability of respiratory rate detection error within ±2 times for eight volunteers can reach 90.48%, and the detection accuracy of the heart rate can reach 90.54%. Finally, short-term heart rate measurement was realized by means of improved empirical mode decomposition and fast independent component analysis algorithm.

In recent years, manufacturing companies have been continuously engaging in research for the full implementation of smart factories, with many studies on methods to prevent facility failures that directly affect the productivity of the manufacturing sites. However, most studies have only analyzed sensor signals rather than text manually typed by operators. In addition, existing studies have not proposed an actual application system considering the manufacturing site environment but only presented a model that predicts the status or failure of the facility. Therefore, in this paper, we propose a real-world failure prevention framework that alerts the operator by providing a list of possible failure categories based on a failure pattern database before the operator starts work. The failure pattern database is constructed by analyzing and categorizing manually entered text to provide more detailed information. The performance of the proposed framework was evaluated utilizing actual manufacturing data based on scenarios that can occur in a real-world manufacturing site. The performance evaluation experiments demonstrated that the proposed framework could prevent facility failures and enhance the productivity and efficiency of the shop floor.

Transition metal hydroxides have attracted a lot of attention as the electrode materials for supercapacitors owing to their relatively high theoretical capacity, low cost, and facile preparation methods. However, their low intrinsic conductivity deteriorates their high-rate performance and cycling stability. Here, self-supported sheets-on-wire CuO@Ni(OH)2/Zn(OH)2 (CuO@NiZn) composite nanowire arrays were successfully grown on copper foam. The CuO nanowire backbone provided enhanced structural stability and a highly efficient electron-conducting pathway from the active hydroxide nanosheets to the current collector. The resulting CuO@NiZn as the battery-type electrode for supercapacitor application delivered a high capacity of 306.2 mAh g−1 at a current density of 0.8 A g−1 and a very stable capacity of 195.1 mAh g−1 at 4 A g−1 for 10,000 charge–discharge cycles. Furthermore, a quasi-solid-state hybrid supercapacitor (qss HSC) was assembled with active carbon, exhibiting 125.3 mAh g−1 at 0.8 A g−1 and a capacity of 41.6 mAh g−1 at 4 A g−1 for 5000 charge–discharge cycles. Furthermore, the qss HSC was able to deliver a high energy density of about 116.0 Wh kg−1. Even at the highest power density of 7.8 kW kg−1, an energy density of 20.5 Wh kg−1 could still be obtained. Finally, 14 red light-emitting diodes were lit up by a single qss HSC at different bending states, showing good potential for flexible energy storage applications.

Polysulfone membranes exhibit resistance to high temperature with low manufacturing cost and high efficiency in the separation process. The composition of gases is an important step that estimates the efficiency of separation in membranes. As membrane types are currently becoming in demand for CO2/CH4 segregation, polysulfone will be an advantageous alternative to have in further studies. Therefore, research is undertaken in this study to evaluate two solvents: chloroform (CF) and tetrahydrofuran (THF). These solvents are tested for casting polymeric membranes from polysulfone (PSF) to separate every single component from a binary gas mixture of CO2/CH4. In addition, the effect of gas pressure was conducted from 1 to 10 bar on the behavior of the permeability and selectivity. The results refer to the fact that the maximum permeability of CO2 and CH4 for THF is 62.32 and 2.06 barrer at 1 and 2 bars, respectively. Further, the maximum permeability of CF is 57.59 and 2.12 barrer at 1 and 2 bars, respectively. The outcome selectivity values are 48 and 36 for THF and CF at 1 bar, accordingly. Furthermore, the study declares that with the increase in pressure, the permeability and selectivity values drop for CF and THF. The performance for polysulfone (PSF) membrane that is manufactured with THF is superior to that of CF relative to the Robeson upper bound. Therefore, through the results, it can be deduced that the solvent during in-situ synthesis has a significant influence on the gas separation of a binary mixture of CO2/CH4.

Pathogenic microbial contamination is significantly influenced by the crop surface properties and surfactant use, which are crucial factors for the postharvest washing process. However, there is little information on the interaction between surfactant and food pathogens on food crops. Thus, this study (1) investigated whether the attachment of Salmonella increases as pesticides denature epicuticular wax crystals and (2) tested if the antibacterial effect of ozonated water can be improved on waxy produce surfaces by adding surfactant to ozonated water. As a result, significantly lower levels of Salmonella Typhimurium attached to waxy leaf surfaces than they did to glossy and pesticide-treated waxy leaf surfaces (3.28 as opposed to 4.10 and 4.32 Log colony forming units (CFU)/cm2, respectively), suggesting that the pesticide containing a surfactant application increased the attachment of S. Typhiumurium on waxy leaf surfaces. There was no significant washing effect on waxy leaf surfaces washed with ozonated water. On the other hand, S. Typhimurium were not detected on waxy leaf surfaces after washing with surfactant-added ozonated water.

[EN] Long-term wounds need a continuous assessment of different biophysical parameters for their treatment, and there is a lack of affordable biocompatible devices capable of obtaining that uninterrupted flow of data. A portable prototype that allows caregivers to know the local temperature behavior of a long-term wound over time and compare it with different reference zones has been developed. Alternative flexible substrates, screen-printing techniques, polymeric inks, and an embedded system have been tested to achieve potential indicators of the status and evolution of chronic wounds. The final system is formed by temperature sensors attached to a flexible and stretchable medical-grade substrate, where silver conductive tracks have been printed as interconnections with the data-acquisition unit. In addition, a specific datalogger has been developed for this system. The whole set will enable health personnel to acquire the temperature of the wound and its surroundings in order to make decisions regarding the state and evolution of the wound., This research was supported by the Spanish Government/FEDER funds (RTI2018-100910B-C43) (MINECO/FEDER). The work presented also was funded by the Conselleria d'Economia Sostenible, Sectors Productius i Treball, through IVACE (Instituto Valenciano de Competitividad Empresarial). HYBRID II Project-Application No.: IMAMCI/2021/1.

Electrodes for resistance spot welding inevitably wear out. In order to extend their service life, the tip-dressing process restores their original geometry. So far, however, the point in time for tip-dressing is mainly based on experience and not on process data. Therefore, this study aims to evaluate the in-situ or inline wear during the welding process without using additional sensors, and to base the timing for tip-dressing on continuous process monitoring, extending electrode life even further. Under laboratory conditions, electrode wear is analyzed by topographical measurements deepening the knowledge of the known main wear modes of resistance-spot-welding electrodes, mushrooming and plateau forming, and characterizing an electrode length delta over the number of spot welds. In general, electrode wear results in deformation of the electrode contact area, which influences process parameters and thereby weld quality. The conducted tests show correlation between this deformed contact area and the electrode length delta. The study shows that this electrode length delta is visible in actual process data, and can therefore be used as a criterion to characterize the wear of electrodes. Furthermore, this study gives reason to question commonly used spot-welding quality criteria and suggests different approaches, such as basing spot-welding quality on the possibility of nondestructive testing.

Accurate segmentation of entity categories is the critical step for 3D scene understanding. This paper presents a fast deep neural network model with Dense Conditional Random Field (DCRF) as a post-processing method, which can perform accurate semantic segmentation for 3D point cloud scene. On this basis, a compact but flexible framework is introduced for performing segmentation to the semantics of point clouds concurrently, contribute to more precise segmentation. Moreover, based on semantics labels, a novel DCRF model is elaborated to refine the result of segmentation. Besides, without any sacrifice to accuracy, we apply optimization to the original data of the point cloud, allowing the network to handle fewer data. In the experiment, our proposed method is conducted comprehensively through four evaluation indicators, proving the superiority of our method.

Position–time tracking of athletes during a race can provide useful information about tactics and performance. However, carrier-phase differential global navigation satellite system (dGNSS)-based tracking, which is accurate to about 5 cm, might also allow for the extraction of variables reflecting an athlete’s technique. Such variables include cycle length, cycle frequency, and choice of sub-technique. The aim of this study was to develop a dGNSS-based method for automated determination of sub-technique and cycle characteristics in cross-country ski skating. Sub-technique classification was achieved using a combination of hard decision rules and a neural network classifier (NNC) on position measurements from a head-mounted dGNSS antenna. The NNC was trained to classify the three main sub-techniques (G2–G4) using optical marker motion data of the head trajectory of six subjects during treadmill skiing. Hard decision rules, based on the head’s sideways and vertical movement, were used to identify phases of turning, tucked position and G5 (skating without poles). Cycle length and duration were derived from the components of the head velocity vector. The classifier’s performance was evaluated on two subjects during an in-field roller skiing test race by comparison with manual classification from video recordings. Classification accuracy was 92–97% for G2–G4, 32% for G5, 75% for turning, and 88% for tucked position. Cycle duration and cycle length had a root mean square (RMS) deviation of 2–3%, which was reduced to &lt, 1% when cycle duration and length were averaged over five cycles. In conclusion, accurate dGNSS measurements of the head’s trajectory during cross-country skiing contain sufficient information to classify the three main skating sub-techniques and characterize cycle length and duration.

Due to its flexibility in terms of charging and billing, the smart grid is an enabler of many innovative energy consumption scenarios. One such example is when a landlord rents their property for a specific period to tenants. Then the electricity bill could be redirected from the landlord’s utility to the tenant’s utility. This novel scenario of the smart grid ecosystem, defined in this paper as Grid-to-Go (G2Go), promotes a green economy and can drive rent reductions. However, it also creates critical privacy issues, since utilities may be able to track the tenant’s activities. This paper presents P4G2Go, a novel privacy-preserving scheme that provides strong security and privacy assertions for roaming consumers against honest but curious entities of the smart grid. At the heart of P4G2Go lies the Idemix cryptographic protocol suite, which utilizes anonymous credentials and provides unlinkability of the consumer activities. Our scheme is complemented by the MASKER protocol, used to protect the consumption readings, and the FIDO2 protocol for strong and passwordless authentication. We have implemented the main components of P4G2Go, to quantitatively assess its performance. Finally, we reason about its security and privacy properties, proving that P4G2Go achieves to fulfill the relevant objectives.

D-allulose is an epimer of D-fructose at the C-3 position. With similar sweetness to sucrose and a low-calorie profile, D-allulose has been considered a promising functional sweetener. D-psicose 3-epimerase (DPEase, EC 5.1.3.30) catalyses the synthesis of D-allulose from D-fructose. Immobilised enzymes are becoming increasingly popular because of their better stability and reusability. However, immobilised DPEase generally exhibits less activity or poses difficulty in separation. This study aimed to obtain immobilised DPEase with high catalytic activity, stability, and ease of separation from the reaction solution. In this study, DPEase was immobilised on an amino-epoxide support, ReliZyme HFA403/M (HFA), in four steps (ion exchange, covalent binding, glutaraldehyde crosslinking, and blocking). Glycine-blocked (four-step immobilisation) and unblocked (three-step immobilisation) immobilised DPEase exhibited activities of 103.5 and 138.8 U/g support, respectively, but contained equal amounts of protein. After incubation at 60 °C for 2 h, the residual activity of free enzyme decreased to 12.5%, but the activities of unblocked and blocked DPEase remained at 40.9% and 52.3%, respectively. Immobilisation also altered the substrate specificity of the enzyme, catalysing L-sorbose to L-tagatose and D-tagatose to D-sorbose. Overall, the immobilised DPEase with intense multipoint attachment, especially glycine-blocked DPEase, showed better properties than the free form, providing a superior potential for D-allulose biosynthesis.

The technological properties of raw fish are influenced by the changes in protein structure under heating, which determines the texture and quality of the product. The aim of the study was to examine the protein denaturation temperature and the rheological properties of Baltic herring muscle tissue. The thermal properties were determined by the differential scanning calorimetry (DSC) method and the rheological properties were determined using dynamic oscillatory tests. DSC showed four peaks associated with denaturing transformations of myosin (39.59 °C), sarcoplasm (51.67 °C), connective tissue (63.16 °C), and actin (74.40 °C). Analysis showed that not all transformations occurred according to the same kinetic model. The first two and the last peak are described by 1st order kinetics, while peak 3 is described by 2nd order kinetics. Correlating the changes in fish tissue structure during heating with the rheological characteristics provides more information. The obtained kinetics models correlated very strongly with the results of model testing. Rheological changes of the G’ and G” values had two inflexion points and demonstrate a high degree of convergence with the DSC changes of herring muscle tissue from 20 to 85 °C.

The world’s oceans are one of the most valuable sources of biodiversity and resources on the planet, although there are areas where the marine ecosystem is threatened by human activities. Marine protected areas (MPAs) are distinctive spaces protected by law due to their unique characteristics, such as being the habitat of endangered marine species. Even with this protection, there are still illegal activities such as poaching or anchoring that threaten the survival of different marine species. In this context, we propose an autonomous surface vehicle (ASV) model system for the surveillance of marine areas by detecting and recognizing vessels through artificial intelligence (AI)-based image recognition services, in search of those carrying out illegal activities. Cloud and edge AI computing technologies were used for computer vision. These technologies have proven to be accurate and reliable in detecting shapes and objects for which they have been trained. Azure edge and cloud vision services offer the best option in terms of accuracy for this task. Due to the lack of 4G and 5G coverage in offshore marine environments, it is necessary to use radio links with a coastal base station to ensure communications, which may result in a high response time due to the high latency involved. The analysis of on-board images may not be sufficiently accurate, therefore, we proposed a smart algorithm for autonomy optimization by selecting the proper AI technology according to the current scenario (SAAO) capable of selecting the best AI source for the current scenario in real time, according to the required recognition accuracy or low latency. The SAAO optimizes the execution, efficiency, risk reduction, and results of each stage of the surveillance mission, taking appropriate decisions by selecting either cloud or edge vision models without human intervention.

Funding Information: This work was supported in part by the Academy of Finland under Project No. 311760 and Project No. 319003. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. Copyright: Copyright 2021 Elsevier B.V., All rights reserved. With the popularization of Internet-of-things (IoT) and wireless communication systems, a diverse set of applications in smart cities are emerging to improve the city-life. These applications usually require a large coverage area and minimal operation and maintenance cost. To this end, the recently emerging backscatter communication (BC) is gaining interest in both industry and academia as a new communication paradigm that provides high energy efficient communications that may even work in a battery-less mode and, thus, it is well suited for smart city applications. However, the coverage of BC in urban area deployments is not available, and the feasibility of its utilization for smart city applications is not known. In this article, we present a comprehensive coverage study of a practical cellular carrier-based BC system for indoor and outdoor scenarios in a downtown area of a Helsinki city. In particular, we evaluate the coverage outage performance of different low-power and wide area technologies, i.e., long range (LoRa) backscatter, arrow band-Internet of Things (NB-IoT), and Bluetooth low energy (BLE) based BC at different frequencies of operation. To do so, we carry out a comprehensive campaign of simulations while using a sophisticated three-dimensional (3D) ray tracing (RT) tool, ITU outdoor model, and 3rd generation partnership project (3GPP) indoor hotspot model. This study also covers the energy harvesting aspects of backscatter device, and it highlights the importance of future backscatter devices with high energy harvesting efficiency. The simulation results and discussion provided in this article will be helpful in understanding the coverage aspects of practical backscatter communication system in a smart city environment.

A modified QuEChERS (quick, easy, cheap, effective, rugged, and safe) extraction was validated for the extraction of seven coumarin anticoagulant rodenticides (ARs) and 36 pharmaceutical active compounds (PhACs) residues in soil samples using liquid chromatography tandem mass spectrometry (LC-MS/MS). The aim of this work was to develop a method for the monitoring of these compounds in agricultural lands as well as in forensic applications for the determination of ARs poisoning. As far as is known, this is the first time that a QuEChERS-based method is used for the extraction of ARs in soil, as well as on such a quantity of PhACs. A matrix effect study was carried out on samples of soil devoted to agriculture in the midland area of the Canary Islands (clay loam type). It was in house validated (accuracy, precision, and linearity) at seven spiked levels between 0.5 and 50 ng g−1. The limits of quantification (LOQ) ranged between 0.5 and 50.0 ng g−1 and the limits of detection (LOD) ranged from 0.024 to 6.25 ng g−1. The method was then successfully used for both the determination of the target analytes in the soils from the agricultural plots that had been irrigated with regenerated water, and in the soil collected from underneath wild bird carcasses (which had been the subject of forensic investigation).

Halogen substituents often lead to a profound effect on the biological activity of organic compounds. Flavin-dependent halogenases offer the possibility of regioselective halogenation at non-activated carbon atoms, while employing only halide salts and molecular oxygen. However, low enzyme activity, instability, and narrow substrate scope compromise the use of enzymatic halogenation as an economical and environmentally friendly process. To overcome these drawbacks, it is of tremendous interest to identify novel halogenases with high enzymatic activity and novel substrate scopes. Previously, Neubauer et al. developed a new hidden Markov model (pHMM) based on the PFAM tryptophan halogenase model, and identified 254 complete and partial putative flavin-dependent halogenase genes in eleven metagenomic data sets. In the present study, the pHMM was used to screen the bacterial associates of the Botryococcus braunii consortia (PRJEB21978), leading to the identification of several putative, flavin-dependent halogenase genes. Two of these new halogenase genes were found in one gene cluster of the Botryococcus braunii symbiont Sphingomonas sp. In vitro activity tests revealed that both heterologously expressed enzymes are active flavin-dependent halogenases able to halogenate indole and indole derivatives, as well as phenol derivatives, while preferring bromination over chlorination. Interestingly, SpH1 catalyses only monohalogenation, while SpH2 can catalyse both mono- and dihalogenation for some substrates.

The influence of cultural and environmental factors on the sensory and chemical profiles of wines has been the subject of research investigation for many years, and an examination of these relationships can help determine whether wine regional trends exist. The present study investigated the chemical and sensory factors that drive regional differences in Pennsylvania Grüner Veltliner wines through a controlled winemaking study across two vintages in 2018 and 2019. Descriptive analysis was used to identify key sensory attributes of Pennsylvania Grüner Veltliner. Intensities of these attributes were evaluated in wines vinified under identical conditions from grapes harvested across nine Pennsylvania vineyards. Chemical profiles of finished wines were examined through volatile, phenolic, and color analyses. Significant sensory differences were found between wine regions, with some trends consistent across both vintages, however, regionality based on compositional analyses was less clear. As the first study to examine Pennsylvania Grüner Veltliner wines sensorially, results revealed sensory characteristics that can be useful for wineries and their tasting room staff in marketing these lesser-known white wines to wine consumers as the variety grows in popularity in the state.

Micellar casein and casein monomers in milk serum are in a dynamic equilibrium. At temperature below 15–20 °C a considerable amount of casein monomers, β-casein in particular, is released from the casein micelle into the aqueous serum phase. This study investigates the effects of added calcium and related variations of pH on this peculiar equilibrium in order to minimize the amount of caseins in the serum and to better understand the casein permeation during microfiltration. The pH was varied in the range of 6.3 to 7.3 and the content of calcium was increased up to 7.5 mM by adding CaCl2. Upon equilibration, the milk was separated by ultracentrifugation and the amounts of protein in the supernatant were analyzed. It was shown that the addition of low amounts of calcium shifts the equilibrium towards the micellar casein phase and can, thus, lower the serum casein content induced at low temperatures. Relative to that, the adjustment of pH separately from the CaCl2 addition had a minor effect on casein concentration and composition in the serum.

A vital and challenging task in computer vision is 3D Object Classification and Retrieval, with many practical applications such as an intelligent robot, autonomous driving, multimedia contents processing and retrieval, and augmented/mixed reality. Various deep learning methods were introduced for solving classification and retrieval problems of 3D objects. Almost all view-based methods use many views to handle spatial loss, although they perform the best among current techniques such as View-based, Voxelization, and Point Cloud methods. Many views make network structure more complicated due to the parallel Convolutional Neural Network (CNN). We propose a novel method that combines a Global Point Signature Plus with a Deep Wide Residual Network, namely GPSP-DWRN, in this paper. Global Point Signature Plus (GPSPlus) is a novel descriptor because it can capture more shape information of the 3D object for a single view. First, an original 3D model was converted into a colored one by applying GPSPlus. Then, a 32 × 32 × 3 matrix stored the obtained 2D projection of this color 3D model. This matrix was the input data of a Deep Residual Network, which used a single CNN structure. We evaluated the GPSP-DWRN for a retrieval task using the Shapnetcore55 dataset, while using two well-known datasets—ModelNet10 and ModelNet40 for a classification task. Based on our experimental results, our framework performed better than the state-of-the-art methods.

Ground-penetrating radar (GPR) has been used for asphalt concrete (AC) pavement density prediction for the past two decades. Recently, it has been considered as a method for pavement quality control and quality assurance. A numerical method to estimate asphalt pavement specific gravity from its dielectric properties was developed and validated. A three-phase numerical model considering aggregate, binder, and air void components was developed using an AC mixture generation algorithm. A take-and-add algorithm was used to generate the uneven air-void distribution in the three-phase model. The proposed three-phase model is capable of correlating pavement density and bulk and component dielectric properties. The model was validated using field data. Two methods were used to calculate the dielectric constant of the AC mixture, including reflection amplitude and two-way travel time methods. These were simulated and compared when vertical and longitudinal heterogeneity existed within the AC pavement layers. Results indicate that the reflection amplitude method is more sensitive to surface thin layers than the two-way travel time methods. Effect of air-void content, asphalt content, aggregate gradation, and aggregate dielectric constants on the GPR measurements were studied using the numerical model.

In this paper, Nuclear Magnetic Resonance spectroscopy (NMR)-based metabolomics were applied for the discrimination of ale and lager craft American beers. A modified pulse sequence that allows the efficient suppression of the water and ethanol peaks was used to achieve high-quality spectra with minimal sample preparation. The initial chemometrics analysis generated models of low predictive power, indicating the high variability in the groups. Due to this variability, we tested the effect of various data pretreatment and chemometrics approaches to improve the model’s performance. Spectral alignment was found to improve the classification significantly, while the type of normalization also played an important role. NMR combined with statistical and machine-learning techniques such as orthogonal projection to latent structures discriminant analysis (OPLS-DA) and random forest was able to discriminate between ale and lager beers, thus providing an important tool for the quality control and analysis of these products.

Precision irrigation represents those strategies aiming to feed the plant needs following the soil’s spatial and temporal characteristics. Such a differential irrigation requires a different approach and equipment with regard to conventional irrigation to reduce the environmental impact and the resources use while maximizing the production and thus profitability. This study described the development of an open source soil moisture LoRa (long-range) device and analysis of the data collected and updated directly in the field (i.e., weather station and ground sensor). The work produced adaptive supervised predictive models to optimize the management of agricultural precision irrigation practices and for an effective calibration of other agronomic interventions. These approaches are defined as adaptive because they self-learn with the acquisition of new data, updating the on-the-go model over time. The location chosen for the experimental setup is a cultivated area in the municipality of Tenna (Trentino, Alto Adige region, Italy), and the experiment was conducted on two different apple varieties during summer 2019. The adaptative partial least squares time-lag time-series modeling, in operative field conditions, was a posteriori applied in the consortium for 78 days during the dry season, producing total savings of 255 mm of irrigated water and 44,000 kW of electricity, equal to 10.82%.

Polygonum aviculare and Persicaria amphibia (subfam. Polygonoideae) are used in traditional cuisines and folk medicine in various cultures. Previous studies indicated that phytochemicals obtained from Polygonoideae plants could sensitize chemoresistant cancer cells and enhance the efficacy of some cytostatics. Here, the cytotoxic properties of chemically characterized ethanol extracts obtained from P. aviculare and P. amphibia, individually and in combination with doxorubicin (D), were determined against hepatocarcinoma HepG2 cells. Phenolic composition, cell viability, cell cycle, apoptosis, and the expression of Keap1 and Nrf2 were examined by following methods: LC-MS/MS, LC-DAD-MS, MTT, flow cytometry, and qRT-PCR. Extracts were rich in dietary polyphenolics. Synergistic cytotoxicity was detected for extracts combined with D. The observed synergisms are linked to the interference with apoptosis, cell cycle, and expression of Keap1-Nrf2 genes involved in cytoprotection. The combined approach of extracts and D could emerge as a potential pathway of chemotherapy improvement.

In modern power systems, power transformers are considered vital components that can ensure the grid’s continuous operation. In this regard, studying the breakdown in the transformer becomes necessary, especially its insulating system. Hence, in this study, Box–Behnken design (BBD) was used to introduce a prediction model of the breakdown voltage (VBD) for the transformer insulating oil in the presence of different barrier effects for point/plane gap arrangement with alternating current (AC) voltage. Interestingly, the BBD reduces the required number of experiments and their costs to examine the barrier parameter effect on the existing insulating oil VBD. The investigated variables were the barrier location in the gap space (a/d)%, the relative permittivity of the barrier materials (εr), the hole radius in the barrier (hr), the barrier thickness (th), and the barrier inclined angle (θ). Then, only 46 experiment runs are required to build the BBD model for the five barrier variables. The BBD prediction model was verified based on the statistical study and some other experiment runs. Results explained the influence of the inclined angle of the barrier and its thickness on the VBD. The obtained results indicated that the designed BBD model provides less than a 5% residual percentage between the measured and predicted VBD. The findings illustrated the high accuracy and robustness of the proposed insulating oil breakdown voltage predictive model linked with diverse barrier effects.

This study aimed to evaluate the ratio of adherence of lactic acid bacteria (LAB) to porous fine ceramics in order to develop a novel LAB-rich pickle container for the production of functional fermented vegetables. Some LAB were isolated from the salted rice bran used for pickling (Nukadoko in Japanese). These isolates were classified in Lactobacillus namurensis by phylogenetic analysis. Some pottery-shard (PS) samples were prepared by varying the mixing rate of polyacetal (POM) resin to clay (0–30% (v/v)) and the burning temperature (1000 °C or 1100 °C). A test of the adherence of strain GYP-74 to the PSs was performed. The results showed that the adherence rate was significantly higher in the PSs burned at 1100 °C as compared with those burned at 1000 °C. A pore distribution analysis showed that pore sizes of less than a few µm and pore sizes in the range of a few µm to a few hundred µm were mainly distributed in the PSs without and with POM, respectively. X-ray diffraction analysis showed that both PSs with and without POM contained quartz and hematite. The PSs burned at 1000 °C and 1100 °C specifically contained microcline and mullite, respectively. This study revealed the basal information regarding what makes PSs adequate for LAB adhesion.

In this study, the microbiological and sensory quality of cultivated mushrooms (Pleurotus ostreatus and eryngii and Lentinula edodes) available at the Austrian retail level were determined. Aerobic mesophilic bacteria (AMC), Enterobacteriaceae (EB), Pseudomonadaceae (PS), lactic acid bacteria (LAB), yeast, moulds and presumptive Bacillus cereus were enumerated at the day of purchase and after storage at 4 °C for 7 or 12 days. Additionally, the presence of Salmonella spp. and Listeria monocytogenes was investigated. Isolates of presumptive spoilage bacteria were confirmed by partial 16S rRNA sequencing. At the day of purchase, 71.2% of the samples were of high microbiological quality and grouped into the low contamination category (AMC &lt, 5.0 log cfu/g), while the sensory quality of 67.1% was categorized as “very good or good”. After storage, the number of samples with high microbial quality was 46.6%, and only 37.0% of the samples scored as “very good or good”. The most abundant species across all mushroom samples were the Pseudomonas fluorescens species complex (58.4%) and the potential mushroom pathogen Ewingella americana (28.3%). All mushroom samples tested negative for Salmonella spp., L. monocytogenes and Bacillus cereus. The microbiological and sensory quality of the analysed mushrooms at the day of purchase and after storage was considered to be good overall. Longer transport distances were found to have a significant influence on the microbiological and sensory quality.

Radio frequency (RF) technology is considered as a rapid heating method. Lipase in rice bran could highly accelerate lipid oxidation. The objectives of this study were to establish the radio frequency heating conditions for lipase inactivation and to evaluate the stability and antioxidant capacity. The results showed that the suitable electrode gap for a 1 kg sample load was 6 cm, and it only took 2 min to heat rice bran from 25 °C to 100 °C. Besides, there were no significant differences in the total phenolic content, flavonoid content and color between the untreated and RF-treated group, and the DPPH free radical scavenging activity of the RF treatment reached 84.8%. The acid value, free fatty acid content and peroxide value of the RF-treated rice bran met the quality standard after 8 weeks of storage at 4, 25 and 37 °C. In summary, this study provides valuable information about the RF heating procedure, and shows the great potential of RF technology for stabilizing rice bran efficiently.

Extracellular vesicles (EVs) have attracted significant attention as impactful diagnostic biomarkers, since their properties are closely related to specific clinical conditions. However, designing experiments that involve EVs phenotyping is usually highly challenging and time-consuming, due to laborious optimization steps that require very long or even overnight incubation durations. In this work, we demonstrate label-free, real-time detection and phenotyping of extracellular vesicles binding to a multiplexed surface. With the ability of label-free kinetic binding measurements using the Interferometric Reflectance Imaging Sensor (IRIS) in a microfluidic chamber, we successfully optimize the capture reaction by tuning various assay conditions (incubation time, flow conditions, surface probe density and specificity). A single (less than 1 hour) experiment allows for characterization of binding affinities of the EVs to multiplexed probes. We demonstrate kinetic characterization of 18 different probe conditions, namely three different antobodies, each spotted at six different concentrations, simultaneously. The affinity characterization is then analyzed through a model which considers the complexity of multivalent binding of large structures to a carpet of probes, and therefore introduces a combination of fast and slow association and dissociation parameters. Additionally, our results confirm higher affinity of EVs to aCD81 with respect to aCD9 and aCD63. Single-vesicle imaging measurements corroborate our findings, as well as confirming the EVs nature of the captured particles through fluorescence staining of the EVs membrane and cargo.

Impedance pneumography has been suggested as an ambulatory technique for the monitoring of respiratory diseases. However, its ambulatory nature makes the recordings more prone to noise sources. It is important that such noisy segments are identified and removed, since they could have a huge impact on the performance of data-driven decision support tools. In this study, we investigated the added value of machine learning algorithms to separate clean from noisy bio-impedance signals. We compared three approaches: a heuristic algorithm, a feature-based classification model (SVM) and a convolutional neural network (CNN). The dataset consists of 47 chronic obstructive pulmonary disease patients who performed an inspiratory threshold loading protocol. During this protocol, their respiration was recorded with a bio-impedance device and a spirometer, which served as a gold standard. Four annotators scored the signals for the presence of artefacts, based on the reference signal. We have shown that the accuracy of both machine learning approaches (SVM: 87.77 ± 2.64% and CNN: 87.20 ± 2.78%) is significantly higher, compared to the heuristic approach (84.69 ± 2.32%). Moreover, no significant differences could be observed between the two machine learning approaches. The feature-based and neural network model obtained a respective AUC of 92.77±2.95% and 92.51±1.74%. These findings show that a data-driven approach could be beneficial for the task of artefact detection in respiratory thoracic bio-impedance signals.

We studied the effects of a hydrophobized reversed-phase feed adsorbent in the form of a polyoctylated polysilicate hydrogel (POPSH) on productivity indicators, metabolic adaptation, and on the level of polychlorinated biphenyls (PCBs) in the blood of growing Holsteinized black-and-white heifers during the transition period. Two groups of two-month-old heifers of 20 head each were used. The experimental group received POPSH in addition to the main diet. The use of the adsorbent led to an increase in daily weight gain by 19.9% and to a decrease in the total concentration of PCB congeners found in whole blood by 40%. The greatest decrease in concentration (35–52%) was observed for tetra-, penta- and hexachlorobiphenyls. These results demonstrate the possibilities of effective protection of calves from lipophilic toxins of feed and their active decontamination.

Understanding aggregation in food protein systems is essential to control processes ranging from the stabilization of colloidal dispersions to the formation of macroscopic gels. Patatin rich potato protein isolates (PPI) have promising techno-functionality as alternatives to established proteins from egg white or milk. In this work, the influence of pH and temperature on the kinetics of PPI denaturation and aggregation was investigated as an option for targeted functionalization. At a slightly acidic pH, rates of denaturation and aggregation of the globular patatin in PPI were fast. These aggregates were shown to possess a low amount of disulfide bonds and a high amount of exposed hydrophobic amino acids (S0). Gradually increasing the pH slowed down the rate of denaturation and aggregation and alkaline pH levels led to an increased formation of disulfide bonds within these aggregates, whereas S0 was reduced. Aggregation below denaturation temperature (Td) favored aggregation driven by disulfide bridge formation. Aggregation above Td led to fast unfolding, and initial aggregation was less determined by disulfide bridge formation. Inter-molecular disulfide formation occurred during extended heating times. Blocking different protein interactions revealed that the formation of disulfide bond linked aggregation is preceded by the formation of non-covalent bonds. Overall, the results help to control the kinetics, morphology, and interactions of potato protein aggregation for potential applications in food systems.

In recent years, remote sensing images has become one of the most popular directions in image processing. A small feature gap exists between satellite and natural images. Therefore, deep learning algorithms could be applied to recognize remote sensing images. We propose an improved Mask R-CNN model, called SCMask R-CNN, to enhance the detection effect in the high-resolution remote sensing images which contain the dense targets and complex background. Our model can perform object recognition and segmentation in parallel. This model uses a modified SC-conv based on the ResNet101 backbone network to obtain more discriminative feature information and adds a set of dilated convolutions with a specific size to improve the instance segmentation effect. We construct WFA-1400 based on the DOTA dataset because of the shortage of remote sensing mask datasets. We compare the improved algorithm with other state-of-the-art algorithms. The object detection AP50 and AP increased by 1–2% and 1%, respectively, objectively proving the effectiveness and the feasibility of the improved model.

In this paper, an integrated thermoelectric (TE) and photovoltaic (PV) hybrid energy harvesting system (HEHS) is proposed for self-powered internet of thing (IoT)-enabled wireless sensor networks (WSNs). The proposed system can run at a minimum of 0.8 V input voltage under indoor light illumination of at least 50 lux and a minimum temperature difference, ∆T = 5 °C. At the lowest illumination and temperature difference, the device can deliver 0.14 W of power. At the highest illumination of 200 lux and ∆T = 13 °C, the device can deliver 2.13 W. The developed HEHS can charge a 0.47 F, 5.5 V supercapacitor (SC) up to 4.12 V at the combined input voltage of 3.2 V within 17 s. In the absence of any energy sources, the designed device can back up the complete system for 92 s. The sensors can successfully send 39 data string to the webserver within this time at a two-second data transmission interval. A message queuing telemetry transport (MQTT) based IoT framework with a customised smartphone application ‘MQTT dashboard’ is developed and integrated with an ESP32 Wi-Fi module to transmit, store, and monitor the sensors data over time. This research, therefore, opens up new prospects for self-powered autonomous IoT sensor systems under fluctuating environments and energy harvesting regimes, however, utilising available atmospheric light and thermal energy.

This study assessed the acute and chronic risk of pesticides, singly and as mixtures, for fish using comprehensive chemical data of four monitoring studies conducted in small- and medium-sized streams of Switzerland between 2012 and 2018. Pesticides were ranked based on single substance risk quotients and relative contribution to mixture risk. Concentrations of the pyrethroid insecticides, λ-cyhalothrin, cypermethrin and deltamethrin, and the fungicides, carbendazim and fenpropimorph, posed acute or chronic single substance risks. Risk quotients of eighteen additional pesticides were equal or greater than 0.1, and thirteen of those contributed ≥30% to mixture risk. Relatively few substances dominated the mixture risk in most water samples, with chronic and acute maximum cumulative ratios never exceeding 5 and 7, respectively. A literature review of toxicity data showed that concentrations of several pesticides detected in Swiss streams were sufficient to cause direct sublethal effects on fish in laboratory studies. Based on the results of our study, we conclude that pesticides detected in Swiss streams, especially pyrethroid insecticides, fungicides and pesticide mixtures, pose a risk to fish health and can cause direct sublethal effects at environmental concentrations. Sensitive life stages of species with highly specialized life history traits may be particularly vulnerable, however, the lack of toxicity data for non-model species currently prevents a conclusive assessment across species.

This research focuses on the problem of scheduling a set of jobs on unrelated parallel machines subject to release dates, sequence-dependent setup times, and additional renewable resource constraints. The objective is to minimize the maximum completion time (makespan). To optimize the problem, a modified harmony search (MHS) algorithm was proposed. The parameters of MHS are regulated using full factorial analysis. The MHS algorithm is examined, evaluated, and compared to the best methods known in the literature. Four algorithms were represented from similar works in the literature. A benchmark instance has been established to test the sensitivity and behavior of the problem parameters of the different algorithms. The computational results of the MHS algorithm were compared with those of other metaheuristics. The competitive performance of the developed algorithm is verified, and it was shown to provide a 42% better solution than the others.

Nowadays the use of remote monitoring sensors is a standard practice in landslide characterization and monitoring. In the last decades, technologies such as LiDAR, terrestrial and satellite SAR interferometry (InSAR) and photogrammetry demonstrated a great potential for rock slope assessment while limited studies and applications are still available for ArcSAR Interferometry, Gigapixel imaging and Acoustic sensing. Taking advantage of the facilities located at the Poggio Baldi Landslide Natural Laboratory, an intensive monitoring campaign was carried out on May 2019 using simultaneously the HYDRA-G ArcSAR for radar monitoring, the Gigapan robotic system equipped with a DSLR camera for photo-monitoring purposes and the DUO Smart Noise Monitor for acoustic measurements. The aim of this study was to evaluate the potential of each monitoring sensor and to investigate the ongoing gravitational processes at the Poggio Baldi landslide. Analysis of multi-temporal Gigapixel-images revealed the occurrence of 84 failures of various sizes between 14–17 May 2019. This allowed us to understand the short-term evolution of the rock cliff that is characterized by several impulsive rockfall events and continuous debris production. Radar displacement maps revealed a constant movement of the debris talus at the toe of the main rock scarp, while acoustic records proved the capability of this technique to identify rockfall events as well as their spectral content in a narrow range of frequencies between 200 Hz to 1000 Hz. This work demonstrates the great potential of the combined use of a variety of remote sensors to achieve high spatial and temporal resolution data in the field of landslide characterization and monitoring.

Firefighter’s interventions under dense smoke and flames are hazardous and ideally need an efficient in-advance geo-located actuation plan. The existing communication and sensing technologies should be customized, optimized, and integrated to better know the conditions (flame locations, air condition) before and during the rescue team’s interventions. In this paper, we propose a firefighter intervention architecture, which consists of several sensing devices (flame detectors, carbon dioxide air content) a navigation platform (an autonomous ground wheeled robot), and a communication/localization network (BLE IoT network) that can be used before and during an intervention in rescue or fire extinguishing missions even for indoor or confined spaces. The paper’s key novelty presents our integrated solution, giving some key implementation details and an intensive experimentation campaign in two real firefighter scenarios with real controlled fires. Results carried out in these real indoor scenarios are presented to demonstrate the feasibility of the system. A fire detection system is proposed to improve fire focus in real time and moving in confined spaces with no visibility and physical references. The results obtained in the experimentation show the proposal’s effectiveness in locating the fire focus’s position and orientation reducing time and risk exposure. This kind of location-aware fire integrated systems would significantly impact the speed and security of first responder interventions.