Background. Shoulder in CrossFit should have a balance between mobility and stability. Glenohumeral internal rotation deficit and posterior shoulder stiffness are risk factors for overhead shoulder injury. Objective. To determine the effectiveness of instrument assisted soft tissue mobilization and horizontal adduction stretch in CrossFit practitioners’ shoulders. Methods: Twenty-one regular CrossFitters were allocated to experimental (stretching with isometric contraction and instrument assisted soft tissue mobilization) or control group (instrument assisted soft tissue mobilization). Each session lasted 5 minutes, 2 days a week, over a period of 4 weeks. Shoulder internal rotation and horizontal adduction (digital inclinometer), and posterior shoulder stretch perception (Park scale) were evaluated. Shapiro-Wilk test was used to analyze the distribution of the sample. Parametric student's t-test was used to obtain the intragroup differences. The inter- and intra-rater differences were calculated using a repeated measures ANOVA. Results. Changes were found in the experimental group following intervention (p < 0.05), and when comparing baseline and follow-up assessments (p < .05) in all variables. Significant differences were found in the control group following intervention (p < 0.05), in right horizontal adduction and left internal rotation. When comparing perception of internal rotation and horizontal adduction in both groups in the three assessments significant differences were found. Conclusions. Instrument assisted soft tissue mobilization can improve shoulder horizontal adduction and internal rotation. An instrument-assisted soft tissue mobilization technique yields the same results alone as those achieved in combination with post-isometric stretch with shoulder adduction.

Purpose: to assess the effect of photobiomodulation (PBM) on human gingival fibroblast proliferation. Methods: The study was conducted using the primary cell cultures of human fibroblasts collected from systemically healthy donors. Three different laser types: Nd:YAG (1064nm), infrared diode laser (980nm) and prototype led laser emitting 405, 450 and 635nm were used to irradiate fibroblasts. Thanks to the patented structure of that laser, it was possible to irradiate fibroblasts with a beam combining two or three wavelengths. The energy density was 3 J/cm², 25 J/cm², 64 J/cm². The viability and proliferation of cells were determined using the MTT test conducted 24, 48 and 72 hours after laser irradiation. Results: The highest percentage of mitochondrial activity (MA=122.1%) was observed in the group irradiated with the 635nm laser, with an energy density of 64 J/cm² after 48 hours. The lowest percentage of MA (94.0%) was observed in the group simultaneously irradiated with three wavelengths (405 + 450 + 635 nm). The use of the 405nm laser at 25 J/cm² gave similar results to the 635 nm laser. Conclusions: The application of the 635nm and 405nm irradiation caused a statistically significant increase in the proliferation of gingival fibroblasts.

Drops are the most important and most common energy dissipator in irrigation networks and erodible canals and consequently, their performance must be well understood. This study was designed to evaluate the capability of Artificial Intelligence (AI) methods including ANN, ANFIS, GRNN, SVM, GP, MLR, and LR to predict the relative energy dissipation (∆E/E0) in vertical drops equipped with a horizontal screen. For this study, 108 experiments were carried out to investigate energy dissipation with variable discharge, varying drop height, and porosity of the horizontal screens. Parameters yc/h, yd/yc, and p are considered as input variables and ∆E/E0 is the output variable. The efficiency of models was compared using Taylor's diagram, Box Plot of the applied error distribution, correlation coefficient (CC), mean absolute error (MAE) and root-mean-square error (RMSE). Results indicate that the performance of the ANFIS_gbellmf based model with CC value of 0.9953, RMSE value of 0.0069 and MAE value of 0.0042 was superior to other applied models. Also, the linear regression model with CC=0.9933, RMSE=0.0083, and MAE= 0.0067performs better than the multiple linear regression model in this study. Results of a sensitivity study suggest that yc/h is the most effective parameter for predicting ∆E/E0.

An attractive option for realizing applications in radiation environments is to employ All-Programmable System-on-Chips (APSoCs) thanks to their high-performance computing and power efficiency merits. Despite APSoC's advantages, like any other electronic device, they are prone to radiation effects. Processors found in APSoCs must, therefore, be adequately hardened against ionizing-radiation to become a viable alternative for harsh environments. This paper proposes a novel triple-core lockstep (TCLS) approach to secure the Xilinx Zynq-7000 APSoC dual-core ARM Cortex-A9 processor against radiation-induced soft errors by coupling it with a MicroBlaze TMR subsystem in Zynq's programmable logic (PL) layer. The proposed strategy uses software-level checkpointing principles along with roll-back and roll-forward mechanisms (i.e. software redundancy), and hardware-level processor replication as well as checker circuits (i.e. hardware redundancy). Results of fault injection experiments show that the proposed solution achieved high soft error security by mitigating about 99% of bit-flips injected into both ARM cores' register data.

This paper presents a comparative analysis of the ZVZCS soft switching technique with the ZVS and the ZCS counterpart. The generalization of the voltage-current crossover or the energy loss factor obtained from simulation of the prototype converter shows that the ZVZCS significantly reduces the loss and helps to improve the efficiency of the converter as compared to the ZVS or the ZCS. On the other hand, it is also found that the soft switching range of operation of the ZVS and the ZCS are largely affected by the maximum switch voltage and switch current respectively. In contrary, these factors have a negligible effect on the ZVZCS operation which results in an extended range of soft switching operation. Additionally, a detailed LTPICE simulation is performed for selected ZVS, ZCS and ZVSCS topologies from the recent literature and the switching losses in the main switches of the converters are measured. It is observed that the energy losses in the ZVZCS mode are reduced on average by approximately 26 % at turn on and 20 % at the turn off as compared to the ZVS and the ZCS. Besides, the low standard deviation in this mode confirms a stable low loss profile which renders extended soft switching range. An experimental test is also conducted by building the prototype converter to verify the simulation results. It is found that the switching losses are minimum while the converter is operated in the ZVZCS mode. Besides, the efficiency drop remains consistently low as compared to the ZVS and the ZCS in the whole operating range. Resultantly, the simulation and the experimental results are both found to be consistent.

Background: PRAME, NY-ESO-1 and SSX2 are cancer testis antigens (CTAs), which in normal tissues are expressed in testicular germ cells with re-expression in numerous cancer types. Their ability to elicit humoral and cellular immune responses have rendered them promising targets for cancer immunotherapy, but they have never been studied in a large and well-characterized cohort soft tissue sarcomas (STS). Methods: On protein level, we examined PRAME, NY-ESO-1 and SSX2 expression in tumour tissues of 249 STS using immunohistochemistry. We correlated expression levels with clinicopathological parameters including Tumour-infiltrating lymphocyte (TIL) counts, grading and long- term survival. Results: Expression of PRAME, NY-ESO-1 and SSX2 was observed in 25 (10%), 19 (8%), and 11 (4%) of 249 specimens with distinct patterns for histo subtypes. Expression of PRAME was associated with shorter patient survival (p=0.005) and higher grade (G2 vs G3, p=0.001) while NY-ESO-1 expression was correlated with more favourable survival (p=0.037) and low grade (G2 vs G3, p=0.029). Both PRAME and NY-ESO-1 expression was more frequent in STS with low TILs counts. Conclusions: CTAs PRAME, NY-ESO-1 and SSX2 show distinct expression patterns in different STS subtypes. These results may guide future immunotherapeutic approaches in STS.

Biofuels and biochemicals play significant roles in the transition towards a fossil-free society. However, large-scale biorefineries are not yet cost-competitive with their fossil-fuel counterparts, and it is important to identify biorefinery concepts with high economic performance. For evaluating early-stage biorefinery concepts, one needs to consider not only the technical performance and process costs but also the economic performance of the full supply chain and the impacts on feedstock and product markets. This article presents and demonstrates a conceptual interdisciplinary framework that can constitute the basis for evaluations of the full supply-chain performance of biorefinery concepts. This framework considers the competition for biomass across sectors, assumes exogenous end-use product demand, and incorporates various geographical and technical constraints. The framework is demonstrated empirically through a case study of a sawmill-integrated biorefinery producing liquefied biomethane from forestry and forest industry residues. The case study results illustrate that acknowledging biomass market effects in the supply chain evaluation implies changes in both biomass prices and the allocation of biomass across sectors. The proposed framework should facilitate the identification of biorefinery concepts with a high economic performance which are robust to feedstock price changes caused by the increase in biomass demand.

Soft drinks exist in various forms and brands and are marketed by different brewery industries across the country. Nigeria been an underdeveloped country were aforesaid health problems are common, people tends to consume them lots believing it gives more energy. This study was therefore set to assess the sugar content from different soft drinks sold in Nigeria. Ten (10) soft drinks registered and licensed in Nigeria by their respective companies were purchased from a supermarket in Ihumudumu environment of Ekpoma, Nigeria. The sugar contents were assessed using standard methods. Result showed significant differences (p<0.05) in ranges of 0.36g/100ml (Limca) to 3.88g/100ml (Teem Bitter Lemon). Conclusively, the sugar contents were all below recommended value and it can be consumed by those who need low sugar in the body.

Over the last decade, the field of Human Attribute Recognition (HAR) has dramatically changed, mainly due to the improvements brought by deep learning solutions. This survey reviews the progress obtained in HAR, considering the transition from the traditional hand-crafted to deep-learning approaches. The most relevant works on the field are analyzed concerning the advances proposed to address the HAR's typical challenges. Furthermore, we outline the applications and typical evaluation metrics used in the HAR context. Finally, we provide a comprehensive review of the publicly available datasets for the development and evaluation of novel HAR approaches.

This paper introduces a closed-loop control strategy for maintaining consistency of liquid temperature in commercial Drop on Demand (DoD) inkjet printing. No additional sensors or additional actuators are installed in the printhead while achieving the consistency in liquid temperature. To this end, this paper presents a novel in situ sensing-actuation policy at every individual liquid-nozzle, where the jetting mechanism has three distinct roles. It is used for jetting liquid droplet onto the print media based on the print-job. It is used as a soft sensor to estimate the real-time liquid temperature of the jetting nozzle. While not jetting liquid, it is used as a heating actuator to minimize the gradient of liquid temperature among nozzles. The soft sensing based in situ controller is implemented in an experimentally validated digital twin that models the thermo-fluidic processes of the printhead. The digital twin is scalable and flexible to incorporate an arbitrary number of liquid-nozzles, making the control strategy applicable for future designs of the printhead.

This paper proposes a model based on gene expression programming for predicting discharge coefficient of triangular labyrinth weirs. The parameters influencing discharge coefficient prediction were first examined and presented as crest height ratio to the head over the crest of the weir (p/y), crest length of water to channel width (L/W), crest length of water to the head over the crest of the weir (L/y), Froude number (F=V/√(gy)) and vertex angle () dimensionless parameters. Different models were then presented using sensitivity analysis in order to examine each of the dimensionless parameters presented in this study. In addition, an equation was presented through the use of nonlinear regression (NLR) for the purpose of comparison with GEP. The results of the studies conducted by using different statistical indexes indicated that GEP is more capable than NLR. This is to the extent that GEP predicts the discharge coefficient with an average relative error of approximately 2.5% in such a manner that the predicted values have less than 5% relative error in the worst model.

The paper proposes a human explainable artificial intelligence approach for mapping the status of environmental phenomena from multisource geo data. It is both knowledge and data driven: it exploits remote sensing expert’s knowledge to define the contributing factors from which partial evidence of the environmental status can be computed. Furthermore, it aggregates the partial evidences to compute a map of the environmental status by adapting to a region of interest through a learning mechanism exploiting Volunteered Geographic Information (VGI), both from in situ observations and photointerpretation. The approach is capable to capture the specificities of local context as well as to cope with the subjectivity and incompleteness of expert’s knowledge. The proposal is exemplified to map the status of standing water areas (i.e. water bodies and river, human driven or natural hazard flooding) by considering satellite data and geotagged observations. Results of the validation experiments were performed in three areas of Northern Italy, characterized by distinct ecosystems. Results of the proposed methodological framework showed better performances than traditional approaches based on single spectral indexes thresholding. The use of expert’s knowledge, possibly imprecise/uncertain and incomplete, the need of few ground truth data for learning, and finally the explainability of learned rules are the distinguishing characteristics of the proposal with respect to traditional machine learning methods.

In this study, we present the concept of interval-valued fuzzy soft point and then introduce the notions of neighborhood and quasi-neighbourhood of it in interval-valued fuzzy soft topological spaces. Separation axioms in interval-valued fuzzy soft topology, so-called $q$-$T_{i}$ for $ i=0,1,2,3,4 ,$ is introduced and some of its basic properties are also studied.

This study reports a general scenario for the out-of-equilibrium features of collapsing polymeric architectures. We use molecular dynamics simulations to characterize the coarsening kinetics, in bad solvent, for several macromolecular systems with an increasing degree of structural complexity. In particular, we focus on: flexible and semiflexible polymer chains, star polymers with 3 and 12 arms, and microgels with both ordered and disordered networks. Starting from a powerful analogy with critical phenomena, we construct a density field representation that removes fast fluctuations and provides a consistent characterization of the domain growth. Our results indicate that the coarsening kinetics presents a scaling behaviour that is independent of the solvent quality parameter, in analogy to time-temperature superposition principle. Interestingly, the domain growth in time follows a power-law behaviour that is approximately independent of the architecture for all the flexible systems; while it is steeper for the semiflexible chains. Nevertheless, the fractal nature of the dense regions emerging during the collapse exhibits the same scaling behaviour for all the macromolecules. This, suggests that the faster growing length scale in the semiflexible chains originates just from a faster mass diffusion along the chain contour, induced by the local stiffness. The decay of the dynamic correlations displays scaling behavior with the growing length scale of the system, which is a characteristic signature in coarsening phenomena.

Magnetic properties of soft magnetic composites are highly sensitive to the processing conditions. In this paper we focus on the possibility to model this effect using the Jiles-Atherton-Sablik theory. It is assumed that the effect of varying compaction pressure may be described by direct introduction of stress-dependent term in the model equations. The values of model parameters are kept constant. Verification of the proposed approach is carried out using measurement data from self-developed iron-based composite cores.

In this paper, a recent meta-heuristic optimization algorithm called the discrete-continuous hyper-spherical search algorithm is used to solve the mixed-integer nonlinear problem of soft open points (SOPs) and renewable distributed generators allocation along with new network reconfiguration methodology under different loading conditions to minimize the total power loss in balanced distribution systems. Multi-scenario studies, which aim to improve the investigation of the overall performance of the strategies, are conducted on IEEE 33-node and 83-node balanced distribution systems. The contributions of SOP losses to the total active losses, as well as the effect of increasing the number of SOPs connected to the system, are investigated to determine the real benefits gained from their allocation. The results obtained validate, with proper justifications, the effectiveness of allocating both SOPs and renewable distributed generators with the proposed network reconfiguration methodology to provide the best operation of distribution networks with minimum losses and enhanced power quality performance. It was also shown that SOPs successfully assist the growing integration plans of the renewable distributed generators units and can address issues related to voltage violations and network losses efficiently.

Soft-Core architecture for Analogue to Digital Converter (ADC) sampling is useful for mixed signal applications. Soft-core architecture for cutting edge odd or even ADC sampling with interface to block RAM memory has not been found. Soft-core architecture as a concept has become popular due to the advantage of customization for different applications as compared to general-core architecture suited for single application. The latest generation of piece wise sampling is odd sampling and was introduced in the second decade of the 20th century. Odd and even order sampling techniques are analogue in nature driven by a tuned (tuned for odd or even) mixer. This paper proposes a third-generation piece wise sampling with soft-core architecture that enables an option to select both odd and even while interfacing to memory mapping. The proposed odd/even has superior SNR performance of 6 dB as compared to existing architecture such as Mod-∆ which recorded worst performance of 18 dB. Advances in soft-core technology have allowed a niche odd/even switching field to be identified and studied, the study has also been extended to include memory architecture.

Building energy consumption plays an essential role in urban sustainability. The prediction of the energy demand is also of particular importance for developing smart cities and urban planning. Machine learning has recently contributed to the advancement of methods and technologies to predict demand and consumption for building energy systems. This paper presents a state of the art of machine learning models and evaluates the performance of these models. Through a systematic review and a comprehensive taxonomy, the advances of machine learning are carefully investigated and promising models are introduced.

In this article, the effect of substitution of Zr and Ga in Dy2Fe17 prepared by arc melting technique are studied. The substitution of Zr and Ga in Dy2Fe17 was found to have an important effect on their structure and magnetic properties. The Rietveld analysis conformed that the crystalline system wereTh2Ni17 structure. Lattice parameters a (Å) and c (Å), unit cell volume (Å3), bonding distance (Å) were calculated by using Rietveld analysis. The unit cell volume of Dy2Fe17-xZrx and Dy2Fe16Ga1-xZrx increase linearly with the Zr and Ga substitution. The substitution of Zr and Ga are limited up to x=1 in order to avoid the decrease in saturation magnetization. The Curie temperature (Tc) of Dy2Fe17-xZrx and Dy2Fe16Ga1-xZrx are found to be Zr content dependent. The Curie temperature is found to be increasing first and then decrease for higher Zr content. The maximum curie temperature was observed 510K at x = 0.75 for Dy2Fe17-xZrx and 505.1 K at x = 0.5 for Dy2Fe17-xZrx which are102 K and 97 K higher than Dy2Fe17. The room temperature Mössbauer analysis shows the decrease in average hyperfine field and increase in isomer shift with Zr doping.

In 1999, Molodtsov initiated the theory of soft sets as a new mathematical tool for dealing with uncertainties in many fields of applied sciences. In 2011, Shabir and Naz introduced and studied the notion of soft topological spaces, also defining and investigating many new soft properties as generalization of the classical ones. In this paper, we introduce the notions of soft separation between soft points and soft closed sets in order to obtain a generalization of the well-known Embedding Lemma to the class of soft topological spaces.

This paper presents a control algorithm for soft-switching series LC converters. The conventional voltage-to-voltage controller is split into a master and a slave controller. The master controller implements constant-current-constant-voltage (CCCV) control, required for demanding applications, i.e. lithium battery charging or laboratory power supplies. It defines the set-current for the open-loop current slave controller, which generates the PWM parameters. The power supply achieves fast large-signal responses, e.g. from 5 V to 24 V, where 95% of the target value is reached in less than 400 µs. The design is evaluated extensively in simulation and on a prototype. A consensus between simulation and measurement is achieved.

The combination of artificial intelligence algorithms and numerical methods has recently become popular in the prediction of macroscopic and microscopic hydrodynamics parameters of bubble column reactors. The multi inputs and outputs machine learning can cover small phase interactions or large fluid behavior in industrial domains. This numerical combination can develop the smart multiphase bubble column reactor with the ability of low-cost computational time. It can also decrease case studies for the optimization process when big data is appropriately used during learning. There are still many model parameters that need to be optimized for a very accurate artificial algorithm, including data processing and initialization, the combination of inputs and outputs, number of inputs and model tuning parameters. For this study, we aim to train four inputs big data during learning process by an adaptive neuro-fuzzy inference system or adaptive-network-based fuzzy inference system (ANFIS) method, and we consider the superficial gas velocity as one of the input variables, while for the first time, one of the computational fluid dynamics (CFD) outputs named gas velocity is used as an output of the artificial algorithm. The results show that the increasing number of input variables improves the intelligence of the ANFIS method up to , and the number of rules during learning process has a significant effect on the accuracy of this type of modeling. The results also show that propper selection of model parameters results in more accuracy in prediction of the flow characteristics in the column structure.

Organised hiking and trekking tours are promoted by many providers in adventure travel. Such guided tours shall be engaging for the tourists and evoke positive attitudes towards enjoying nature. We present the Trekking Engagement Profile as a tool to evaluate engagement factors of such guided tours, based on engagement research performed in a different domain, namely evaluating engagement in museums and science centres. This tool shall strengthen guides’ soft skills and enable them to increase engagement during an ongoing arrangement. For instance, the Trekking Engagement Profile can be applied when tourists indicate that the quality of a guided tour is not entirely to their liking. We show the viability of using the Trekking Engagement Profile in a small study with guides and participants of trekking tours offered by the Norwegian Trekking Association. Further, we offer guidance and charts to promote its practical use.

Nowadays, the development or improvement of actuation mechanisms is a crucial topic for the achievement of dextrous manipulation using soft robots. Then, a primary target of research is the design of actuation and driving devices. Consequently, in this paper, we introduce a soft driving epicyclical mechanism that mimics human muscle behavior and fulfills motion requirements to achieve grasping gestures using a robotic finger. The prototype is experimentally assessed, and results show that our approach has enough performance for the implementation in grasping tasks. Furthermore, we introduce the basis for a new soft epicyclical mechanism merger with shape memory alloys to allow active stiffness control of the mechanism.

In this paper, we present the design, manufacturing and characterization of a soft textile-based clutch (TBC) that switches between locking and unlocking of its linear displacement by exploiting vacuum stimulation. The applied vacuum locks the relative sliding motion between two elaborated textile webbings covered by an elastic silicone rubber bag. Based on different fabrication techniques, such as silicone casting on textile, melt embossing for direct fabrication of miniature patterns on textile and sewing, we developed three groups of TBC samples based on friction and interlocking principles and we compared their performance in blocking configuration. The clutch with interlocking mechanism presented the highest withstanding force (150 N) respect to the one (54 N) recorded for the friction-based clutch. The simple and compact structure of the proposed clutch, together with the intrinsic adaptability of fabric with other clothing and soft materials, make it a proper solution for applications in soft wearable robotics and generally as locking and variable stiffness solution for soft robotic applications.

Appropriate estimation of soil settlement is of significant importance since it directly influences the performance of building and infrastructures that are built on soil. In particular, the settlement of fine-grained soils is critical because of low permeability and continuous settlement with time. Coefficient of consolidation (Cc) is a key parameter to estimate settlement of fine-grained soil layers. However, estimation of this parameter is time consuming, needs skilled technicians, and specific equipment. In this study, Cc was estimated using several soil parameters such as liquid limit (LL), plastic limit (PL), and initial void ratio (e0). Estimating such parameters in laboratory is straight forward and needs substantially less time and cost compared to conventional tests to estimate Cc such as Oedometer test. This study presents a novel prediction model for Cc of fine-grained soils using gene-expression programming (GEP). GEP is a biologically inspired technique capable of offering closed-form solution for the optimal solution. A database consisted of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate Cc based on LL, PL, and e0. The performance of developed GEP-based model was evaluated through coefficient of determination (R2), root mean squared error (RMSE), and mean average error (MAE). High R2 and low error values indicated the descent performance of the model. Furthermore, the model was evaluated using the additional performance measures and met all the suggested criteria. Furthermore, the model had a better performance in terms of R2, RMSE, and MAE compared to most of existing models. It is expected that the developed model will decrease the time and cost associate with determining Cc of fine-grained soils.

Appropriate estimation of soil settlement is of significant importance since it directly influences the performance of building and infrastructures that are built on soil. In particular, the settlement of fine-grained soils is critical because of low permeability and continuous settlement with time. Coefficient of consolidation (C_c) is a key parameter to estimate settlement of fine-grained soil layers. However, estimation of this parameter is time consuming, needs skilled technicians, and specific equipment. In this study, C_c was estimated using several soil parameters such as liquid limit (LL), plastic limit (PL), and initial void ratio (e₀). Estimating such parameters in laboratory is straight forward and needs substantially less time and cost compared to conventional tests to estimate C_c such as oedometer test. This study presents a novel prediction model for C_c of fine-grained soils using gene-expression programming (GEP). GEP is a biologically inspired technique capable of offering closed-form solution for the optimal solution. A database consisted of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate C_c based on LL, PL, and e₀. The performance of developed GEP-based model was evaluated through coefficient of determination (R²), root mean squared error (RMSE), and mean average error (MAE). High R² and low error values indicated the descent performance of the model. Furthermore, the model was evaluated using the additional performance measures and met all the suggested criteria. Furthermore, the model had a better performance in terms of R², RMSE, and MAE compared to most of existing models. It is expected that the developed model will decrease the time and cost associate with determining C_c of fine-grained soils.Keywords: evolutionary model, gene-expression programming (GEP), prediction, soil compression index, estimation, soil engineering, soil informatics, civil engineering, machine learning, data science, big data, soft computing, deep learning, forecasting, subject classification codes, construction informatics, computational intelligence (CI), artificial intelligence (AI), estimation

Machine learning (ML) methods has recently contributed very well in the advancement of the prediction models used for energy consumption. Such models highly improve the accuracy, robustness, and precision and the generalization ability of the conventional time series forecasting tools. This article reviews the state of the art of machine learning models used in the general application of energy consumption. Through a novel search and taxonomy the most relevant literature in the field are classified according to the ML modeling technique, energy type, perdition type, and the application area. A comprehensive review of the literature identifies the major ML methods, their application and a discussion on the evaluation of their effectiveness in energy consumption prediction. This paper further makes a conclusion on the trend and the effectiveness of the ML models. As the result, this research reports an outstanding rise in the accuracy and an ever increasing performance of the prediction technologies using the novel hybrid and ensemble prediction models.

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

This research paper contributes to the literature of deterrence theory in general, and in particular with respect to white-collar crime, offering valuable inside by using a unique data set of fraud and violation of trust incidents for Paraguay. Descriptive evidence show a clear and continuous misallocation of funds and human capital, and therefore providing less efficient services for the public. Regression analysis suggests that clearance rate exerts a highly significant effect in deterring fraud but results are not clear for violation of trust incidents. Despite the limitations of available data, results confirm deterrence theory in Paraguay. However, to more than two-thirds of victims, not even the attempt was made to seek justice. As a side-result, it seems that a soft on crime strategy, induced from the former German penal code, has led to an increasing share of pre-trial diversion and therefore enhancing white-collar crimes like fraud and violation of trust due to impunity.

In this paper, several two-dimensional clustering scenarios are given. In those scenarios, soft partitioning clustering algorithms (Fuzzy C-means (FCM) and Possibilistic c-means (PCM)) are applied. Afterward, VAT is used to investigate the clustering tendency visually, and then in order of checking cluster validation, three types of indices (e.g., PC, DI, and DBI) were used. After observing the clustering algorithms, it was evident that each of them has its limitations; however, PCM is more robust to noise than FCM as in case of FCM a noise point has to be considered as a member of any of the cluster.

The increasing trend of studying the innate softness of robotic structures and amalgamating it with the benefits of the extensive developments in the field of embodied intelligence has led to sprouting of a relatively new yet extremely rewarding sphere of technology. The fusion of current deep reinforcement algorithms with physical advantages of a soft bio-inspired structure certainly directs us to a fruitful prospect of designing completely self-sufficient agents that are capable of learning from observations collected from their environment to achieve a task they have been assigned. For soft robotics structure possessing countless degrees of freedom, it is often not easy (something not even possible) to formulate mathematical constraints necessary for training a deep reinforcement learning (DRL) agent for the task in hand, hence, we resolve to imitation learning techniques due to ease of manually performing such tasks like manipulation that could be comfortably mimicked by our agent. Deploying current imitation learning algorithms on soft robotic systems have been observed to provide satisfactory results but there are still challenges in doing so. This review article thus posits an overview of various such algorithms along with instances of them being applied to real world scenarios and yielding state-of-the-art results followed by brief descriptions on various pristine branches of DRL research that may be centers of future research in this field of interest.

In the current natural environment, due to the complexity of the background and the high similarity of the color between immature green tomato and plant, the occlusion of the key organs (flower and fruit) by the leaves and stems will lead to low recognition rate and poor generalization of the detection model. Therefore, an improved tomato organ detection method based on convolutional neural network has been proposed in this paper. Based on the original Faster R-CNN algorithm, Resnet-50 with residual blocks was used to replace the traditional vgg16 feature extraction network, and K-means clustering method was used to adjust more appropriate anchor size than manual setting to improve detection accuracy. A variety of data augmentation techniques were used to train the network. The test results showed that compared with the traditional Faster R-CNN model, the mean average precision (mAP) of the optimal model was improved from 85.2% to 90.7%, the memory requirement decreased from 546.9MB to 115.9 MB, and the average detection time was shortened to 0.073S/sheet. As the performance greatly improved, the training model can be transplanted to the embedded system, which lays a theoretical foundation for the development of precise targeting pesticide application system and automatic picking device.

Despite a global push in the development and implementation of widespread alternative energy use, significant disparities exist across given nation-states. These disparities reflect both technical and economic factors, as well as the social, political, and ecological gaps between how communities see energy development and national/global policy goals. Known as the “local-national gap,” many nations struggle with fostering meaningful conversations about the role of alternative energy technologies within communities. Mitigation of this problem first requires understanding the distribution of existing alternative energy technologies at the local level of policymaking. Using the State of Georgia, U.S.A. as a case study, we present a model for analyzing how existing adoption trends enable/limit conversation at the scale of local governance (i.e., county governments). Leveraging existing work on the Gini Coefficient as a metric for measuring energy inequity, we argue these tools can be applied to analyze where gaps exist in ongoing solar adoption trends. As we demonstrate, communities that adopt solar tend to be concentrated in a few counties, indicating existing conversations are limited to a circumscribed set of social networks. This information and the model we demonstrate can enable focused qualitative analyses of existing solar trends, not only amongst high-adoption areas but within communities where little to no adoption has occurred.

Soft set theory is a mathematical tool for dealing with uncertainty. This paper investigates limits of interval type of soft sets (for short, it-soft sets). The concept of it-soft sets is first introduced. Then, limits of it-soft sets are proposed and their properties are obtained. Next, point-wise continuity of it-soft sets and continuous it-soft sets are discussed. Finally, an application for rough sets is given.

Cells take decisions on their responses depending on the stimuli received by the surrounding extracellular environment, that provides the essential cues at the micro and the nano-lengthscales required for adhesion, orientation, proliferation and differentiation. In this study, discontinuous microcones on silicon (Si) and continuous microgrooves on polyethylene terephthalate (PET) substrates were fabricated via ultrashort-pulsed laser irradiation at various fluences, resulting in microstructures with different roughness and geometrical characteristics. The topographical models attained were specifically developed to imitate the guidance and alignment of Schwann cells for oriented axonal regrowth, towards nerve regeneration. At the same time, positive replicas of the silicon microstructures formed were successfully reproduced, via soft lithography, on the biodegradable polymer poly(lactide-co-glycolide) (PLGA). The anisotropic continuous (PET) and discontinuous (PLGA replicas) microstructured polymeric substrates were assessed in terms of their influence on the Schwann cells responses. It is shown that the developed micropatterned substrates enable control over the cellular adhesion, proliferation and orientation and are thus useful to engineer cell alignment in vitro. This property could be potentially useful in the fields of neural tissue engineering and for dynamic microenvironment systems that simulate in vivo conditions.

In this systematic survey, an overview of non-conventional and soft-actuators is presented. The review is performed by using well-defined performance criteria with a direction to identify the exemplary applications in robotics. In addition to this, initial guidelines to measure the performance and applicability of soft actuators are provided. The meta-analysis is restricted to four main types of soft actuators: shape memory alloys (SMA), fluidic elastomer actuators (FEA), dielectric electro-activated polymers (DEAP) and shape morphing polymers (SMP). In exploring and comparing the capabilities of these actuators, the focus was on seven different aspects: compliance, topology, scalability-complexity, energy efficiency, operation range, performance and technological readiness level. The overview presented here provides a state-of-the-art summary of the advancements and can help researchers to select the most convenient soft actuators using the comprehensive comparison of the performance criteria.

The aim of this article is introduced the concept of double framed T-soft fuzzy set (DFT-soft fuzzy set) which is the combination of soft set and fuzzy set. We also defined the notions and apply this concept in BCK/BCI-algebras. By using example, we also discussed the concept of double framed T-soft fuzzy algebra (DFT-soft fuzzy algebra) and double framed B-soft fuzzy algebra (DFB-soft fuzzy algebra) and also investigated their properties. Each double framed T-soft fuzzy algebra is double framed B-soft fuzzy algebra but by using example, we proved that converse may or may not be possible.

High fructose and sugar-sweetened soft drink (SSSD) intakes are associated with elevated blood uric acid concentrations and increased risk of gout and cardiovascular disease. Fruits are naturally high in fructose but their effect on cardiometabolic risk is unknown. We examined the effect on serum uric acid and cardiometabolic risk factors of consuming fructose from either fruit or SSSD in overweight adults. 48 healthy, overweight (BMI≥ 28 kg/m²) men (n=21) and women (n=20) were randomised to either a fruit (n = 19) or SSSSD (n = 22) intervention for 4 weeks. The fruit group received 6 items of fresh and dried fruit per day and the SSSD group received 955ml of SSSD per day with treatments matched for energy and fructose content. Serum uric acid concentrations were significantly reduced in men in the fruit group compared to the SSSD group (difference 57.2 μmol/L [95% CI: 16.4, 98.0], p= 0.008) but there was no difference amongst women (1.3 μmol/L [95%CI: -9.5, 6.9], p= 0.295). There differences in weight change or other cardiometabolic risk factors. These findings suggest no need to restrict fruit intake in individuals with elevated serum uric acid concentrations, such as those with gout.

Objective: CAD/CAM generated ceramic implant abutments have recently attracted interest due to their superior customization possibilities and aesthetic advantages. Despite their widespread clinical use, little information is currently available on their surface topography, however. The transmucosal portion of the abutment shoulder is of particular interest, as it ideally supports soft tissue but minimizes mechanical plaque retention. The aim of this in vitro study was to topographically characterize the trans- and subgingival roughness of CAD/CAM zirconia abutments from different manufacturers and compare them with zirconia stock abutments. Material and Method: The surface topography of eight CAD/CAM zirconia implant abutments (tests) and two prefabricated zirconia stock abutments (controls) was determined using focus variation microscopy. Two points on the abutment shoulder were subjected to profilometric examination. 2D and 3D parameters of roughness were obtained and compared. Results: The surface roughness of all the test abutments exceeded the recommended threshold of Ra = 0.2 µm and therefore exhibited an increased risk of mechanical plaque retention. Obvious differences in surface structure were apparent, allowing conclusions to be drawn about the manufacturing method and subsequent reworking processes. Conclusion: Manually reworking the trans- and submucosal area of the investigated CAD/CAM zirconia abutments appears necessary to fulfil the conditions for optimal surface topography. The Sa value as arithmetic mean, taking the maximum height (Sz value) and surface excess (Sdr) into account, is an essential parameter for assessing the surface topography of implant abutments.

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

The Wind Energy Conversion System (WECS) plays an inevitable role across the world. In particular, the attention for Permanent Magnet Synchronous Generators (PMSGs) connected with wind farm is popular. This paper deals with the literature review that describes the recent advances, progresses and innovatory trends on PMSGs for WECS. Comparison between geared and direct-driven conversion systems and the classification of electrical machines used in WECS are discussed. A detailed analysis on the design aspects considering various topologies of PMSGs are encompassed in the literature. The PMSG design and optimization problems are solved by field computation techniques and optimized by using Soft Computing (SC) techniques .The three-dimensional, finite element software platform for the analysis and design of PMSGs is discussed. This paper also deals with the interdisciplinary modeling, analysis, and optimization of PMSG using Finite Element Analysis (FEM) and SC techniques. Finally, PMSGs are reviewed and compared for further exploration.

Since the first description of bisphosphonate-related osteonecrosis of the jaw (BRONJ) numerous research groups have focused on possible pathological mechanisms including the suppression of the bone turnover of the jaw, antiangiogenic effects and soft tissue toxicity. In our review we focused on summarizing the role of the soft tissues in the development and progression of BRONJ. The biological behavior of fibroblasts can be significantly influenced by bisphosphonates (BP) such as a concentration dependent reduction of cell viability. High concentrations of BP can induce apoptosis and necrosis of the cells. Comparable effects could be detected for keratinocytes. Compared to non-nitrogen containing bisphosphonates nitrogen-containing BP have worse effects on cell biology by blocking the mevalonate pathway. Next to this the cell architecture and the expression levels of several genes and proteins are significantly disturbed by BP. These inhibitory effects of BP are in accordance with BP related reduced angiogenesis and neovascularization and could underline the hypothesis that inhibition of fibroblasts and keratinocytes results in delayed wound healing and can induce and trigger BRONJ.