Analytical Chemistry

Chemically modified electrodes (CMEs) based on 2-phenyl-4-((4,6,8-trimethylazulen-1-yl)methylene)oxazol-5(4H)-one (M) were obtained by irreversible electrooxidation of M in millimolar solutions in 0.1 M tetrabutylammonium perchlorate (TBAP) in acetonitrile. These CMEs were characterized by a ferrocene redox probe, electrochemical impedance spectroscopy (EIS), X-ray photon spectroscopy (XPS), and scanning electron microscopy (SEM). The influence of the preparation conditions (charge and potential) was examined. The CMEs were finally used for the analysis of synthetic samples of heavy metal (HM) ions. The paper highlights the importance of potential and electropolymerization charge on the film properties, with accent on recognition of HMs, in order to identify the best conditions for their detection in water. The observed findings are relevant for further design and development of advanced materials based on azulenyl-phenyloxazolone for the analysis of HMs in water.

We discuss situations where thermogravimetric/Fourier transform infrared (TG/FT-IR) plots are obtained which differ substantially from the expected ones. The most common of these situations involves samples that release atmospheric components (H2O, CO2) at low temperatures. It is shown that the phenomena are mainly related to the purging action of the carrier gas, which strongly influences the spectroscopic portion of the TG/FT-IR plot. Such an influence, as well as the different situations originating from it, is discussed and explained on the basis of an analogy with the operational mode of a conventional dispersive spectrometer.

The Heat Loss Coefficient (HLC) characterizes the envelope efficiency of a building under in-use conditions, and it represents one of the main causes of the performance gap between the building design and its real operation. Accurate estimations of the HLC contribute to optimizing the energy consumption of a building. In this context, the application of black-box models in building energy analysis has been consolidated in recent years. The aim of this paper is to estimate the HLC of an existing building through the prediction of building thermal demands using a methodology based on Machine Learning (ML) models. Specifically, three different ML methods are applied to a public library in the northwest of Spain and compared; eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR) and Multi-Layer Perceptron (MLP) neural network. Furthermore, the accuracy of the results is measured, on the one hand, using both CV(RMSE) and Normalized Mean Biased Error (NMBE), as advised by A...

This paper introduces a comprehensive approach based on computer vision for the automatic detection, identification and pose estimation of lamps in a building using the image and location data from low-cost sensors, allowing the incorporation into the building information modelling (BIM). The procedure is based on our previous work, but the algorithms are substantially improved by generalizing the detection to any light surface type, including polygonal and circular shapes, and refining the BIM integration. We validate the complete methodology with a case study at the Mining and Energy Engineering School and achieve reliable results, increasing the successful real-time processing detections while using low computational resources, leading to an accurate, cost-effective and advanced method. The suitability and the adequacy of the method are proved and concluded.

Application of pervaporation and osmotic membrane distillation to the regeneration of spent solutions from the osmotic food dehydration Results of pervaporation (PV) of sucrose and calcium chloride spent solutions were presented. Additionally, osmotic membrane distillation (OMD) of sucrose solutions was investigated. It was found that the regeneration of spent sucrose solution for the reuse is possible by using PV or OMD processes. However, OMD process produces another spent stream i.e. CaCl2. Pervaporation membranes showed fluxes in the range of 0.5 - 0.9 kg m-2 h-1 in contact with 40° Brix sucrose solution, whereas OMD water permeate fluxes were in the range of 4 - 5 kg m-2 h-1 for the same feed concentration. Two different hybrid processes were suggested: i) pretreatment followed by OMD reconcentration of spent sucrose solution and independently PV for CaCl2 regeneration; ii) membrane pretreatment (MP) followed by PV of sucrose solution. Based on the experimental results, the mem...

In CH3NH3PbI3-based high efficiency perovskite solar cells (PSCs), tiny amount of PbI2 impurity was often found with the perovskite crystal. However, for two-step solution process-based perovskite films, most of findings have been based on the films having different morphologies between with and without PbI2. This was mainly due to the inferior morphology of pure perovskite film without PbI2, inevitably produced when the remaining PbI2 forced to be converted to perovskite, so advantages of pure perovskite photoactive layer without PbI2 impurity have been overlooked. In this work, we designed a printing-based two-step process, which could not only generate pure perovskite crystal without PbI2, but also provide uniform and full surface coverage perovskite film, of which nanoscale morphology was comparable to that prepared by conventional two-step solution process having residual PbI2. Our results showed that, in two-step solution process-based PSC, pure perovskite had better photon ab...

Here we provide data from a head-to-head comparison study using four biosensor platforms: GE Healthcare׳s Biacore T100, Bio-Rad׳s ProteOn XPR36, ForteBio׳s Octet RED384, and Wasatch Microfluidics׳s IBIS MX96. We used these instruments to analyze the binding interactions of a panel of ten high-affinity monoclonal antibodies with their antigen, human proprotein convertase subtilisin kexin type 9 (PCSK9). For each instrument, binding curves obtained at multiple densities of surface antibodies were fit to the 1:1 Langmuir kinetic model, and the association and dissociation rate constants and corresponding affinity constants were calculated. The data supplied in this article accompany the research article entitled, "Comparison of biosensor platforms in the evaluation of high affinity antibody-antigen binding kinetics" (Yang et al., 2016) [1], which further discusses the strengths and weaknesses of each biosensor platform with an emphasis on data consistency, comparability, and ...

The acquisition of reliable kinetic parameters for the characterization of biomolecular interactions is an important component of the drug discovery and development process. While several benchmark studies have explored the variability of kinetic rate constants obtained from multiple laboratories and biosensors, a direct comparison of these instruments' performance has not been undertaken, and systematic factors contributing to data variability from these systems have not been discussed. To address these questions, a panel of ten high-affinity monoclonal antibodies was simultaneously evaluated for their binding kinetics against the same antigen on four biosensor platforms: GE Healthcare's Biacore T100, Bio-Rad's ProteOn XPR36, ForteBio's Octet RED384, and Wasatch Microfluidics's IBIS MX96. We compared the strengths and weaknesses of these systems and found that despite certain inherent systematic limitations in instrumentation, the rank orders of both the associa...

Stiffness sensing is an important issue in medical diagnostic, robotics surgery, safe handling, and safe grasping of objects in production lines. Detecting and obtaining the characteristics in dwelling lumps embedded in a soft tissue and safe removing and handling of detected lumps is needed in surgery. Also in industry, grasping and handling an object without damaging in a place where it is not possible to access a human operator is very important. In this paper, a method for object handling is presented. It is based on the use of an intelligent gripper to detect the object stiffness and then setting a programmable force for grasping the object to move it. The main components of this system includes sensors (sensors for measuring force and displacement), electrical (electrical and electronic circuits, tactile data processing and force control system), mechanical (gripper mechanism and driving system for the gripper) and the display unit. The system uses a rotary potentiometer for m...

Cervical cancer is the fourth most prevalent cancer in women which has claimed 3,41,831 lives and accounted for 6,04,127 new cases in 2020 worldwide. To reduce such a vast mortality rate, early detection of the disease is essential. A fast, accurate, and interpretable machine learning model is a research subject. Fewer features reduce the computational effort and improve interpretation. A 3-Stage Hybrid feature selection approach and a Stacked Classification model are evaluated on the cervical cancer dataset obtained from the UCI Machine Learning Repository with 35 features and one outcome variable. Stage-1 uses a Genetic Algorithm and Logistic Regression Architecture for Feature Selection and selects twelve features well correlated with the class but not among themselves. Stage-2 utilizes the same Genetic Algorithm and Logistic Regression Architecture for Feature Selection to select five features. In Stage-3, Logistic Regression (LR), Naïve Bayes (NB), Support Vector Machine (SVM),...

This paper proposes a probability-based algorithm to track the LED in vehicle visible light communication systems using a camera. In this system, the transmitters are the vehicles' front and rear LED lights. The receivers are high speed cameras that take a series of images of the LEDs. ThedataembeddedinthelightisextractedbyfirstdetectingthepositionoftheLEDsintheseimages. Traditionally, LEDs are detected according to pixel intensity. However, when the vehicle is moving, motion blur occurs in the LED images, making it difficult to detect the LEDs. Particularly at high speeds, some frames are blurred at a high degree, which makes it impossible to detect the LED as well as extract the information embedded in these frames. The proposed algorithm relies not only on the pixel intensity, but also on the optical flow of the LEDs and on statistical information obtained from previous frames. Based on this information, the conditional probability that a pixel belongs to a LED is calculated. Th...

In recent years, there has been a growing demand for cybersecurity experts, and, according to predictions, this demand will continue to increase. Cyber Ranges can fill this gap by combining hands-on experience with educational courses, and conducting cybersecurity competitions. In this paper, we conduct a systematic survey of ten Cyber Ranges that were developed in the last decade, with a structured interview. The purpose of the interview is to find details about essential components, and especially the tools used to design, create, implement and operate a Cyber Range platform, and to present the findings.

Skin lesion detection is crucial in diagnosing and managing dermatological conditions. In this study, we developed and demonstrated the potential applicability of a novel mixed-scale dense convolution, self-attention mechanism, hierarchical feature fusion, and attention-based contextual information technique (MSHA) model for skin lesion detection using digital skin images of chickenpox and shingles lesions. The model adopts a combination of unique architectural designs, such as a mixed-scale dense convolution layer, self-attention mechanism, hierarchical feature fusion, and attention-based contextual information, enabling the MSHA model to capture and extract relevant features more effectively for chickenpox and shingles lesion classification. We also implemented an effective training strategy to enhance a better capacity to learn and represent the relevant features in the skin lesion images. We evaluated the performance of the novel model in comparison to state-of-the-art models, i...

In the era of rapid development in industry, an automatic production line is the fundamental and crucial mission for robotic pick-place. However, most production works for picking and placing workpieces are still manual operations in the stamping industry. Therefore, an intelligent system that is fully automatic with robotic pick-place instead of human labor needs to be developed. This study proposes a dynamic workpiece modeling integrated with a robotic arm based on two stereo vision scans using the fast point-feature histogram algorithm for the stamping industry. The point cloud models of workpieces are acquired by leveraging two depth cameras, type Azure Kinect Microsoft, after stereo calibration. The 6D poses of workpieces, including three translations and three rotations, can be estimated by applying algorithms for point cloud processing. After modeling the workpiece, a conveyor controlled by a microcontroller will deliver the dynamic workpiece to the robot. In order to accompl...

Policies for partially observed Markov decision processes can be efficiently learned by imitating expert policies learned using asymmetric information. Unfortunately, existing approaches for this kind of imitation learning have a serious flaw: the expert does not know what the trainee cannot see, and may therefore encourage actions that are sub-optimal or unsafe under partial information. To address this flaw, we derive an update that, when applied iteratively to an expert, maximizes the expected reward of the trainee’s policy. Using this update, we construct a computationally efficient algorithm, adaptive asymmetric DAgger (A2D), that jointly trains the expert and trainee policies. We then show that A2D allows the trainee to safely imitate the modified expert, and outperforms policies learned either by imitating a fixed expert or direct reinforcement learning.

The fast expansion of ICT (information and communications technology) has provided rich sources of data for the analysis, modeling, and interpretation of human mobility patterns. Many researchers have already introduced behavior-aware protocols for a better understanding of architecture and realistic modeling of behavioral characteristics, similarities, and aggregation of mobile users. We are introducing the similarity analytical framework for the mobile encountering analysis to allow for more direct integration between the physical world and cyber-based systems. In this research, we propose a method for finding the similarity behavior of users’ mobility patterns based on location and time. This research was conducted to develop a technique for producing co-occurrence matrices of users based on their similar behaviors to determine their encounters. Our approach, named SAA (similarity analysis approach), makes use of the device info i.e., IP (internet protocol) and MAC (media access ...

Liposomes were used as biomimetic models in capillary electrokinetic chromatography (EKC) for the determination of distribution constants (KD) of certain local anesthetics and a commonly used preservative. Synthetic liposomes comprised phosphatidylcholine and phosphatidylglycerol phospholipids with and without cholesterol. In addition, ghost liposomes made from red blood cell (RBC) lipid extracts were used as pseudostationary phase to acquire information on how the liposome composition affects the interactions between anesthetics and liposomes. These results were compared with theoretical distribution coefficients at pH 7.4. In addition to 25°C, the distribution constants were determined at 37 and 42°C to simulate physiological conditions. Moreover, the usability of five electroosmotic flow markers in liposome (LEKC) and micellar EKC (MEKC) was studied. LEKC was proven to be a convenient and fast technique for obtaining data about the distribution constants of local anesthetics betw...

Diabetes mellitus (DM) can lead to plantar ulcers, amputation and death. Plantar foot thermogram images acquired using an infrared camera have been shown to detect changes in temperature distribution associated with a higher risk of foot ulceration. Machine learning approaches applied to such infrared images may have utility in the early diagnosis of diabetic foot complications. In this work, a publicly available dataset was categorized into different classes, which were corroborated by domain experts, based on a temperature distribution parameter—the thermal change index (TCI). We then explored different machine-learning approaches for classifying thermograms of the TCI-labeled dataset. Classical machine learning algorithms with feature engineering and the convolutional neural network (CNN) with image enhancement techniques were extensively investigated to identify the best performing network for classifying thermograms. The multilayer perceptron (MLP) classifier along with the fea...

Steam explosion was proposed as a pretreatment method to accelerate the hydrodistillation (SE-HD) of kaffir lime leaves. This research aimed to compare SE-HD and conventional hydrodistillation (HD) in terms of extraction yield, extraction time, chemical composition, and essential oil quality. The extraction of kaffir lime oil with SE-HD was superior to HD with regard to extraction time (35 min. vs 120 min.) and extraction yield (1.386% vs 1.182%). The chemical composition and the quality of the extracted essential oil from both methods were quite similar, indicating the absence of adverse influence from the application of steam explosion as pretreatment. Therefore, SE-HD was a fast and an energy-saving method for extracting essential oil from kaffir lime leaves.