Multi-Objective Task-Aware Offloading and Scheduling Framework for Internet of Things Logistics.

IoT-based smart transportation monitors vehicles, cargo, and driver statuses for safe movement. Due to the limited computational capabilities of the sensors, the IoT devices require powerful remote servers to execute their tasks, and this phenomenon is called task offloading. Researchers have developed efficient task offloading and scheduling mechanisms for IoT devices to reduce energy consumption and response time. However, most research has not considered fault-tolerance-based job allocation for IoT logistics trucks, task and data-aware scheduling, priority-based task offloading, or multiple-parameter-based fog node selection. To overcome the limitations, we proposed a Multi-Objective Task-Aware Offloading and Scheduling Framework for IoT Logistics (MT-OSF). The proposed model prioritizes the tasks into delay-sensitive and computation-intensive tasks using a priority-based offloader and forwards the two lists to the Task-Aware Scheduler (TAS) for further processing on fog and cloud nodes. The Task-Aware Scheduler (TAS) uses a multi-criterion decision-making process, i.e., the analytical hierarchy process (AHP), to calculate the fog nodes' priority for task allocation and scheduling. The AHP decides the fog nodes' priority based on node energy, bandwidth, RAM, and MIPS power. Similarly, the TAS also calculates the shortest distance between the IoT-enabled vehicle and the fog node to which the IoT tasks are assigned for execution. A task-aware scheduler schedules delay-sensitive tasks on nearby fog nodes while allocating computation-intensive tasks to cloud data centers using the FCFS algorithm. Fault-tolerant manager is used to check task failure; if any task fails, the proposed system re-executes the tasks, and if any fog node fails, the proposed system allocates the tasks to another fog node to reduce the task failure ratio. The proposed model is simulated in iFogSim2 and demonstrates a 7% reduction in response time, 16% reduction in energy consumption, and 22% reduction in task failure ratio in comparison to Ant Colony Optimization and Round Robin.

Acoustofluidic separation of prolate and spherical micro-objects.

Most microfluidic separation techniques rely largely on object size as a separation marker. The ability to separate micro-objects based on their shape is crucial in various biomedical and chemical assays. Here, we develop an on-demand, label-free acoustofluidic method to separate prolate ellipsoids from spherical microparticles based on traveling surface acoustic wave-induced acoustic radiation force and torque. The freely rotating non-spherical micro-objects were aligned under the progressive acoustic field by the counterrotating radiation torque, and the major axis of the prolate ellipsoids was parallel to the progressive wave propagation. The specific alignment of the ellipsoidal particles resulted in a reduction in the cross-sectional area perpendicular to the wave propagation. As a consequence, the acoustic backscattering decreased, resulting in a decreased magnitude of the radiation force. Through the variation in radiation force, which depended on the micro-object morphology enabled the acoustofluidic shape-based separation. We conducted numerical simulations for the wave scattering of spherical and prolate objects to elucidate the working mechanism underlying the proposed method. A series of experiments with polystyrene microspheres, prolate ellipsoids, and peanut-shaped microparticles were performed for validation. Through quantitative analysis of the separation efficiency, we confirmed the high purity and high recovery rate of the proposed acoustofluidic shape-based separation of micro-objects. As a bioparticle, we utilize Thalassiosira eccentrica to perform shape-based separation, as the species has a variety of potential applications in drug delivery, biosensing, nanofabrication, bioencapsulation and immunoisolation.

Ultrasonic surface acoustic wave-assisted separation of microscale droplets with varying acoustic impedance.

In droplet-based microfluidic platforms, precise separation of microscale droplets of different chemical composition is increasingly necessary for high-throughput combinatorial chemistry in drug discovery and screening assays. A variety of droplet sorting methods have been proposed, in which droplets of the same kind are translocated. However, there has been relatively less effort in developing techniques to separate the uniform-sized droplets of different chemical composition. Most of the previous droplet sorting or separation techniques either rely on the droplet size for the separation marker or adopt on-demand application of a force field for the droplet sorting or separation. The existing droplet microfluidic separation techniques based on the in-droplet chemical composition are still in infancy because of the technical difficulties. In this study, we propose an acoustofluidic method to simultaneously separate microscale droplets of the same volume and dissimilar acoustic impedance using ultrasonic surface acoustic wave (SAW)-induced acoustic radiation force (ARF). For extensive investigation on the SAW-induced ARF acting on both cylindrical and spherical droplets, we first performed a set of the droplet sorting experiments under varying conditions of acoustic impedance of the dispersed phase fluid, droplet velocity, and wave amplitude. Moreover, for elucidation of the underlying physics, a new dimensionless number ARD was introduced, which was defined as the ratio of the ARF to the drag force acting on the droplets. The experimental results were comparatively analyzed by using a ray acoustics approach and found to be in good agreement with the theoretical estimation. Based on the findings, we successfully demonstrated the simultaneous separation of uniform-sized droplets of the different acoustic impedance under continuous application of the acoustic field in a label-free and detection-free manner. Insomuch as on-chip, precise separation of multiple kinds of droplets is critical in many droplet microfluidic applications, the proposed acoustofluidic approach will provide new prospects for microscale droplet separation.

Machine learning based skin lesion segmentation method with novel borders and hair removal techniques.

The effective segmentation of lesion(s) from dermoscopic skin images assists the Computer-Aided Diagnosis (CAD) systems in improving the diagnosing rate of skin cancer. The results of the existing skin lesion segmentation techniques are not up to the mark for dermoscopic images with artifacts like varying size corner borders with color similar to lesion(s) and/or hairs having low contrast with surrounding background. To improve the results of the existing skin lesion segmentation techniques for such kinds of dermoscopic images, an effective skin lesion segmentation method is proposed in this research work. The proposed method searches for the presence of corner borders in the given dermoscopc image and removes them if found otherwise it starts searching for the presence of hairs on it and eliminate them if present. Next, it enhances the resultant image using state-of-the-art image enhancement method and segments lesion from it using machine learning technique namely, GrabCut method. The proposed method was tested on PH2 and ISIC 2018 datasets containing 200 images each and its accuracy was measured with two evaluation metrics, i.e., Jaccard index, and Dice index. The evaluation results show that our proposed skin lesion segmentation method obtained Jaccard Index of 0.77, 0.80 and Dice index of 0.87, 0.82 values on PH2, and ISIC2018 datasets, respectively, which are better than state-of-the-art skin lesion segmentation techniques.

An Efficient Defocus Blur Segmentation Scheme Based on Hybrid LTP and PCNN.

The defocus or motion effect in images is one of the main reasons for the blurry regions in digital images. It can affect the image artifacts up to some extent. However, there is a need for automatic defocus segmentation to separate blurred and sharp regions to extract the information about defocus-blur objects in some specific areas, for example, scene enhancement and object detection or recognition in defocus-blur images. The existence of defocus-blur segmentation algorithms is less prominent in noise and also costly for designing metric maps of local clarity. In this research, the authors propose a novel and robust defocus-blur segmentation scheme consisting of a Local Ternary Pattern (LTP) measured alongside Pulse Coupled Neural Network (PCNN) technique. The proposed scheme segments the blur region from blurred fragments in the image scene to resolve the limitations mentioned above of the existing defocus segmentation methods. It is noticed that the extracted fusion of upper and lower patterns of proposed sharpness-measure yields more noticeable results in terms of regions and edges compared to referenced algorithms. Besides, the suggested parameters in the proposed descriptor can be flexible to modify for performing numerous settings. To test the proposed scheme's effectiveness, it is experimentally compared with eight referenced techniques along with a defocus-blur dataset of 1000 semi blurred images of numerous categories. The model adopted various evaluation metrics comprised of Precision, recall, and F1-Score, which improved the efficiency and accuracy of the proposed scheme. Moreover, the proposed scheme used some other flavors of evaluation parameters, e.g., Accuracy, Matthews Correlation-Coefficient (MCC), Dice-Similarity-Coefficient (DSC), and Specificity for ensuring provable evaluation results. Furthermore, the fuzzy-logic-based ranking approach of Evaluation Based on Distance from Average Solution (EDAS) module is also observed in the promising integrity analysis of the defocus blur segmentation and also in minimizing the time complexity.

Seamless Copy-Move Replication in Digital Images.

The importance and relevance of digital-image forensics has attracted researchers to establish different techniques for creating and detecting forgeries. The core category in passive image forgery is copy-move image forgery that affects the originality of image by applying a different transformation. In this paper, a frequency-domain image-manipulation method is presented. The method exploits the localized nature of discrete wavelet transform (DWT) to attain the region of the host image to be manipulated. Both patch and host image are subjected to DWT at the same level l to obtain 3l+1 sub-bands, and each sub-band of the patch is pasted to the identified region in the corresponding sub-band of the host image. Resulting manipulated host sub-bands are then subjected to inverse DWT to obtain the final manipulated host image. The proposed method shows good resistance against detection by two frequency-domain forgery detection methods from the literature. The purpose of this research work is to create a forgery and highlight the need to produce forgery detection methods that are robust against malicious copy-move forgery.

Automated multi-class brain tumor types detection by extracting RICA based features and employing machine learning techniques.

Among the other cancer types, the brain tumor is one the leading cause of cancer across globe. If the tumor is properly identified at an earlier stage, then the chances of the survival can be increased. To categorize the brain tumor there are several factors including texture, type and location of brain tumor. We proposed a novel reconstruction independent component analysis (RICA) feature extraction method to detect multi-class brain tumor types (pituitary, meningioma, and glioma). We then employed the robust machine learning techniques as support vector machine (SVM) with quadratic and linear kernels and linear discriminant analysis (LDA). For training and testing of the data validation, a 10-fold cross validation was employed. For the multi-class classification, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy and AUC were, respectively, 97.78%, 100%, 100%, 99.07, 99.34% and 0.9892 to detect pituitary using SVM Cubic followed by meningioma with accuracy (96.96%0, AUC (0.9348) and glioma with accuracy (95.88%), AUC (0.9635). The findings indicates that RICA feature based proposed methodology has more potential to detect the multiclass brain tumor types for improving diagnostic efficiency and can further improve the prediction accuracy to achieve the clinical outcomes.

VCC-BPS: Vertical Collaborative Clustering using Bit Plane Slicing.

The vertical collaborative clustering aims to unravel the hidden structure of data (similarity) among different sites, which will help data owners to make a smart decision without sharing actual data. For example, various hospitals located in different regions want to investigate the structure of common disease among people of different populations to identify latent causes without sharing actual data with other hospitals. Similarly, a chain of regional educational institutions wants to evaluate their students' performance belonging to different regions based on common latent constructs. The available methods used for finding hidden structures are complicated and biased to perform collaboration in measuring similarity among multiple sites. This study proposes vertical collaborative clustering using a bit plane slicing approach (VCC-BPS), which is simple and unique with improved accuracy, manages collaboration among various data sites. The VCC-BPS transforms data from input space to code space, capturing maximum similarity locally and collaboratively at a particular bit plane. The findings of this study highlight the significance of those particular bits which fit the model in correctly classifying class labels locally and collaboratively. Thenceforth, the data owner appraises local and collaborative results to reach a better decision. The VCC-BPS is validated by Geyser, Skin and Iris datasets and its results are compared with the composite dataset. It is found that the VCC-BPS outperforms existing solutions with improved accuracy in term of purity and Davies-Boulding index to manage collaboration among different data sites. It also performs data compression by representing a large number of observations with a small number of data symbols.

Emerging oxidized and defective phases in low-dimensional CrCl3.

Two-dimensional (2D) magnets such as chromium trihalides CrX3 (X = I, Br, Cl) represent a frontier for spintronics applications and, in particular, CrCl3 has attracted research interest due its relative stability under ambient conditions without rapid degradation, as opposed to CrI3. Herein, mechanically exfoliated CrCl3 flakes are characterized at the atomic scale and the electronic structures of pristine, oxidized, and defective monolayer CrCl3 phases are investigated employing density functional theory (DFT) calculations, scanning tunneling spectroscopy (STS), core level X-ray photoemission spectroscopy (XPS), and valence band XPS and ultraviolet photoemission spectroscopy (UPS). As revealed by atomically resolved transmission electron microscopy (TEM) and energy dispersive X-ray (EDX) analysis, the CrCl3 flakes show spontaneous surface oxidation upon air exposure with an extrinsic long-range ordered oxidized O-CrCl3 structure and amorphous chromium oxide formation on the edges of the flakes. XPS proves that CrCl3 is thermally stable up to 200 °C having intrinsically Cl vacancy-defects whose concentration is tunable via thermal annealing up to 400 °C. DFT calculations, supported by experimental valence band analysis, indicate that pure monolayer (ML) CrCl3 is an insulator with a band gap of 2.6 eV, while the electronic structures of oxidized and Cl defective phases of ML CrCl3, extrinsically emerging in exfoliated CrCl3 flakes, show in-gap spin-polarized states and relevant modifications of the electronic band structures.

Unsupervised color image segmentation: A case of RGB histogram based K-means clustering initialization.

Color-based image segmentation classifies pixels of digital images in numerous groups for further analysis in computer vision, pattern recognition, image understanding, and image processing applications. Various algorithms have been developed for image segmentation, but clustering algorithms play an important role in the segmentation of digital images. This paper presents a novel and adaptive initialization approach to determine the number of clusters and find the initial central points of clusters for the standard K-means algorithm to solve the segmentation problem of color images. The presented scheme uses a scanning procedure of the paired Red, Green, and Blue (RGB) color-channel histograms for determining the most salient modes in every histogram. Next, the histogram thresholding is applied and a search in every histogram mode is performed to accomplish RGB pairs. These RGB pairs are used as the initial cluster centers and cluster numbers that clustered each pixel into the appropriate region for generating the homogeneous regions. The proposed technique determines the best initialization parameters for the conventional K-means clustering technique. In this paper, the proposed approach was compared with various unsupervised image segmentation techniques on various image segmentation benchmarks. Furthermore, we made use of a ranking approach inspired by the Evaluation Based on Distance from Average Solution (EDAS) method to account for segmentation integrity. The experimental results show that the proposed technique outperforms the other existing clustering techniques by optimizing the segmentation quality and possibly reducing the classification error.

Global Survey of the Roles, Satisfaction, and Barriers of Home Health Care Nurses on the Provision of Palliative Care.

Background: The World Health Assembly urges members to build palliative care (PC) capacity as an ethical imperative. Nurses provide PC services in a variety of settings, including the home and may be the only health care professional able to access some disparate populations. Identifying current nursing services, resources, and satisfaction and barriers to nursing practice are essential to build global PC capacity. Objective: To globally examine home health care nurses' practice, satisfaction, and barriers, regarding existing palliative home care provision. Design: Needs assessment survey. Setting/Subjects: Five hundred thirty-two home health care nurses in 29 countries. Measurements: A needs assessment, developed through literature review and cognitive interviewing. Results: Nurses from developing countries performed more duties compared with those from high-income countries, suggesting a lack of resources in developing countries. Significant barriers to providing home care exist: personnel shortages, lack of funding and policies, poor access to end-of-life or hospice services, and decreased community awareness of services provided. Respondents identified lack of time, funding, and coverages as primary educational barriers. In-person local meetings and online courses were suggested as strategies to promote learning. Conclusions: It is imperative that home health care nurses have adequate resources to build PC capacity globally, which is so desperately needed. Nurses must be up to date on current evidence and practice within an evidence-based PC framework. Health care policy to increase necessary resources and the development of a multifaceted intervention to facilitate education about PC is indicated to build global capacity.

Watermarking of ultrasound medical images in teleradiology using compressed watermark.

The open accessibility of Internet-based medical images in teleradialogy face security threats due to the nonsecured communication media. This paper discusses the spatial domain watermarking of ultrasound medical images for content authentication, tamper detection, and lossless recovery. For this purpose, the image is divided into two main parts, the region of interest (ROI) and region of noninterest (RONI). The defined ROI and its hash value are combined as watermark, lossless compressed, and embedded into the RONI part of images at pixel's least significant bits (LSBs). The watermark lossless compression and embedding at pixel's LSBs preserve image diagnostic and perceptual qualities. Different lossless compression techniques including Lempel-Ziv-Welch (LZW) were tested for watermark compression. The performances of these techniques were compared based on more bit reduction and compression ratio. LZW was found better than others and used in tamper detection and recovery watermarking of medical images (TDARWMI) scheme development to be used for ROI authentication, tamper detection, localization, and lossless recovery. TDARWMI performance was compared and found to be better than other watermarking schemes.

Watermark Compression in Medical Image Watermarking Using Lempel-Ziv-Welch (LZW) Lossless Compression Technique.

In teleradiology, image contents may be altered due to noisy communication channels and hacker manipulation. Medical image data is very sensitive and can not tolerate any illegal change. Illegally changed image-based analysis could result in wrong medical decision. Digital watermarking technique can be used to authenticate images and detect as well as recover illegal changes made to teleradiology images. Watermarking of medical images with heavy payload watermarks causes image perceptual degradation. The image perceptual degradation directly affects medical diagnosis. To maintain the image perceptual and diagnostic qualities standard during watermarking, the watermark should be lossless compressed. This paper focuses on watermarking of ultrasound medical images with Lempel-Ziv-Welch (LZW) lossless-compressed watermarks. The watermark lossless compression reduces watermark payload without data loss. In this research work, watermark is the combination of defined region of interest (ROI) and image watermarking secret key. The performance of the LZW compression technique was compared with other conventional compression methods based on compression ratio. LZW was found better and used for watermark lossless compression in ultrasound medical images watermarking. Tabulated results show the watermark bits reduction, image watermarking with effective tamper detection and lossless recovery.

A Chronic Fatigue Syndrome (CFS) severity score based on case designation criteria.

BACKGROUND: Chronic Fatigue Syndrome case designation criteria are scored as physicians' subjective, nominal interpretations of patient fatigue, pain (headaches, myalgia, arthralgia, sore throat and lymph nodes), cognitive dysfunction, sleep and exertional exhaustion. METHODS: Subjects self-reported symptoms using an anchored ordinal scale of 0 (no symptom), 1 (trivial complaints), 2 (mild), 3 (moderate), and 4 (severe). Fatigue of 3 or 4 distinguished "Fatigued" from "Not Fatigued" subjects. The sum of the 8(Sum8) ancillary criteria was tested as a proxy for fatigue. All subjects had history and physical examinations to exclude medical fatigue, and ensure categorization as healthy or CFS subjects. RESULTS: Fatigued subjects were divided into CFS with ≥4 symptoms or Chronic Idiopathic Fatigue (CIF) with ≤3 symptoms. ROC of Sum8 for CFS and Not Fatigued subjects generated a threshold of 14 (specificity=0.934; sensitivity=0.928). CFS (n=256) and CIF (n=55) criteria were refined to include Sum8≥14 and ≤13, respectively. Not Fatigued subjects had highly skewed Sum8 responses. Healthy Controls (HC; n=269) were defined by fatigue≤2 and Sum8≤13. Those with Sum8≥14 were defined as CFS-Like With Insufficient Fatigue Syndrome (CFSLWIFS; n=20). Sum8 and Fatigue were highly correlated (R(2)=0.977; Cronbach's alpha=0.924) indicating an intimate relationship between symptom constructs. Cluster analysis suggested 4 clades each in CFS and HC. Translational utility was inferred from the clustering of proteomics from cerebrospinal fluid. CONCLUSIONS: Plotting Fatigue severity versus Sum8 produced an internally consistent classifying system. This is a necessary step for translating symptom profiles into fatigue phenotypes and their pathophysiological mechanisms.

Genetic characterization of commonly used glioma cell lines in the rat animal model system.

OBJECT: Animal models have been used extensively to discern the molecular biology of diseases and to gain insight into treatments. Nevertheless, discrepancies in the effects of treatments and procedures have been encountered during the transition from these animal models to application of the information to clinical trials in humans. To assess the genetic similarities between human gliomas and four cell lines used routinely in animal models, the authors used microarray technology to characterize the similarities and differences in gene expression. METHODS: To define the changes in gene expression, normal rat astrocytes were compared with four rat glioma cell lines (C6, 9L, F98, and RG2). The data were analyzed using two different methods: fold-change analysis and statistical analysis with t statistics. The gene products that were highlighted after intersecting the lists generated by the two methods of analysis were scrutinized against changes in gene expression reported in the literature. Tumorigenesis involves three major steps: the accumulation of genetic alterations, uncontrolled growth, and selected survival of transformed cells. The discussion of the results focuses attention on genes whose primary function is in pathways involved in glioma proliferation, infiltration, and neovascularization. A comparative microarray analysis of differentially expressed genes for four of the commonly used rat tumor cell lines is presented here. CONCLUSIONS: Due to the variances between the cell lines and results from analyses in humans, caution must be observed in interpreting as well as in the translation of information learned from animal models to its application in human trials.

Neuropathology in rhinosinusitis.

Pathophysiologic differences in neural responses to hypertonic saline (HTS) were investigated in subjects with acute sinusitis (n = 25), subjects with chronic fatigue syndrome (CFS) with nonallergic rhinitis (n = 14), subjects with active allergic rhinitis (AR; n = 17), and normal (n = 20) subjects. Increasing strengths of HTS were sprayed into their nostrils at 5-minute intervals. Sensations of nasal pain, blockage, and drip increased with concentration and were significantly elevated above normal. These parallels suggested activation of similar subsets of afferent neurons. Urea and lysozyme secretion were dose dependent in all groups, suggesting that serous cell exocytosis was one source of urea after neural stimulation. Only AR and normal groups had mucin dose responses and correlations between symptoms and lysozyme secretion (R(2) = 0.12-0.23). The lysozyme dose responses may represent axon responses in these groups. The neurogenic stimulus did not alter albumin (vascular) exudation in any group. Albumin and mucin concentrations were correlated in sinusitis, suggesting that nonneurogenic factors predominated in sinusitis mucous hypersecretion. CFS had neural hypersensitivity (pain) but reduced serous cell secretion. HTS nasal provocations identified significant, unique patterns of neural and mucosal dysregulation in each rhinosinusitis syndrome.