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Maintenance and replacement of aging water pipes are critical for urban development. To this end, trenchless rehabilitation methods that do not require excavation offer an efficient replacement for aging water pipes. Thus, this study developed a trenchless method using die drawing, which continuously inserts polyethylene (PE) liners into the reach point while maintaining traction in the host aging pipe. If the traction is removed after a certain period, the initially contracted liner pipe undergoes expansion under natural restoration and tightly adheres to the inner wall of the host pipe to achieve structural regeneration of the aging pipe. This study aimed to determine the factors influencing the die drawing process using a rehabilitation design. Accordingly, we experimented with the die drawing method to achieve structural reinforcement using high elasticity PE liners. Thereafter, based on the experimental data, we developed prediction models to estimate traction, pipe restoration rate after traction release, and pipe restoration period. The developed models estimated the minimum required restoration period and provided deeper insights into the restoration behavior of the die drawing method employed in pre-equipment maintenance before on-site construction. HIGHLIGHTS This study developed a trenchless method based on die drawing for aging pipelines, specifically applicable to large diameters.; The influential factors were determined with test-bed experiments.; We developed prediction models to estimate traction, pipe restoration rate after traction release, and pipe restoration period.; This can evaluate the appropriate construction time and work scale.;

Background: In the United States, diabetic kidney disease (DKD) affects about one-third of individuals with type 2 diabetes, causing significant economic burdens on the health care system and affecting patients’ quality of life. Objective: The aim of the study was to quantify the burden of care in patients at different stages of DKD and to monitor shifts in healthcare costs throughout these stages. Methods: This study used data from the Veterans Affairs National database, focusing on US veterans diagnosed with DKD between January 2016 and March 2022. Aggregated all-cause health care costs per month were summarized using descriptive statistics. We used a generalized linear model to calculate the cost of DKD patent care based on the stages, dialysis phase, and kidney replacement therapy. Results: The cohort of 685,288 patients with DKD was predominantly male (96.51%), White (74.42%), and non-Hispanic (93.54%). The mean (SD) per-patient per-month costs were $1,597 ($3,178), $1,772 ($4,269), $2,857 ($13,072), $3,722 ($12,134), $5,505 ($14,639), and $6,999 ($16,901) for stages 1, 2, 3a, 3b, 4 and 5 respectively. The average monthly expenditure for patients receiving long-term dialysis was $12,299. Costs peaked sharply during the first month of kidney replacement therapy at $38,359 but subsequently decreased to $6,636 after 1 year. Conclusions: The economic implications of DKD are profound, emphasizing the need for efficient early detection and disease management strategies. Preventing patients from progressing to advanced DKD stage will minimize the economic repercussions of DKD and will assist health care systems in optimizing resource allocation. Plain-Language Summary: Diabetic kidney disease (DKD) places a substantial burden on health care systems in the United States. In part of our effort to close the knowledge gap around the disease burden, care cost analysis for the patients with DKD was performed for US veterans. Along with stage progression, overall care costs per-patient per-month drastically increases from $1,597 (stage 1) to $6,999 (stage 5). Monthly costs exceeded $10,000 once veterans started to receive long-term dialysis. The quantitative summary will help health care systems efficiently allocate resources across various disease sectors.

Attention deficit hyperactivity disorder (ADHD) is one of the most common behavioral disorders in children in the early school years. This study proposes a novel virtual reality (VR) training system that simultaneously improves visual attention and pulmonary function in children with ADHD using fast Pranayama breathing, which is similar to the breathing method used to measure pulmonary function. We conducted the experiment with 12 children with ADHD who have normal pulmonary function in the control group and the experimental group, using the developed visual attention and pulmonary VR training system for eight training sessions in four weeks. The proposed system for children with ADHD consists of hardware that measures breathing and three VR games that use breathing data as direct input. The experiment result was analyzed by the Paired t-test for the pre-post comparisons and the Unpaired t-test for intergroup comparisons. Based on the conducted experiments and observations, we confirmed that the developed system can simultaneously improve visual attention and breathing capacity in children with ADHD.

Abstract The transgenic plant is a promising strategy for the production of highly valuable biotherapeutic proteins such as recombinant vaccines and antibodies. To achieve an efficient level of protein production, codon sequences and expression cassette elements need to be optimized. However, the systematical expression of recombinant proteins in plant biomass can generally be controlled for the production of therapeutic proteins after the generation of transgenic plants. Without understanding the transgene expression patterns in plant tissue, it is difficult to enhance further production levels. In this study, single-cell RNA-sequencing (scRNA-seq) analysis of transgenic tobacco (Nicotiana tabacum) leaf, expressing an immunotherapeutic llama antibody against breast cancer, anti-HER2 VHH–Fc, was conducted to obtain data on the expression pattern of tissue-specific cells. These high-quality scRNA-seq data enabled the identification of gene expression patterns by cell types, which can be applied to select the best cell types or tissues for the high production of these recombinant antibodies. These data provide a foundation to elucidate the mechanisms that regulate the biosynthesis of recombinant proteins in N. tabacum.

Owing to the consecutive occurrence of water quality incidents, such as the black and red water situation in Incheon, Mullae (Seoul), and Pohang, as well as the tap water larval events in Incheon and Jeju in 2020, South Korea's Ministry of Environment announced the new addition of Article 12-2 of the Enforcement Decree of the Water Supply and Waterworks Installation Act 2021. Additionally, the importance of intensive management for vulnerable areas where water quality incidents and complaints may occur repeatedly was specified. Therefore, this study suggests the hazard ranking of district-metered areas in the study area using the deterioration of the water distribution network, complaints (water quality, leakage), and modeling results (water pressure, residual chlorine concentration) to support the establishment of a reasonable water distribution network maintenance plan. The effective and sustainable maintenance of the water distribution network is possible if the hazard ranking is regularly determined using the suggested methodology proposed herein and the water distribution network intensive management area is managed. HIGHLIGHTS This study suggested a method to evaluate the hazard of water distribution networks and establish regional rankings from an engineering perspective.; The data that can be accumulated in the water distribution network, such as the number of failures, water quality, and leakage complaints, were used.; If the proposed methodology is used in establishing a network improvement and maintenance plan, the effective and sustainable maintenance of the water distribution network will be possible.;

Abstract Background Diminished immune defense plays an important role in cancer development. Cancer risk in immunocompromised patients may differ. Identifying individuals with elevated cancer risk can inform strategies for routine cancer screening. This study aimed to understand and compare cancer incidence and risk in three patient groups: recipients of solid organ transplant (SOT) or hematopoietic stem cell transplant (HSCT); diagnosis of primary or secondary immunodeficiency disorder (PID/SID); and recipients of tumor necrosis factor inhibitor (TNF-i) therapy. Methods This retrospective cohort study used the University of Utah Health System database and Huntsman Cancer Institute tumor registry. Patients aged ≥18 years with SOT/HSCT, PID/SID or ≥ 3 months of TNF-i therapy were included. The date of transplant, diagnosis of PID/SID, or 1st TNF-i medication order date was defined as the index date. We calculated cumulative cancer incidence by Kaplan-Meier method. A Cox-proportional hazard regression model with a stepwise variable selection process was used to identify independent risk factors associated with the time to onset of a new primary cancer. Results In total, 13,887 patients were included which comprised of 2982 (21%) SOT/HSCT, 7542 (54%) PID/SID and 3363 (24%) patients receiving TNF-i. The mean (SD) age ranged from 46.8 (15) years - 50.4 (18.2) years. The proportion of white patients ranged from 72.3–84.8%. The estimated cumulative cancer incidence was 11.5% in the SOT/HSCT cohort, 14.3% in the PID/SID cohort, and 8.8% in the TNF-i cohort. The multivariable model adjusted for age, benign in-situ disease, Charlson Comorbidity Index, hypertension/cardiovascular disease/end stage renal disease, gender, race/ethnicity, and renal cyst as significant risk factors. The adjusted hazard ratios for cancer development in SOT/HSCT and PID/SID cohorts compared to the TNF-i cohort over the full follow-up period were 1.57 (95% CI: 1.16–2.13) and 2.14 (95% CI: 1.65–2.77), respectively. Conclusion A significantly increased risk of cancer was observed in PID/SID patients and SOT/HSCT patients compared to TNF-i patients. Age ≥ 50 years, male gender, and clinical comorbidities were additional factors impacting cancer risk. PID/SID and SOT/HSCT patients may benefit from more intensive cancer screening.

This paper presents two systematic machine learning (ML) approaches for predicting the hydrogen production rate and cell current density for proton exchange membrane electrolyzer cells (PEMEC) as a function of different design parameters. Management of the hydrogen production rate and current density remains an important research topic for many technologies, including PEM water electrolysis cells, which are the focus of this study; therefore, two applications of ML were proposed to simulate the hydrogen production rate and current density for PEMEC. In this study, five different ML models were trained and tested for each simulation i.e., artificial neural networks, polynomial regression, support vector machine regressor, K-nearest neighbor regressor, and decision tree regressor, using 15 different input parameters and single output parameters. Box and whisker data analysis was applied to obtain the highest activation materials for the current density in the dataset. The box whisker plots revealed that Nafion115 and Nafion117 for the membrane type, titanium for the electrode type (cathode and anode), platinum (Pt) as the cathode catalyst, Pt and ruthenium as anode catalysts, and deionized water and methanol as the electrolyte (catholyte and anolyte) were the highest activation materials for current density. The performance comparison of the ML models was given by calculating the mean absolute error for the training and test data for each model. For both simulations, the ANN model showed the best performance, with a mean absolute error of 5.0006 for training and 6.4383 for testing for hydrogen production simulation, and 0.03819 for training and 0.04036 for testing for the current density simulation. Both applications have minimal error values, which means that the proposed methods simulate the real PEM cell performance well. This study paves the way for a significant method to reduce the cost, effort, and time required to fabricate PEMEC.

Abstract Virtual reality (VR) has recently been used as a clinical treatment because it can efficiently simulate situations that are difficult to control in real-world settings. In our study, we assessed the potential of VR in patients with chronic subjective tinnitus. An evaluation of its clinical benefits was performed based on analyses of patient electroencephalograms (EEGs) and by questionnaire responses after 6–8 weeks of patient involvement in our VR-based alleviation program. Clinical trials were performed at a tertiary academic hospital. Nineteen patients (aged 33–64 years) who visited our hospital with chronic subjective tinnitus over 3 months were enrolled in the study. The intervention consisted of trashing the tinnitus avatar in VR. We expected that the patients would have the subjective feeling of controlling tinnitus through our intervention. The VR environment comprised four different sessions in four different settings: a bedroom, a living room, a restaurant, and a city street. We analyzed changes in the source activities of the prefrontal regions related to tinnitus in these patients using standardized low-resolution brain electromagnetic tomography. The Tinnitus Handicap Inventory (THI), the total score (from 50.11 to 44.21, P = 0.046) and the grade (from 3.16 to 2.79, P = 0.035) were significantly improved after the VR-based tinnitus treatment program (P

Leucyl-tRNA synthetase 1 (LARS1) is a leucine sensor for mTORC1 signaling and regulates leucine utilization depending on glucose availability. Here, the author show that O-GlcNAcylation of LARS1 is crucial for its ability to regulate mTORC1 activity and leucine metabolism upon glucose starvation.

Aims: Treatment intensification (TI) may help patients with type 2 diabetes mellitus (T2DM) achieve target hemoglobin A1c (A1c) < 7.0%. This study aimed to measure the influence of TI on A1c outcome in patients who insufficiently responded to two classes of oral antidiabetic drugs (2OADs). Materials and methods: A retrospective observational study of patients with T2DM was performed using health plan claims and A1c records accrued between January 2010 and March 2017. The study population had an A1c ≥ 7.0% (baseline A1c) after treatment with 2OADs for one year. Patients who had TI with a third-class antidiabetic agent, including basal/biphasic insulin, glucagon-like peptide-1 receptor agonists (GLP-1RA), or OAD, within 365 days after baseline A1c were included. Patients who did not receive TI (NTI) within one year from the suboptimal A1c control were matched with TI patients using a propensity score approach. The odds ratio of achieving an A1c < 9.0% and < 7.0% for TI vs. NTI were calculated by logistic regressions. Results: A1c values of 401 TI − NTI matched pairs were analyzed. TI patients achieved a significantly lower follow-up A1c than NTI patients (7.79% ± 1.45 vs. 8.02% ± 1.67, p = 0.03). The odds ratio [95% confidence interval] of achieving A1c < 9.0% and < 7.0% for TI was 1.50 [1.04−2.17] and 1.19 [0.87−1.63], respectively. Conclusion: TI with a third-class agent further reduced A1c levels in patients whose A1c insufficiently responded with 2OADs; however, most patients failed to achieve an A1c < 7.0% on the intensified treatment.

Automated inspection systems utilizing computer vision technology are effective in managing traffic control devices (TCDs); however, they face challenges due to the limited availability of training datasets and the difficulty in generating new datasets. To address this, our study establishes a benchmark for cost-effective model training methods that achieve the desired accuracy using data from related domains and YOLOv5, a one-stage object detector known for its high accuracy and speed. In this study, three model cases were developed using distinct training approaches: (1) training with COCO-based pre-trained weights, (2) training with pre-trained weights from the source domain, and (3) training with a synthesized dataset mixed with source and target domains. Upon comparing these model cases, this study found that directly applying source domain data to the target domain is unfeasible, and a small amount of target domain data is necessary for optimal performance. A model trained with fine-tuning-based domain adaptation using pre-trained weights from the source domain and minimal target data, proved to be the most resource-efficient approach. These results contribute valuable guidance for practitioners aiming to develop TCD models with limited data, enabling them to build optimal models while conserving resources.

A global energy shift to a carbon-neutral society requires clean energy. Hydrogen can accelerate the process of expanding clean and renewable energy sources. However, conventional hydrogen compression and storage technology still suffers from inefficiencies, high costs, and safety concerns. An electrochemical hydrogen compressor (EHC) is a device similar in structure to a water electrolyzer. Its most significant advantage is that it can accomplish hydrogen separation and compression at the same time. With no mechanical motion and low energy consumption, the EHC is the key to future hydrogen compression and purification technology breakthroughs. In this study, the compression performance, efficiency, and other related parameters of EHC are investigated through experiments and simulation calculations. The experimental results show that under the same experimental conditions, increasing the supply voltage and the pressure in the anode chamber can improve the reaction rate of EHC and balance the pressure difference between the cathode and anode. The presence of residual air in the anode can impede the interaction between hydrogen and the catalyst, as well as the proton exchange membrane (PEM), resulting in a decrease in performance. In addition, it was found that a single EHC has a better compression ratio and reaction rate than a double EHC. The experimental results were compatible with the theoretical calculations within less than a 7% deviation. Finally, the conditions required to reach commercialization were evaluated using the theoretical model.

With advances in machine vision systems (e.g., artificial eye, unmanned aerial vehicles, surveillance monitoring) scene semantic recognition (SSR) technology has attracted much attention due to its related applications such as autonomous driving, tourist navigation, intelligent traffic and remote aerial sensing. Although tremendous progress has been made in visual interpretation, several challenges remain (i.e., dynamic backgrounds, occlusion, lack of labeled data, changes in illumination, direction, and size). Therefore, we have proposed a novel SSR framework that intelligently segments the locations of objects, generates a novel Bag of Features, and recognizes scenes via Maximum Entropy. First, denoising and smoothing are applied on scene data. Second, modified Fuzzy C-Means integrates with super-pixels and Random Forest for the segmentation of objects. Third, these segmented objects are used to extract a novel Bag of Features that concatenate different blobs, multiple orientations, Fourier transform and geometrical points over the objects. An Artificial Neural Network recognizes the multiple objects using the different patterns of objects. Finally, labels are estimated via Maximum Entropy model. During experimental evaluation, our proposed system illustrated a remarkable mean accuracy rate of 90.07% over the MSRC dataset and 89.26% over the Caltech 101 for object recognition, and 93.53% over the Pascal-VOC12 dataset for scene recognition, respectively. The proposed system should be applicable to various emerging technologies, such as augmented reality, to represent the real-world environment for military training and engineering design, as well as for entertainment, artificial eyes for visually impaired people and traffic monitoring to avoid congestion or road accidents.

The monitoring of human physical activities using wearable sensors, such as inertial-based sensors, plays a significant role in various current and potential applications. These applications include physical health tracking, surveillance systems, and robotic assistive technologies. Despite the wide range of applications, classification and recognition of human activities remains imprecise and this may contribute to unfavorable reactions and responses. To improve the recognition of human activities, we designed a dataset in which ten participants (five male and five female) performed 11 different activities wearing three body-worn inertial sensors in different locations on the body. Our model extracts data via a hierarchical feature-based technique. These features include time, wavelet, and time-frequency domains, respectively. Stochastic gradient descent (SGD) is then introduced to optimize selective features. The selected features with optimized patterns are further processed by multi-layered kernel sliding perceptron to develop adaptive learning for the classification of physical human activities. Our proposed model was experimentally evaluated and applied on three benchmark datasets: IM-WSHA, a self-annotated dataset, PAMAP2 dataset which is comprised of daily living activities, and an HuGaDB, a dataset which contains physical activities for aging people. The experimental results show that the proposed method achieves better results and outperforms others in terms of recognition accuracy, achieving an accuracy rate of 83.18%, 94.16%, and 92.50% respectively, when IM-WSHA, PAMAP2, and HuGaDB datasets are applied.

Human-Object Interaction (HOI) recognition, due to its significance in many computer vision-based applications, requires in-depth and meaningful details from image sequences. Incorporating semantics in scene understanding has led to a deep understanding of human-centric actions. Therefore, in this research work, we propose a semantic HOI recognition system based on multi-vision sensors. In the proposed system, the de-noised RGB and depth images, via Bilateral Filtering (BLF), are segmented into multiple clusters using a Simple Linear Iterative Clustering (SLIC) algorithm. The skeleton is then extracted from segmented RGB and depth images via Euclidean Distance Transform (EDT). Human joints, extracted from the skeleton, provide the annotations for accurate pixel-level labeling. An elliptical human model is then generated via a Gaussian Mixture Model (GMM). A Conditional Random Field (CRF) model is trained to allocate a specific label to each pixel of different human body parts and an interaction object. Two semantic feature types that are extracted from each labeled body part of the human and labelled objects are: Fiducial points and 3D point cloud. Features descriptors are quantized using Fisher’s Linear Discriminant Analysis (FLDA) and classified using K-ary Tree Hashing (KATH). In experimentation phase the recognition accuracy achieved with the Sports dataset is 92.88%, with the Sun Yat-Sen University (SYSU) 3D HOI dataset is 93.5% and with the Nanyang Technological University (NTU) RGB+D dataset it is 94.16%. The proposed system is validated via extensive experimentation and should be applicable to many computer-vision based applications such as healthcare monitoring, security systems and assisted living etc.

Video/audio conferencing systems have been used extensively for remote collaboration over many years. Recently, virtual and mixed reality (VR/MR) systems have started to show great potential as communication media for remote collaboration. Prior studies revealed that the creation of common ground between discourse participants is crucial for collaboration and that grounding techniques change with the communication medium. However, it is difficult to find previous research that compares VR and MR communication system performances with video conferencing systems regarding the creation of common ground for collaborative problem solving. On the other hand, prior studies have found that display fidelity and interaction fidelity had significant effects on performance-intensive individual tasks in virtual reality. Fidelity in VR can be defined as the degree of objective accuracy with which the real-world is represented by the virtual world. However, to date, fidelity for collaborative tasks in VR/MR has not been defined or studied much. In this paper, we compare five different communication media for the establishment of common ground in collaborative problem-solving tasks: Webcam, headband camera, VR, MR, and audio-only conferencing systems. We analyzed these communication media with respect to collaborative fidelity components which we defined. For the experiments, we utilized two different types of collaborative tasks: a 2D Tangram puzzle and a 3D Soma cube puzzle. The experimental results show that the traditional Webcam performed better than the other media in the 2D task, while the headband camera performed better in the 3D task. In terms of collaboration fidelity, these results were somehow predictable, although there was a little difference between our expectations and the results.

Many children with attention deficit hyperactivity disorder (ADHD) perform poorly in their academic studies. They also have difficulties in their social lives due to a lack of interpersonal skills and this often continues into adult life. Appropriate early therapies and medications can be very beneficial. In this paper, we introduce and demonstrate the benefits of a new type of treatment, namely, an eye-contact game which successfully exploits mixed reality technology. None of the patients in our experiment were older than ten years of age. They were able to pay attention and sustain interest in the treatment sessions over a span of six weeks. After participating in the treatment sessions with our game, the omission/commission errors which were evaluated in an attention test taken by the experimental group decreased significantly and appeared within the normal range. In addition to the improvement in error rates, the mean response time to an interactive metronome test significantly decreased. Importantly, our game allows patients to conduct this treatment by themselves at home without professional assistance. To the best of our knowledge, this study is one of the first studies to use a mixed reality head-mounted display to treat children with attention deficit hyperactivity disorder and to prove its potential as a treatment for clinically diagnosed children.

The study of human posture analysis and gait event detection from various types of inputs is a key contribution to the human life log. With the help of this research and technologies humans can save costs in terms of time and utility resources. In this paper we present a robust approach to human posture analysis and gait event detection from complex video-based data. For this, initially posture information, landmark information are extracted, and human 2D skeleton mesh are extracted, using this information set we reconstruct the human 2D to 3D model. Contextual features, namely, degrees of freedom over detected body parts, joint angle information, periodic and non-periodic motion, and human motion direction flow, are extracted. For features mining, we applied the rule-based features mining technique and, for gait event detection and classification, the deep learning-based CNN technique is applied over the mpii-video pose, the COCO, and the pose track datasets. For the mpii-video pose dataset, we achieved a human landmark detection mean accuracy of 87.09% and a gait event recognition mean accuracy of 90.90%. For the COCO dataset, we achieved a human landmark detection mean accuracy of 87.36% and a gait event recognition mean accuracy of 89.09%. For the pose track dataset, we achieved a human landmark detection mean accuracy of 87.72% and a gait event recognition mean accuracy of 88.18%. The proposed system performance shows a significant improvement compared to existing state-of-the-art frameworks.

We propose efficient multiple machine learning (ML) models using specifically polynomial and logistic regression ML methods to predict the optimal design of proton exchange membrane (PEM) electrolyzer cells. The models predict eleven different parameters of the cell components for four different input parameters such as hydrogen production rate, cathode area, anode area, and the type of cell design (e.g., single or bipolar). The models fit well as we trained multiple machine learning models on 148 samples and validated the model performance on a test set of 16 samples. The average accuracy of the classification model and the mean absolute error is 83.6% and 6.825, respectively, which indicates that the proposed technique performs very well. We also measured the hydrogen production rate using a custom-made PEM electrolyzer cell fabricated based on the predicted parameters and compared it to the simulation result. Both results are in excellent agreement and within a negligible experimental uncertainty (i.e., a mean absolute error of 0.615). Finally, optimal PEM electrolyzer cells for commercial-scaled hydrogen production rates ranging from 500 to 5000 mL/min were designed using the machine learning models. To the best of our knowledge, we are the first group to model the PEM design problem with such large parameter predictions using machine learning with those specific input parameters. This study opens the route for providing a form of technology that can greatly save the cost and time required to develop water electrolyzer cells for future hydrogen production.

Immunization with thetumor-associated antigen GA733 glycoprotein, which is highly expressed in colorectal cancer, is considered to be a promising strategy for cancer prevention and treatment. We cloned a fusion gene of GA733 and immunoglobulin Fc fragment (GA733-Fc), and that of GA733-Fc and an endoplasmic reticulum retention motif (GA733-FcK) into the Cowpea mosaic virus (CPMV)-based transient plant expression vector, pEAQ-HT. Agrobacterium tumefaciens (LBA4404) transformed with the vectors pEAQ-HT-GA733-Fc and pEAQ-HT-GA733-FcK was infiltrated into the leaves of Nicotiana benthamiana plants. To optimize harvesting of leaf to express therapeutic glycoproteins both spatially and temporally, protein expression levels at various leaf positions (top, middle, and base) and days post-infiltration (dpi) were investigated. The GA733-Fc and GA733-FcK genes were detected in leaves at 1–10 dpi using PCR. As assessed by western blot, GA733-Fc and GA733-FcK were expressed at the highest levels in the top leaf position at 5 dpi, and GA733-FcK was expressed more than GA733-Fc. The proteins were successfully purified from infiltrated N. benthamiana leaves using protein A affinity chromatography. ELISA verified that an anti-GA733 antibody recognized both purified proteins. Thus, a functional GA733-Fc colorectal cancer vaccine protein can be transiently expressed using a CPMV virus-based vector, with an optimized expression time and leaf position post-infiltration.

Background Atrial fibrillation (AF) is a comorbidity associated with heart failure and catecholaminergic polymorphic ventricular tachycardia. Despite the Ca2+‐dependent nature of both of these pathologies, AF often responds to Na+ channel blockers. We investigated how targeting interdependent Na+/Ca2+ dysregulation might prevent focal activity and control AF. Methods and Results We studied AF in 2 models of Ca2+‐dependent disorders, a murine model of catecholaminergic polymorphic ventricular tachycardia and a canine model of chronic tachypacing‐induced heart failure. Imaging studies revealed close association of neuronal‐type Na+ channels (nNav) with ryanodine receptors and Na+/Ca2+ exchanger. Catecholamine stimulation induced cellular and in vivo atrial arrhythmias in wild‐type mice only during pharmacological augmentation of nNav activity. In contrast, catecholamine stimulation alone was sufficient to elicit atrial arrhythmias in catecholaminergic polymorphic ventricular tachycardia mice and failing canine atria. Importantly, these were abolished by acute nNav inhibition (tetrodotoxin or riluzole) implicating Na+/Ca2+ dysregulation in AF. These findings were then tested in 2 nonrandomized retrospective cohorts: an amyotrophic lateral sclerosis clinic and an academic medical center. Riluzole‐treated patients adjusted for baseline characteristics evidenced significantly lower incidence of arrhythmias including new‐onset AF, supporting the preclinical results. Conclusions These data suggest that nNaVs mediate Na+‐Ca2+ crosstalk within nanodomains containing Ca2+ release machinery and, thereby, contribute to AF triggers. Disruption of this mechanism by nNav inhibition can effectively prevent AF arising from diverse causes.

Abstract Background The aim of this study is (1) to compare joint line (JL) restoration and clinical outcomes in revision TKA based on the contemporary prosthesis type and (2) to determine the restoration of posterior condylar offset (PCO) according to the use of a femoral offset stem. Methods Sixty knees that underwent revision TKA from April 2003 to December 2013 with a minimum of 1 year follow up were included. These were further subdivided into three groups according to prosthesis type: group I (2 mm offset), group II (4.5 mm offset), group III (2, 4, and 6 mm offset). The JL position change was defined as a change in the adductor tubercle distance, preoperatively versus postoperatively. We also collected the change of PCO in distal femur and clinical outcomes including range of motion (ROM) and knee scores at the preoperative and last follow-up periods. Results The JL elevation for group III was significantly lower than that of the other groups. Usage of the tibial and femoral offset stem in group III was more frequent than in the other groups. PCO in revision TKA with a femoral offset stem was significantly greater than in those with a femoral straight stem. The JL position in revision TKA with a femoral offset stem was less elevated than in those with a femoral straight stem. Conclusions More recent developed revision prosthesis with various sizes option of offset stem may be effective in restoring the native joint line as using the femoral offset stem more convenience in revision TKAs.

Background Critically ill patients experience muscle weakness, which leads to functional disability. Both functional electrical stimulation (FES) and in-bed cycling can be an alternative measure for intensive care unit (ICU) patients who are not feasible for active exercise. The aim of this study was to examine whether FES and in-bed cycling have a positive effect on muscle mass in ICU patients. Methods Critically ill patients who received mechanical ventilation for at least 24 hours were included. After passive range of motion exercise, in-bed cycling was applied for 20 minutes, and FES was applied for 20 minutes on the left leg. The right leg received in-bed cycling and the left leg received both FES and in-bed cycling. Thigh circumferences and rectus femoris cross-sectional area (CSA) were assessed with ultrasonography before and after the intervention. Muscle strength was assessed by Medical Research Council scale. Results A total of 10 patients were enrolled in this study as a pilot study. Before and after the intervention, the CSA of right rectus femoris increased from 5.08 ± 1.51 cm2 to 6.01 ± 2.21 cm2 , which was statistically significant (P = 0.003). The thigh circumference was also increased and statistically significant (P = 0.006). There was no difference between left and right in regard to FES application. There is no significant change in muscle strength before and after the intervention (right and left, P = 0.317 and P = 0.368, respectively). Conclusions In-bed cycling increased thigh circumferences rectus femoris CSA. Adding FES did not show differences.

Indoor heating systems currently used are highly dependent on fossil fuels; hence, it is urgent to develop a new heating system to achieve carbon zero-emission. A solar air heater is eco-friendly because it generates nearly zero greenhouse gases. In this study, a parametric study was conducted for optimizing solar air heater design applicable to indoor heating. Installing the internal structure in the solar heater changes the interior flow characteristic, resulting in the air temperature increased by about 14.2 K on average compared to the heater without the internal structure. An additional case study was carried out to optimize the ideal quantity of phase change materials (PCM) in terms of mass fraction and heat capacity for various operating conditions. An excessive amount of PCM (e.g., 66% of the storage space filled with PCM) deteriorates the performance of the air heater unless the entire PCM could be melted during the daytime. After heating, the air temperature was maintained the longest when only 33% of the internal space was filled with PCM. The solar air heater can fully replace or partly assist a conventional heater for indoor heating, and it could reduce approximately 0.6 tCO2 per year.

Peanut (Arachis hypogaea L.) seeds were germinated to investigate the effect of the fermentation period of oak tree sawdust on germination viability and seedling characteristics. Its germination rate, seedling weight, length, and total vigor index were assessed. The seeds were sown in oak tree sawdust fermented for 0, 30, 45, and 60 days. The germination rates of the seeds in fermented sawdust were significantly different. The seeds in the 45-day fermented sawdust produced the heaviest biomass weight (4.6 g) with the longest true leaf (1.7 cm) and hypocotyl (3.4 cm) resulting in the highest total vigor index (925.8). In contrast, seeds in 0-day fermented sawdust had the lowest total vigor index (18.3). Microbiome analysis showed that the microbial community in the sawdust changed as the fermentation progressed, indicating that the microbial community seems to affect seed germination physiology. Taken together, 45-day fermented sawdust is recommended for optimal peanut seed germination and seedling growth.

The pressure on water infrastructure has increased due to an increase in the number of aging water pipes. Aging pipes are prone to failure, causing significant financial losses and service disruptions. The increasing number of aged pipes and limited budget for pipe rehabilitation or replacement necessitates water infrastructure asset management to ensure sustainable water supply services. In this study, contingent valuation was used to estimate the willingness to pay (WTP) and value improved water supply services through the implementation of asset management. To estimate the WTP at the individual and county levels, we performed a nationwide survey including eight provinces and 24 counties/cities with distinct water supply service performances. At the individual level, the median WTP estimated using the double-bounded dichotomous choice model was 249.50 KRW/month (0.22 USD/month). The results showed that high-level satisfaction of customers with water supply services and positive price perception of water bills resulted in a high WTP. At the county level, decreasing water supply service performances were associated with a low WTP, indicating that proper interventions by local utilities are required to achieve sustainable water supply services. Our results provide a quantitative basis for decision-making in implementation of water infrastructure asset management.

Automatic head tracking and counting using depth imagery has various practical applications in security, logistics, queue management, space utilization and visitor counting. However, no currently available system can clearly distinguish between a human head and other objects in order to track and count people accurately. For this reason, we propose a novel system that can track people by monitoring their heads and shoulders in complex environments and also count the number of people entering and exiting the scene. Our system is split into six phases; at first, preprocessing is done by converting videos of a scene into frames and removing the background from the video frames. Second, heads are detected using Hough Circular Gradient Transform, and shoulders are detected by HOG based symmetry methods. Third, three robust features, namely, fused joint HOG-LBP, Energy based Point clouds and Fused intra-inter trajectories are extracted. Fourth, the Apriori-Association is implemented to select the best features. Fifth, deep learning is used for accurate people tracking. Finally, heads are counted using Cross-line judgment. The system was tested on three benchmark datasets: the PCDS dataset, the MICC people counting dataset and the GOTPD dataset and counting accuracy of 98.40%, 98%, and 99% respectively was achieved. Our system obtained remarkable results.

This work presents the grouping of dependent tasks into a cluster using the Bayesian analysis model to solve the affinity scheduling problem in heterogeneous multicore systems. The non-affinity scheduling of tasks has a negative impact as the overall execution time for the tasks increases. Furthermore, non-affinity-based scheduling also limits the potential for data reuse in the caches so it becomes necessary to bring the same data into the caches multiple times. In heterogeneous multicore systems, it is essential to address the load balancing problem as all cores are operating at varying frequencies. We propose two techniques to solve the load balancing issue, one being designated “chunk-based scheduler” (CBS) which is applied to the heterogeneous systems while the other system is “quantum-based intra-core task migration” (QBICTM) where each task is given a fair and equal chance to run on the fastest core. Results show 30–55% improvement in the average execution time of the tasks by applying our CBS or QBICTM scheduler compare to other traditional schedulers when compared using the same operating system.

Leucyl-tRNA synthetase (LRS) is a leucine sensor of the mTORC1 pathway. Here, the authors identify inhibitors of the GTPase activating function of LRS, not affecting its catalytic activity, and demonstrate that the leucine sensor function of LRS can be a new target for mTORC1 inhibition.

Venous thromboembolism (VTE) represents a major health-care problem. Understanding patient satisfaction with VTE care is an important health-care goal. A national online survey was administered to adults who had experienced a recent VTE event. The survey assessed patient satisfaction by: (1) satisfaction with VTE care provider; (2) likelihood to recommend VTE provider; and (3) satisfaction with communication between VTE care providers. Each question was correlated with patient demographics, patient care harms (ie, misdiagnosis, wrong treatment), patient beliefs concerning outcomes, and type of anticoagulant therapy. Respondents (907) were 52.4 ± 14.4 years, predominantly Caucasian, mostly women, and generally had health insurance. Most respondents were satisfied with VTE care providers, likely to recommend their VTE provider, and satisfied with communication between providers. Dissatisfaction was strongly associated with treatment mistakes, a wrong diagnosis or treatment, or delayed treatment. A national sample of VTE patients were generally satisfied with VTE care experiences. The VTE care dissatisfaction was strongly associated with perceived mistakes in VTE care. Interventions aimed at reducing, acknowledging, and communicating errors could be studied to improve VTE care satisfaction.

Objective: Leucyl-tRNA synthetase (LRS) is an aminoacyl-tRNA synthetase catalyzing ligation of leucine to its cognate tRNA and is involved in the activation of mTORC1 by sensing cytoplasmic leucine. In this study, the usefulness of LRS as a therapeutic target of non-small cell lung cancer (NSCLC) and the anticancer effect of the LRS inhibitor, BC-LI-0186, was evaluated. Methods: LRS expression and the antitumor effect of BC-LI-0186 were evaluated by immunohistochemical staining, immunoblotting, and live cell imaging. The in vivo antitumor effect of BC-LI-0186 was evaluated using Lox-Stop-Lox (LSL) K-ras G12D mice. Results: LRS was frequently overexpressed in NSCLC tissues, and its expression was positively correlated with mTORC1 activity. The guanosine-5’-triphosphate (GTP) binding status of RagB was related to the expression of LRS and the S6K phosphorylation. si RNA against LRS inhibited leucine-mediated mTORC1 activation and cell growth. BC-LI-0186 selectively inhibited phosphorylation of S6K without affecting phosphorylation of AKT and leucine-mediated co-localization of Raptor and LAMP2 in the lysosome. BC-LI-0186 induced cleaved poly (ADP-ribose) polymerase (PARP) and caspase-3 and increase of p62 expression, showing that it has the autophagy-inducing property. BC-LI-0186 has the cytotoxic effect at nanomolar concentration and its GI 50 value was negatively correlated with the degree of LRS expression. BC-LI-0186 showed the antitumor effect, which was comparable with that of cisplatin, and mTORC1 inhibitory effect in a lung cancer model. Conclusions: BC-LI-0186 inhibits the noncanonical mTORC1-activating function of LRS. These results provide a new therapeutic strategy for NSCLC and warrant future clinical development by targeting LRS.

BackgroundImatinib mesylate (IM) is a first-line treatment option for patients with chronic myeloid leukemia (CML). Patients who fail or are intolerant to IM therapy are treated with more expensive second and third-generation tyrosine kinase inhibitors. Patients show wide variation in trough concentrations in response to standard dosing. Thus, many patients receive subtherapeutic or supratherapeutic doses. Therapeutic drug monitoring (TDM) may improve dose management that, in turn, may reduce costs and improve outcomes. However, TDM also adds to the cost of patient care. The objective of this study was to determine the cost-effectiveness of TDM for generic IM therapy.MethodsWe developed a microsimulation model for the trough plasma concentration of IM which is related to a cytogenetic or molecular response. We compared two cohorts: one with TDM and one without TDM (NTDM). The lifetime incremental cost-effectiveness ratio (ICER) was calculated using quality-adjusted life years (QALYs) as the effectiveness measure. One-way and probabilistic sensitivity analyses were performed.ResultsThe lifetime cost and QALY of treatment with TDM were $2,137K [95% Ci: 2,079K; 2,174K] and 12.37 [95% CI: 12.07; 12.55], respectively. The cost and QALY of NTDM were $2,132K [95% CI: 2,091K; 2,197K] and 12.23 [95% CI: 11.96; 12.50], respectively. The incremental cost and QALY for TDM relative to NTDM was $4,417 [95% CI: -52,582; 32,097]) and 0.15 [95% CI: -0.13; 0.28]. The ICER for TDM relative to NTDM was $30,450/QALY. Probabilistic sensitivity analysis showed that TDM was cost-effective relative to NTDM in 90% of the tested scenarios at a willingness-to-pay threshold of $100,000/QALY.ConclusionsAlthough the impact of TDM is modest, the cost-effectiveness over a lifetime horizon (societal perspective, ($30,450/QALY) falls within the acceptable range (< $100k/QALY).

To prevent disasters and to control and supervise crowds, automated video surveillance has become indispensable. In today’s complex and crowded environments, manual surveillance and monitoring systems are inefficient, labor intensive, and unwieldy. Automated video surveillance systems offer promising solutions, but challenges remain. One of the major challenges is the extraction of true foregrounds of pixels representing humans only. Furthermore, to accurately understand and interpret crowd behavior, human crowd behavior (HCB) systems require robust feature extraction methods, along with powerful and reliable decision-making classifiers. In this paper, we describe our approach to these issues by presenting a novel Particles Force Model for multi-person tracking, a vigorous fusion of global and local descriptors, along with a robust improved entropy classifier for detecting and interpreting crowd behavior. In the proposed model, necessary preprocessing steps are followed by the application of a first distance algorithm for the removal of background clutter; true-foreground elements are then extracted via a Particles Force Model. The detected human forms are then counted by labeling and performing cluster estimation, using a K-nearest neighbors search algorithm. After that, the location of all the human silhouettes is fixed and, using the Jaccard similarity index and normalized cross-correlation as a cost function, multi-person tracking is performed. For HCB detection, we introduced human crowd contour extraction as a global feature and a particles gradient motion (PGD) descriptor, along with geometrical and speeded up robust features (SURF) for local features. After features were extracted, we applied bat optimization for optimal features, which also works as a pre-classifier. Finally, we introduced a robust improved entropy classifier for decision making and automated crowd behavior detection in smart surveillance systems. We evaluated the performance of our proposed system on a publicly available benchmark PETS2009 and UMN dataset. Experimental results show that our system performed better compared to existing well-known state-of-the-art methods by achieving higher accuracy rates. The proposed system can be deployed to great benefit in numerous public places, such as airports, shopping malls, city centers, and train stations to control, supervise, and protect crowds.

Advances in video capturing devices enable adaptive posture estimation (APE) and event classification of multiple human-based videos for smart systems. Accurate event classification and adaptive posture estimation are still challenging domains, although researchers work hard to find solutions. In this research article, we propose a novel method to classify stochastic remote sensing events and to perform adaptive posture estimation. We performed human silhouette extraction using the Gaussian Mixture Model (GMM) and saliency map. After that, we performed human body part detection and used a unified pseudo-2D stick model for adaptive posture estimation. Multifused data that include energy, 3D Cartesian view, angular geometric, skeleton zigzag and moveable body parts were applied. Using a charged system search, we optimized our feature vector and deep belief network. We classified complex events, which were performed over sports videos in the wild (SVW), Olympic sports, UCF aerial action dataset and UT-interaction datasets. The mean accuracy of human body part detection was 83.57% over the UT-interaction, 83.00% for the Olympic sports and 83.78% for the SVW dataset. The mean event classification accuracy was 91.67% over the UT-interaction, 92.50% for Olympic sports and 89.47% for SVW dataset. These results are superior compared to existing state-of-the-art methods.

Object recognition in depth images is challenging and persistent task in machine vision, robotics, and automation of sustainability. Object recognition tasks are a challenging part of various multimedia technologies for video surveillance, human–computer interaction, robotic navigation, drone targeting, tourist guidance, and medical diagnostics. However, the symmetry that exists in real-world objects plays a significant role in perception and recognition of objects in both humans and machines. With advances in depth sensor technology, numerous researchers have recently proposed RGB-D object recognition techniques. In this paper, we introduce a sustainable object recognition framework that is consistent despite any change in the environment, and can recognize and analyze RGB-D objects in complex indoor scenarios. Firstly, after acquiring a depth image, the point cloud and the depth maps are extracted to obtain the planes. Then, the plane fitting model and the proposed modified maximum likelihood estimation sampling consensus (MMLESAC) are applied as a segmentation process. Then, depth kernel descriptors (DKDES) over segmented objects are computed for single and multiple object scenarios separately. These DKDES are subsequently carried forward to isometric mapping (IsoMap) for feature space reduction. Finally, the reduced feature vector is forwarded to a kernel sliding perceptron (KSP) for the recognition of objects. Three datasets are used to evaluate four different experiments by employing a cross-validation scheme to validate the proposed model. The experimental results over RGB-D object, RGB-D scene, and NYUDv1 datasets demonstrate overall accuracies of 92.2%, 88.5%, and 90.5% respectively. These results outperform existing state-of-the-art methods and verify the suitability of the method.

Nowadays, wearable technology can enhance physical human life-log routines by shifting goals from merely counting steps to tackling significant healthcare challenges. Such wearable technology modules have presented opportunities to acquire important information about human activities in real-life environments. The purpose of this paper is to report on recent developments and to project future advances regarding wearable sensor systems for the sustainable monitoring and recording of human life-logs. On the basis of this survey, we propose a model that is designed to retrieve better information during physical activities in indoor and outdoor environments in order to improve the quality of life and to reduce risks. This model uses a fusion of both statistical and non-statistical features for the recognition of different activity patterns using wearable inertial sensors, i.e., triaxial accelerometers, gyroscopes and magnetometers. These features include signal magnitude, positive/negative peaks and position direction to explore signal orientation changes, position differentiation, temporal variation and optimal changes among coordinates. These features are processed by a genetic algorithm for the selection and classification of inertial signals to learn and recognize abnormal human movement. Our model was experimentally evaluated on four benchmark datasets: Intelligent Media Wearable Smart Home Activities (IM-WSHA), a self-annotated physical activities dataset, Wireless Sensor Data Mining (WISDM) with different sporting patterns from an IM-SB dataset and an SMotion dataset with different physical activities. Experimental results show that the proposed feature extraction strategy outperformed others, achieving an improved recognition accuracy of 81.92%, 95.37%, 90.17%, 94.58%, respectively, when IM-WSHA, WISDM, IM-SB and SMotion datasets were applied.

The classification of human activity is becoming one of the most important areas of human health monitoring and physical fitness. With the use of physical activity recognition applications, people suffering from various diseases can be efficiently monitored and medical treatment can be administered in a timely fashion. These applications could improve remote services for health care monitoring and delivery. However, the fixed health monitoring devices provided in hospitals limits the subjects’ movement. In particular, our work reports on wearable sensors that provide remote monitoring that periodically checks human health through different postures and activities to give people timely and effective treatment. In this paper, we propose a novel human activity recognition (HAR) system with multiple combined features to monitor human physical movements from continuous sequences via tri-axial inertial sensors. The proposed HAR system filters 1D signals using a notch filter that examines the lower/upper cutoff frequencies to calculate the optimal wearable sensor data. Then, it calculates multiple combined features, i.e., statistical features, Mel Frequency Cepstral Coefficients, and Gaussian Mixture Model features. For the classification and recognition engine, a Decision Tree classifier optimized by the Binary Grey Wolf Optimization algorithm is proposed. The proposed system is applied and tested on three challenging benchmark datasets to assess the feasibility of the model. The experimental results show that our proposed system attained an exceptional level of performance compared to conventional solutions. We achieved accuracy rates of 88.25%, 93.95%, and 96.83% over MOTIONSENSE, MHEALTH, and the proposed self-annotated IM-AccGyro human-machine dataset, respectively.

A concentric annular heat pipe heat sink (AHPHS) was proposed and fabricated to investigate its thermal behavior. The present AHPHS consists of two concentric pipes of different diameters, which create vacuumed annular vapor space. The main advantage of the AHPHS as a heat sink is that it can largely increase the heat transfer area for cooling compared to conventional heat pipes. In the current AHPHS, condensation takes place along the whole annular space from the certain heating area as the evaporator section. Therefore, the whole inner space of the AHPHS except the heating area can be considered the condenser. In the present study, AHPHSs of different diameters were fabricated and studied experimentally. Basic studies were carried out with a 50 mm-long stainless steel AHPHS with diameter ratios of 1.1 and 1.3 and the same inner tube diameter of 76 mm. Several experimental parameters such as volume fractions of 10–70%, different air flow velocity, flow configurations, and 10–50 W heat inputs were investigated to find their effects on the thermal performance of an AHPHS. Experimental results show that a 10% filling ratio was found to be the optimum charged amount in terms of temperature profile with a low heater surface temperature and water as the working fluid. For the methanol, a 40% filling ratio shows better temperature behavior. Internal working behavior shows not only circular motion but also 3-D flow characteristics moving in axial and circular directions simultaneously.

Recent developments in sensor technologies enable physical activity recognition (PAR) as an essential tool for smart health monitoring and for fitness exercises. For efficient PAR, model representation and training are significant factors contributing to the ultimate success of recognition systems because model representation and accurate detection of body parts and physical activities cannot be distinguished if the system is not well trained. This paper provides a unified framework that explores multidimensional features with the help of a fusion of body part models and quadratic discriminant analysis which uses these features for markerless human pose estimation. Multilevel features are extracted as displacement parameters to work as spatiotemporal properties. These properties represent the respective positions of the body parts with respect to time. Finally, these features are processed by a maximum entropy Markov model as a recognition engine based on transition and emission probability values. Experimental results demonstrate that the proposed model produces more accurate results compared to the state-of-the-art methods for both body part detection and for physical activity recognition. The accuracy of the proposed method for body part detection is 90.91% on a University of Central Florida’s (UCF) sports action dataset and, for activity recognition on a UCF YouTube action dataset and an IM-DailyRGBEvents dataset, accuracy is 89.09% and 88.26% respectively.

Boil-off gas (BOG) disposal in liquefied natural gas (LNG) tankers has long been considered inevitable owing to the constant vaporization of the LNG in the storage tanks, but results in energy waste and environmental pollution. To address these challenges, we developed a re-liquification system that can condense the BOG and return it to the storage tank. The re-liquification system was modeled, and a case study was conducted to evaluate the viability of the system. The energy waste, which was quantified by tonnes of oil equivalent (TOE), greenhouse-gas emissions in tonnes of carbon dioxide (TCO2), and cost reduction in millions of U.S. dollars (MUSD), was evaluated for five different tanker cruising speeds. The re-liquification system significantly reduced the average TOE, TCO2, and cost by up to 9120.40 TOE/year, 19,474.33 TCO2/year, and 1.9765 MUSD/year, respectively, for five different tanker speeds with multi-stage compression.

Automatic identification of human interaction is a challenging task especially in dynamic environments with cluttered backgrounds from video sequences. Advancements in computer vision sensor technologies provide powerful effects in human interaction recognition (HIR) during routine daily life. In this paper, we propose a novel features extraction method which incorporates robust entropy optimization and an efficient Maximum Entropy Markov Model (MEMM) for HIR via multiple vision sensors. The main objectives of proposed methodology are: (1) to propose a hybrid of four novel features—i.e., spatio-temporal features, energy-based features, shape based angular and geometric features—and a motion-orthogonal histogram of oriented gradient (MO-HOG); (2) to encode hybrid feature descriptors using a codebook, a Gaussian mixture model (GMM) and fisher encoding; (3) to optimize the encoded feature using a cross entropy optimization function; (4) to apply a MEMM classification algorithm to examine empirical expectations and highest entropy, which measure pattern variances to achieve outperformed HIR accuracy results. Our system is tested over three well-known datasets: SBU Kinect interaction; UoL 3D social activity; UT-interaction datasets. Through wide experimentations, the proposed features extraction algorithm, along with cross entropy optimization, has achieved the average accuracy rate of 91.25% with SBU, 90.4% with UoL and 87.4% with UT-Interaction datasets. The proposed HIR system will be applicable to a wide variety of man–machine interfaces, such as public-place surveillance, future medical applications, virtual reality, fitness exercises and 3D interactive gaming.

In recent years, interest in scene classification of different indoor-outdoor scene images has increased due to major developments in visual sensor techniques. Scene classification has been demonstrated to be an efficient method for environmental observations but it is a challenging task considering the complexity of multiple objects in scenery images. These images include a combination of different properties and objects i.e., (color, text, and regions) and they are classified on the basis of optimal features. In this paper, an efficient multiclass objects categorization method is proposed for the indoor-outdoor scene classification of scenery images using benchmark datasets. We illustrate two improved methods, fuzzy c-mean and mean shift algorithms, which infer multiple object segmentation in complex images. Multiple object categorization is achieved through multiple kernel learning (MKL), which considers local descriptors and signatures of regions. The relations between multiple objects are then examined by intersection over union algorithm. Finally, scene classification is achieved by using Multi-class Logistic Regression (McLR). Experimental evaluation demonstrated that our scene classification method is superior compared to other conventional methods, especially when dealing with complex images. Our system should be applicable in various domains such as drone targeting, autonomous driving, Global positioning systems, robotics and tourist guide applications.

Advancements in wearable sensors technologies provide prominent effects in the daily life activities of humans. These wearable sensors are gaining more awareness in healthcare for the elderly to ensure their independent living and to improve their comfort. In this paper, we present a human activity recognition model that acquires signal data from motion node sensors including inertial sensors, i.e., gyroscopes and accelerometers. First, the inertial data is processed via multiple filters such as Savitzky–Golay, median and hampel filters to examine lower/upper cutoff frequency behaviors. Second, it extracts a multifused model for statistical, wavelet and binary features to maximize the occurrence of optimal feature values. Then, adaptive moment estimation (Adam) and AdaDelta are introduced in a feature optimization phase to adopt learning rate patterns. These optimized patterns are further processed by the maximum entropy Markov model (MEMM) for empirical expectation and highest entropy, which measure signal variances for outperformed accuracy results. Our model was experimentally evaluated on University of Southern California Human Activity Dataset (USC-HAD) as a benchmark dataset and on an Intelligent Mediasporting behavior (IMSB), which is a new self-annotated sports dataset. For evaluation, we used the “leave-one-out” cross validation scheme and the results outperformed existing well-known statistical state-of-the-art methods by achieving an improved recognition accuracy of 91.25%, 93.66% and 90.91% when compared with USC-HAD, IMSB, and Mhealth datasets, respectively. The proposed system should be applicable to man–machine interface domains, such as health exercises, robot learning, interactive games and pattern-based surveillance.