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Purpose of reviewOrganosulfur compounds are intentionally added to natural gas as malodorants with the intent of short-term nasal inhalation to aid in leak detection. Regulatory exposure limits have not been established for all commonly used natural gas odorants, and recent community-level exposure events and growing evidence of indoor natural gas leakage have raised concerns associated with natural gas odorant exposures. We conducted a scoping review of peer-reviewed scientific publications on human exposures and animal toxicological studies of natural gas odorants to assess toxicological profiles, exposure potential, health effects and regulatory guidelines associated with commonly used natural gas odorants.Recent findingsWe identified only 22 studies which met inclusion criteria for full review. Overall, there is limited evidence of both transient nonspecific health symptoms and clinically diagnosed causative neurotoxic effects associated with prolonged odorant exposures. Across seven community-level exposure events and two occupational case reports, consistent symptom patterns included: headache, ocular irritation, nose and throat irritation, respiratory complaints such as shortness of breath and asthma attacks, and skin irritation and rash. Of these, respiratory inflammation and asthma exacerbations are the most debilitating, whereas the high prevalence of ocular and dermatologic symptoms suggest a non-inhalation route of exposure. The limited evidence available raises the possibility that organosulfur odorants may pose health risks at exposures much lower than presently understood, though additional dose-response studies are needed to disentangle specific toxicologic effects from nonspecific responses to noxious organosulfur odors. Numerous recommendations are provided including more transparent and prescriptive natural gas odorant use practices.

Background Tuberculosis (TB) remains one of the top infectious killers in the world and a prominent fatal disease in developing countries. This study proposes a prototypical solution to early prevention of TB based on its primary symptoms, signs, and risk factors, implemented by means of machine learning (ML) predictive algorithms. Further novelty of the study lies in the uniqueness of patient dataset collected from three top-ranked hospitals of Sindh, Pakistan, via a self-administered survey patient-records that comprises a set of questions asked by the doctors treating TB patients in real-time. A total of 1,200 survey patient-records were evenly distributed among all three hospitals, viz. ICT Kotri, LUMHS Jamshoro, and Civil Hospital Hyderabad. Methods To develop the required prototypes, the research made use of five distinct benchmark ML algorithms: decision tree (DT), Gaussian naive Bayes (GNB), logistic regression classifier (LRC), adaptive boosting (AdaBoost), and neural network (NN), whose performance was evaluated by considering various performance metrics, i.e., accuracy, precision, recall, F1 score, and confusion matrix. Results The experimental results, graphically visualized and systematically discoursed, demonstrate that early detection of TB classifiers, including DT, GNB, LRC, AdaBoost, and NN, attained accuracy rates of 92.11%, 89.04%, 90.35%, 93.42%, and 92.98%, respectively. These results indicate effective diagnosis of TB disease by each implemented ML algorithm.

Tightly-coupled HPC systems have rigid memory allocation and can result in expensive memory resource underutilization. As novel memory and network technologies mature, disaggregated memory systems are becoming a promising solution for future HPC systems. It allows workloads to use the available memory of the entire system. In this paper, we propose a design framework to explore the disaggregated memory system design space. The framework incorporates memory capacity, network bandwidth, and local and remote memory access ratio, and provides an intuitive approach to guide machine configurations based on technology trends and workload characteristics. We apply our framework to analyze eleven workloads from five computational scenarios, including AI training, data analysis, genomics, protein, and traditional HPC. We demonstrate the ability of our methodology to understand the potential and pitfalls on a disaggregated memory system and motivate machine configurations. Our methodology shows that the 10 out of our 11 applications/workflows can leverage disaggregated memory without affecting performance.

Hardware specialization is a promising direction for the future of digital computing. Reconfigurable technologies enable hardware specialization with modest non-recurring engineering cost, but their performance and energy efficiency compared to state-of-the-art processor architectures remain an open question. In this article, we use FPGAs to evaluate the benefits of building specialized hardware for numerical kernels found in scientific applications. In order to properly evaluate performance, we not only compare Intel Arria 10 and Xilinx U280 performance against Intel Xeon, Intel Xeon Phi, and NVIDIA V100 GPUs, but we also extend the Empirical Roofline Toolkit (ERT) to FPGAs in order to assess our results in terms of the Roofline model. We show design optimization and tuning techniques for peak FPGA performance at reasonable hardware usage and power consumption. As FPGA peak performance is known to be far less than that of a GPU, we also benchmark the energy efficiency of each platform for the scientific kernels comparing against microbenchmark and technological limits. Results show that while FPGAs struggle to compete in absolute terms with GPUs on memory- and compute-intensive kernels, they require far less power and can deliver nearly the same energy efficiency.

The Roofline performance model provides an intuitive approach to identify performance bottlenecks and guide performance optimization. However, the classic FLOP-centric approach is inappropriate for the emerging applications that perform more integer operations than floating point operations. In this article, we reintroduce our Instruction Roofline Model on NVIDIA GPUs and expand our evaluation of it. The Instruction Roofline incorporates instructions and memory transactions across all memory hierarchies together, and provides more performance insights than the FLOP-oriented Roofline Model, that is, instruction throughput, stride memory access patterns, bank conflicts, and thread predication. We use our Instruction Roofline methodology to analyze eight proxy applications: HPGMG from AMReX, Matrix Transpose benchmarks, ADEPT from MetaHipMer's sequence alignment phase, EXTENSION from MetaHipMer's local assembly phase, CUSP, cuSPARSE, cudaTensorCoreGemm, and cuBLAS. We demonstrate the ability of our methodology to understand various aspects of performance and performance bottlenecks on NVIDIA GPUs and motivate code optimizations.

Amputations are a prominent affliction that occur worldwide, with causes ranging from congenital, disease-based, or external reasons such as trauma. Prosthesis provides the closest alternative functional replacement to the loss of a limb. Before any form of rehabilitation support can be offered to amputee patients, an assessment of their degree and level of mobility first needs to be evaluated using the K-level grading system. The typical means towards the assigning of a K-level grading is through qualitative methods, which have been criticized for being subjective and, at times, imprecise. As a means towards remedying this shortcoming, we investigated the prospect of utilizing data from wearable sensors for analyzing the stride pattern and cadence of various subjects towards the quantitative inference of a K-level. This was accomplished using data from accelerometers, alongside advanced signal processing and machine learning models, towards the quantitative identification and differentiation of the various K-levels of amputees of varied levels of mobility. The experimental results showed that this aim could be accomplished under the circumstance investigated and the models applied as part of this research. Additional analysis was also done on the use of data from accelerometers towards the differentiation between amputated and non-amputated subjects, which showed that the cohorts could be classified and differentiated using purely accelerometer data and the accompanying postprocessing methods.

Fish stocking occurs in aquatic systems for conservation purposes, to create or enhance recreational fisheries and to enhance wild-catch commercial fisheries. Identifying and quantifying the contribution of stocking efforts to wild populations is crucial to informing these management objectives. Provenance determination methods trade off accuracy, replicability, and costeffectiveness at fishery-relevant scales. We present and assess multiple methods for provenance determination using a case study of barramundi (Lates calcarifer) in the Dry Tropics region of northern Australia. A novel application of near-infrared spectroscopy (NIRS) is compared to two established methods for fish provenance, otolith microchemistry and genetic parentage analysis using microsatellites. The otolith microchemistry method was able to provide extremely high provenance resolution (>99% accuracy). The microsatellite parentage analysis method had a slightly lower overall accuracy (95%), likely as a result of genetic introgression in this region. Provenance determination using otolith NIRS had the lowest overall accuracy (76%). Once limitations regarding spectral noise, image resolution, and sample size are addressed, NIRS may have potential for costeffectively determining provenance in fish. Key words: barramundi, Lates calcarifer, provenance, natal origin, fish stocking, stock enhancement, otolith microchemistry, NIRS, microsatellite parentage analysis, Introduction Fish stocking occurs in aquatic systems around the world for conservation purposes, to create or enhance recreational fisheries and to enhance wild-catch commercial fisheries (Warren-Myers et al. 2018). Identifying [...]

Abstract Surface electromyogram (sEMG) offers a rich set of motor information for decoding limb motion intention that serves as a control input to Intelligent human-machine synergy systems (IHMSS). Despite growing interest in IHMSS, the current publicly available datasets are limited and can hardly meet the growing demands of researchers. This study presents a novel lower limb motion dataset (designated as SIAT-LLMD), comprising sEMG, kinematic, and kinetic data with corresponding labels acquired from 40 healthy humans during 16 movements. The kinematic and kinetic data were collected using a motion capture system and six-dimensional force platforms and processed using OpenSim software. The sEMG data were recorded using nine wireless sensors placed on the subjects’ thigh and calf muscles on the left limb. Besides, SIAT-LLMD provides labels to classify the different movements and different gait phases. Analysis of the dataset verified the synchronization and reproducibility, and codes for effective data processing are provided. The proposed dataset can serve as a new resource for exploring novel algorithms and models for characterizing lower limb movements.

Deciphering hand motion intention from surface electromyography (sEMG) encounters challenges posed by the requisites of multiple degrees of freedom (DOFs) and adaptability. Unlike discrete action classification grounded in pattern recognition, the pursuit of continuous kinematics estimation is appreciated for its inherent naturalness and intuitiveness. However, prevailing estimation techniques contend with accuracy limitations and substantial computational demands. Kalman estimation technology, celebrated for its ease of implementation and real-time adaptability, finds extensive application across diverse domains. This study introduces a continuous Kalman estimation method, leveraging a system model with sEMG and joint angles as inputs and outputs. Facilitated by model parameter training methods, the approach deduces multiple DOF finger kinematics simultaneously. The method’s efficacy is validated using a publicly accessible database, yielding a correlation coefficient (CC) of 0.73. With over 45,000 windows for training Kalman model parameters, the average computation time remains under 0.01 s. This pilot study amplifies its potential for further exploration and application within the realm of continuous finger motion estimation technology.

Accurate and timely evaluation and assessment of emission data and its impact on environmental status has been a key challenge due to the conventional manual approach utilized for independently computing most emission parameters. To resolve this long-standing issue, we proposed an Artificial Intelligence (AI)-driven Decision Tree model to adequately classify Environmental Protection Agency (EPA) status based on multiple Emission Parameters. The model's performance was systematically evaluated using multiple emission parameters obtained from a two-stroke motorcycle dataset collected in Nigeria across various metrics such as K-S Statistics, Confusion Matrix, Correlation Heat Map, Decision Tree, Validation Curve, and Threshold Plot. The K-S Statistics plot's experimental results showed a considerable correlation between HC, CO, and the target variable, with values ranging from 0.75 to 0.80. At the same time, CO2 and O2 do not correlate with the target variable with values between 0.00 and 0.09. The Confusion Matrix revealed that the proposed model has an overall accuracy of 99.9% with 481 true positive predictions and 75 true negative predictions, indicating the effectiveness of the proposed AI-driven model. In conclusion, our proposed AI-driven model can effectively classify EPA status based on multiple emission parameters with high accuracy, which may spur positive advancement in policy enhancement for proper environmental management.