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After a series of gas pipeline explosions in Kaohsiung City, Taiwan, in 2014, the Environmental Protection Administration (EPA) issued regulations in 2017 requiring all chemical tank trucks to be equipped with GPS for real-time monitoring and control. Hazmat transportation problems, along with solution algorithms and applications, have been studied extensively. However, most of the research has focused on the route planning level. Dynamic characteristics should be incorporated to reflect possible dynamic traffic situations with more advanced information and communication capabilities for chemical tank trucks. This research formulates a bi-objective dynamic model and proposes a solution algorithm based on a genetic algorithm (GA) for dynamic hazmat route optimization. A dynamic bi-objective model, including transportation cost and risk, is developed to reflect time-varying traffic conditions. Dynamic traffic characteristics are reflected through traffic volume and travel time, simulated from simulation models. Numerical experiments are conducted in the Kaohsiung City network. The results show that dynamic routes for hazmat transportation vary with respect to traffic flow conditions. The discussions of this research are expected to provide some insights into policy implications.

BackgroundUsing Minnesota Multiphasic Personality Inventory-2 (MMPI-2) clinical scales to evaluate clinical symptoms in schizophrenia is a well-studied topic. Nonetheless, research focuses less on how these clinical scales interact with each other.AimsInvestigates the network structure and interaction of the MMPI-2 clinical scales between healthy individuals and patients with schizophrenia through the Bayesian network.MethodData was collected from Wuhan Psychiatric Hospital from March 2008 to May 2018. A total of 714 patients with schizophrenia and 714 healthy subjects were identified through propensity score matching according to the criteria of the International Classification of Diseases (ICD-11). Separated MMPI-2 clinical scales Bayesian networks were built for healthy subjects and patients with schizophrenia, respectively.ResultsThe Bayesian network showed that the lower 7 scale was a consequence of the correlation between the lower 2 scale and the greater 8 scale. A solely lower 7 scale does yield neither a lower 2 scale nor a higher 8 scale. The proposed method showed 72% of accuracy with 78% area under the ROC curve (AUC), similar to the previous studies.LimitationsThe proposed method simplified the continuous Bayesian network to predict binary outcomes, including other categorical data is not explored. Besides, the participants might only represent an endemic as they come from a single hospital.ConclusionThis study identified MMPI-2 clinical scales correlation and built separated Bayesian networks to investigate the difference between patients with schizophrenia and healthy people. These differences may contribute to a better understanding of the clinical symptoms of schizophrenia and provide medical professionals with new perspectives for diagnosis.

The niche variation hypothesis (NVH) predicts that populations with wider niches exhibit greater morphological variation through increased interindividual differences in both niche and morphology. In this study, we examined niche–trait relationships in three passerine species (Cyanoderma ruficeps, Sinosuthora webbiana, and Zosterops simplex). A total of 289 C. ruficeps from 7 sites, 259 S. webbiana from 8 sites, and 144 Z. simplex from 6 sites were sampled along an elevation gradient (0–2,700 m) in Taiwan from 2009 to 2017. We measured bill traits (length, width, and depth of bill) and body size traits (length of head, tarsus, and wing) of the birds, which were reduced to four principal components (bill PC1, bill PC2, body size PC1, and body size PC2). We collected feather tissues for stable carbon and nitrogen isotope analyses to quantify their isotope niche. We quantified interindividual differences in isotope space and trait space with four diversity metrics (divergence, dispersion, evenness, and uniqueness) and tested whether interindividual differences in isotope space and trait space are positively associated. We quantified population isotope niche width by Bayesian ellipse area and population morphological variation by variances of the PCs. The results showed that individual uniqueness in isotope niche and bill morphology (average closeness of individuals within the population isotope/trait space) were positively associated across three species. Furthermore, isotope niche width and bill PC1 (reflecting the size of bill) variation at population level were also positively associated across the three species, supporting the NVH. Of the three species, C. ruficeps and S. webbiana showed stronger support for the NVH than Z. simplex, possibly due to the latter having narrower elevational distribution and a more specialized, plant‐based diet. The diversity metrics represented different aspects of interindividual differences in niche/trait space, and for the passerines, individual uniqueness appeared to play an important role in their niche–trait dynamics., This study provided empirical evidence for the niche variation hypothesis in three passerine species (Cyanoderma ruficeps, Sinosuthora webbiana, and Zosterops simplex) in Taiwan. All three species showed positive associations between isotope niche width and bill size variation. Furthermore, all three species shared the same pattern that as individual birds occupied unique bill trait space within the population, they also tended to occupy unique isotope space, suggesting niche width expansion at population level through morphological differentiation at individual level.

On July 31, 2014, a series of gas pipeline explosions occurred in Kaohsiung City, causing 32 deaths and 321 injuries. Following this accident, the Kaohsiung City government decided to replace the use of pipelines with trucks for transporting hazmat, transfering the risk from beneath the road to the surface. This means that careful consideration needs to be given to safety and cost in hazardous material (hazmat) transportation route planning. The issue of how to design optimal routes for the transportation of hazmat is, therefore, important and involves consideration of various criteria. This research focuses on three objectives: cost, risk, and emergency response capability. It then constructs two solution algorithms, the compromise weight model and the evolutionary algorithm, to solve the multi-objective problem. The results from these two algorithms are observed and compared. In addition, this research also provides some recommendations for stakeholders, including the hazmat industry, government, and residents.

Post-stroke patients usually suffer from a higher fall risk. Identifying potential fallers and giving them proper attention could reduce their chance of a fall that results in severe injuries and decreased quality of life. In this study, we introduced a novel approach for fall risk prediction that evaluates Short-form Berg Balance Scale scores via inertial measurement unit data measured from a 3-meter timed-up-and-go test. This approach used sensor technology and was thus easy to implement, and allowed a quantitative analysis of both gait and balance. The results showed that elastic net logistic regression achieved the best performance with 85% accuracy and 88% area under the curve compared with support vector machine, least absolute shrinkage and selection operator (LASSO), and stepwise logistic regression. This paper provides a framework for using sensor-based features together with a feature-selection strategy for screening and predicting the fall risk of post-stroke patients in a convenient setup with high accuracy. The findings of this study will not only enable the assessment of fall risk among post-stroke patients in a cost-effective manner but also provide decision-making support for community care providers and medical professionals in the form of sensor-based data on gait performance.

The UV-C light emitting diode (LED) has shown numerous advantages over the traditional UV mercury lamp for water sterilization applications. Multi-chip LED array was used to provide sufficient UV fluence for bacteria inactivation in limited time. According to the point light source characteristic of LEDs, the arrangement of LEDs in the batch reactor is crucial to optimize the inactivation efficiency. In this study, the inactivation of

Characteristics of egg surfaces serve as recognition cues that allow avian hosts to detect and reject foreign eggs in brood parasitism. The blunt egg pole hypothesis suggests that the blunt egg pole is an essential signal in parasitic egg recognition. In the present study, eggs of Yellow-bellied Prinia (Prinia flaviventris) were painted with black spots at either the blunt or sharp egg pole to determine and compare the responses of different populations exposed to brood parasitism (parasitized population) or not (non-parasitized population). These results supported the above hypothesis and showed that the parasitized population displayed a higher rate of rejection of eggs with blunt pole-painted spots than with sharp pole-painted spots. In contrast, the non-parasitized population accepted all experimental eggs, which may be related to decreased egg recognition resulting from low exposure to brood parasitism.

Background Clinical mobility and balance assessments identify older adults who have a high risk of falls in clinics. In the past two decades, sensors have been a popular supplement to mobility and balance assessment to provide quantitative information and a cost-effective solution in the community environment. Nonetheless, the current sensor-based balance assessment relies on manual observation or motion-specific features to identify motions of research interest. Objective The objective of this study was to develop an automatic motion data analytics framework using signal data collected from an inertial sensor for balance activity analysis in community-dwelling older adults. Methods In total, 59 community-dwelling older adults (19 males and 40 females; mean age = 81.86 years, SD 6.95 years) were recruited in this study. Data were collected using a body-worn inertial measurement unit (including an accelerometer and a gyroscope) at the L4 vertebra of each individual. After data preprocessing and motion detection via a convolutional long short-term memory (LSTM) neural network, a one-class support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighborhood (k-NN) were adopted to classify high-risk individuals. Results The framework developed in this study yielded mean accuracies of 87%, 86%, and 89% in detecting sit-to-stand, turning 360°, and stand-to-sit motions, respectively. The balance assessment classification showed accuracies of 90%, 92%, and 86% in classifying abnormal sit-to-stand, turning 360°, and stand-to-sit motions, respectively, using Tinetti Performance Oriented Mobility Assessment-Balance (POMA-B) criteria by the one-class SVM and k-NN. Conclusions The sensor-based approach presented in this study provided a time-effective manner with less human efforts to identify and preprocess the inertial signal and thus enabled an efficient balance assessment tool for medical professionals. In the long run, the approach may offer a flexible solution to relieve the community’s burden of continuous health monitoring.

Dynamically self-balancing wheeled robots possess the characteristics of having a small footprint, low base-to-height ratios, high accelerations and speeds, and low costs. They are suitable for working in human-centric environments. As part of our ongoing effort in creating self-balancing wheeled robots, in this article, we reported the development of our latest model – $\mathrm{J}4.\beta$ . In contrast to the previous model – $\mathrm{J}4.\alpha$ , the new model has a greater dynamic mass-to-total ratio; thus, the acceleration and the ultimate speed are both increased. Here, the maximum speed of 4.4 m/s of the motion platform is achievable by $\mathrm{J}4.\beta$ . We analyzed the system dynamics and had confirmations from measurements; a speed servo system was developed based on PID control. To simplify the dynamics of the mobile robot, a stepper motor instead of a DC motor was adopted for the actuation of the dynamic mass; the overall controlled plant could be approximated as a second-order system. To acquire the PID coefficients, a series of road tests were performed in a common office building. A set of suitable PID coefficients was obtained and verified by three speeds: 0.5 m/s, 1 m/s, and 2 m/s. The speed curves exhibited fast ramp-up, low overshoot, setpoint matching, and low oscillation. For regulation testing, a zero speed was set and external disturbance was applied. The robot was witnessed to slow down rapidly and remain stationary without intensive oscillation. While constructing the autonomous navigation and remote control systems for the mobile robot, the sampling rate of the control system was largely upgraded to 4k Hz to achieve a better tracking and regulation ability.

Counting the repetition of human exercise and physical rehabilitation is a common task in rehabilitation and exercise training. The existing vision-based repetition counting methods less emphasize the concurrent motions in the same video. This work presents a vision-based human motion repetition counting applicable to counting concurrent motions through the skeleton location extracted from various pose estimation methods. The presented method was validated on the University of Idaho Physical Rehabilitation Movements Data Set (UI-PRMD), and MM-fit dataset. The overall mean absolute error (MAE) for mm-fit was 0.06 with off-by-one Accuracy (OBOA) 0.94. Overall MAE for UI-PRMD dataset was 0.06 with OBOA 0.95. We have also tested the performance in a variety of camera locations and concurrent motions with conveniently collected video with overall MAE 0.06 and OBOA 0.88. The proposed method provides a view-angle and motion agnostic concurrent motion counting. This method can potentially use in large-scale remote rehabilitation and exercise training with only one camera.

BACKGROUND Clinical mobility and balance assessments identify older adults who have a high risk of falls in clinics. In the past two decades, sensors have been a popular supplement to mobility and balance assessment to provide quantitative information and a cost-effective solution in the community environment. Nonetheless, the current sensor-based balance assessment relies on manual observation or motion-specific features to identify motions of research interest. OBJECTIVE The objective of this study was to develop an automatic motion data analytics framework using signal data collected from an inertial sensor for balance activity analysis in community-dwelling older adults. METHODS In total, 59 community-dwelling older adults (19 males and 40 females; mean age = 81.86 years, SD 6.95 years) were recruited in this study. Data were collected using a body-worn inertial measurement unit (including an accelerometer and a gyroscope) at the L4 vertebra of each individual. After data preprocessing and motion detection via a convolutional long short-term memory (LSTM) neural network, a one-class support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighborhood (k-NN) were adopted to classify high-risk individuals. RESULTS The framework developed in this study yielded mean accuracies of 87%, 86%, and 89% in detecting sit-to-stand, turning 360°, and stand-to-sit motions, respectively. The balance assessment classification showed accuracies of 90%, 92%, and 86% in classifying abnormal sit-to-stand, turning 360°, and stand-to-sit motions, respectively, using Tinetti Performance Oriented Mobility Assessment-Balance (POMA-B) criteria by the one-class SVM and k-NN. CONCLUSIONS The sensor-based approach presented in this study provided a time-effective manner with less human efforts to identify and preprocess the inertial signal and thus enabled an efficient balance assessment tool for medical professionals. In the long run, the approach may offer a flexible solution to relieve the community’s burden of continuous health monitoring.

Significance It is a tenet of modern biology that species adapt through natural selection to cope with the ever-changing environment. By comparing genetic variants between the island and mainland populations of a passerine, we inferred the related age of genetic variants across its entire genome and suggest that preexisting standing variants played the predominant role in local adaptation. Our findings not only resolve a long-standing fundamental problem in biology regarding the genetic sources of adaptation, but imply that the evolutionary potential of a population is highly associated with its preexisting genetic variation., What kind of genetic variation contributes the most to adaptation is a fundamental question in evolutionary biology. By resequencing genomes of 80 individuals, we inferred the origin of genomic variants associated with a complex adaptive syndrome involving multiple quantitative traits, namely, adaptation between high and low altitudes, in the vinous-throated parrotbill (Sinosuthora webbiana) in Taiwan. By comparing these variants with those in the Asian mainland population, we revealed standing variation in 24 noncoding genomic regions to be the predominant genetic source of adaptation. Parrotbills at both high and low altitudes exhibited signatures of recent selection, suggesting that not only the front but also the trailing edges of postglacial expanding populations could be subjected to environmental stresses. This study verifies and quantifies the importance of standing variation in adaptation in a cohort of genes, illustrating that the evolutionary potential of a population depends significantly on its preexisting genetic diversity. These findings provide important context for understanding adaptation and conservation of species in the Anthropocene.

Recently, massive deployment of Internet of Things (IoT) devices has accelerated digital transformation of modern society in various aspects. Naturally, how to ensure security of deployed IoT devices has become an important and practical issue. Several successful large-scale DDoS attacks were utilized vulnerabilities of firmware on IoT devices in the past couple of years. Therefore, effective and efficient firmware update solution on IoT devices is in demand. In this study, an automatic real-time firmware update framework based on blockchain technology and MQTT protocol is proposed. To support automatic real-time firmware update operation on deployed IoT devices, multiple MQTT servers are installed in corresponding blockchain nodes to execute firmware patch delivery operation by the design of our framework. Blockchain technology is used to record the original manufacturer of a published firmware patch for a dedicated IoT device and smart contract is adopted to discover MQTT service nodes in a blockchain network. Data integrity of a newly generated firmware patch is preserved by distributing the patch with its hashed value. A framework prototype is constructed and experiments are conducted. Based on the experimental results, the proposed framework can efficiently and securely deliver firmware patches to targeted gateways in real-time scale.

Background Obligate brood parasites exert strong selective pressure on target hosts. In response, hosts typically evolve anti-parasitism strategies, of which egg recognition is one of the most efficient. Generally, host egg-recognition capacity is determined using model eggs. Previous studies have shown that some host species, which are capable of detecting parasite eggs, do not reject model eggs. However, it is unknown that whether the reaction to model eggs varies among distinct populations of the same host in relation to the degree of parasitism pressure. Results Here, we compared the rejection frequencies of model eggs and real eggs between mainland and island populations of the plain prinia (Prinia inornata), which are respectively sympatric and allopatric with their brood parasite, the common cuckoo (Cuculus canorus). Our results indicated that the mainland and island populations rejected real eggs at similar rates, but rejected model eggs, which were similar in size to real eggs but heavier, at significantly different rates: the island population rejected fewer model eggs, possibly because the rejection motivation of this population was lower due to absence of parasitism. Conclusions Our results indicated that some factors affecting the decision to reject, such as rejection motivation, varied according to the degree of parasitism pressure, and thus influenced the frequency of egg rejection. Furthermore, our results suggested that model eggs should be used with caution in comparative studies of egg recognition abilities among species or populations subjected to different intensities of brood parasitism. That is, model eggs may fail to accurately detect egg recognition in host populations with little to no risk of parasitism.

To support various network services, routers have to classify packets based on header fields and a set of filters. Each filter contains several fields to specify ranges of header values, and an associated action, which specifies how to handle the packet matching the filter. If two filters overlap and the actions defined in the filters conflict, it may lead to a false classification. In this paper, we propose a fast bit-vector-based algorithm for conflict detection. Experimental results show that our algorithm can reduce the average detection time per filter by 22.6% to 77.9% compared to the scalable bit vector (SBV) algorithm. Moreover, our algorithm does not consume significant extra memory and pre-computation time.

Recently, perovskites have attracted intense attention due to their high potential in optoelectronic applications. Employing perovskites as the emissive materials of light-emitting electrochemical cells (LECs) shows the advantages of simple fabrication process, low-voltage operation, and compatibility with inert electrodes, along with saturated electroluminescence (EL) emission. Unlike in previously reported perovskite LECs, in which salts are incorporated in the emissive layer, the ion-transport layer was separated from the emissive layer in this work. The layer of ionic transition metal complex (iTMC) not only provides mobile ions but also serves as an electron-injection/transport layer. Orthogonal solvents are used in spin coating to prevent the intermixing of stacked perovskite and iTMC layers. The blue iTMC with high ionization potential is effective in blocking holes from the emissive layer and thus ensures EL color saturation. In addition, the carrier balance of the perovskite/iTMC LECs can be optimized by adjusting the iTMC layer thickness. The optimized external quantum efficiency of the CsPbBr3 /iTMC LEC reaches 6.8 %, which is among the highest reported values for perovskite LECs. This work successfully demonstrates that, compared with mixing all components in a single emissive layer, separating the layer of ion transport, electron injection and transport from the perovskite emissive layer is more effective in adjusting device carrier balance. As such, solution-processable perovskite/iTMC LECs open up a new way to realize efficient perovskite LECs.