Morphological and Transcriptomic Analyses Reveal the Involvement of Key Metabolic Pathways in Male Sterility in Chimonanthus praecox (L.) Genotypes.

Chimonanthus praecox (Calycanthaceae family) is a unique ornamental and economic flowering tree in China, and after thousands of years of cultivation, it has produced several varieties and varietal types. Notably, male sterility is common in flowering plants and is an important tool for the genetic improvement in plants and optimization using hybrid plant technology; however, there have been no reports on male-sterile material or related studies on C. praecox. To our knowledge, this is the first time that C. praecox male sterility is dissected unveiling the involvement of key metabolic pathways. Notably, male sterility in C. praecox was observed during the budding period and likely occurred during the premature stage of pollen cell maturation. Additionally, differentially expressed genes in the starch and sucrose metabolism pathway and the plant hormone signal transduction pathway showed regular expression trends. This study reports on significant genetic differences that contribute to male sterility in C. praecox and provides a basis for further research and breeding strategies.

Establishment of Novel Simple Sequence Repeat (SSR) Markers from Chimonanthus praecox Transcriptome Data and Their Application in the Identification of Varieties.

Chimonanthus praecox, a member of the Calycanthaceae family, is a unique, traditional, and famous flowering economic tree species in China. Despite the existence of several varieties, only a few cultivars have been formally named. Currently, expression sequence tag-simple sequence repeat (EST-SSR) markers are extensively used to identify different species and varieties; a large number of microsatellites can be identified from transcriptome databases. A total of 162,638 unigenes were assembled using RNA-seq; 82,778 unigenes were annotated using the Nr, Nt, Swiss-Prot, Pfam, GO, KOG, and KEGG databases. In total, 13,556 SSR loci were detected from 11,691 unigenes, with trinucleotide repeat motifs being the most abundant among the six repeat motifs. To develop the markers, 64,440 pairs of SSR primers with polymorphism potential were designed, and 75 pairs of primers were randomly selected for amplification. Among these markers, seven pairs produced amplified fragments of the expected size with high polymorphism. Using these markers, 12 C. praecox varieties were clustered into two monophyletic clades. Microsatellites in the transcriptome of C. praecox exhibit rich types, strong specificity, and great polymorphism potential. These EST-SSR markers serve as molecular technical methods for identifying different varieties of C. praecox and facilitate the exploration of a large number of candidate genes associated with important traits.

Carbon storage estimation and strategy optimization under low carbon objectives for urban attached green spaces.

In the context of rapid urbanization, scarce land resources have highlighted the importance of attached green spaces (AGS), which have received limited attention despite their critical importance in the urban carbon cycle. Analyzing the differences in carbon storage of different AGS types and proposing different optimization strategies can provide a reference for urban managers to scientifically enhance the carbon sinks of greenfield. This study estimated the carbon storage of AGS in central Shanghai using a series of detailed field survey data and remote sensing data. The results showed that AGS stored 296 Gg (1Gg = 109 g) of carbon, accounting for 56 % of the total carbon storage in all urban green spaces. While carbon density in AGS is lower than that in other green space types due to the dominance of small trees, simple vegetation structures and poor maintenance in some areas. Significant differences in carbon storage capacity were also found among different types of AGS, related to individual vegetation attributes and vegetation structure. Our findings emphasized that optimal design strategies should be different for various green space types, and proposed corresponding optimization strategies for different AGS types. Furthermore, the dual role of multi-layered planting structures in carbon sinks and biodiversity was also emphasized.

Longitudinal changes in blood pressure and fasting plasma glucose among 5,398 primary care patients with concomitant hypertension and diabetes: An observational study and implications for community-based cardiovascular prevention.

Aims: To assess longitudinal changes in blood pressure (BP) and fasting plasma glucose (FPG) in primary care patients with concomitant hypertension and type 2 diabetes mellitus (T2DM), and to explore factors associated with patients' inability to improve BP and FPG at follow-up. Methods: We constructed a closed cohort in the context of the national basic public health (BPH) service provision in an urbanised township in southern China. Primary care patients who had concomitant hypertension and T2DM were retrospectively followed up from 2016 to 2019. Data were retrieved electronically from the computerised BPH platform. Patient-level risk factors were explored using multivariable logistic regression analysis. Results: We included 5,398 patients (mean age 66 years; range 28.9 to 96.1 years). At baseline, almost half [48.3% (2,608/5,398)] of patients had uncontrolled BP or FPG. During follow-up, more than one-fourth [27.2% (1,467/5,398)] of patients had no improvement in both BP and FPG. Among all patients, we observed significant increases in systolic BP [2.31 mmHg, 95% confidence interval (CI): 2.04 to 2.59, p < 0.001], diastolic BP (0.73 mmHg, 0.54 to 0.92, p < 0.001), and FPG (0.12 mmol/l, 0.09 to 0.15, p < 0.001) at follow-up compared to baseline. In addition to changes in body mass index [adjusted odds ratio (aOR)=1.045, 1.003 to 1.089, p = 0.037], poor adherence to lifestyle advice (aOR = 1.548, 1.356 to 1.766, p < 0.001), and unwillingness to actively enrol in health-care plans managed by the family doctor team (aOR = 1.379, 1.128 to 1.685, p = 0.001) were factors associated with no improvement in BP and FPG at follow-up. Conclusion: A suboptimal control of BP and FPG remains an ongoing challenge to primary care patients with concomitant hypertension and T2DM in real-world community settings. Tailored actions aiming to improve patients' adherence to healthy lifestyles, expand the delivery of team-based care, and encourage weight control should be incorporated into routine healthcare planning for community-based cardiovascular prevention.

Association between METS-IR and Prediabetes or Type 2 Diabetes Mellitus among Elderly Subjects in China: A Large-Scale Population-Based Study.

The metabolic score for insulin resistance (METS-IR) was recently proposed as a non-insulin-based, novel index for assessing insulin resistance (IR) in the Western population. However, evidence for the link between METS-IR and prediabetes or type 2 diabetes mellitus (T2DM) among the elderly Chinese population was still limited. We aimed to investigate the associations between METS-IR and prediabetes or T2DM based on large-scale, cross-sectional, routine physical examination data. In a total of 18,112 primary care service users, an increased METS-IR was independently associated with a higher prevalence of prediabetes (adjusted odds ratio [aOR] = 1.457, 95% confidence interval [CI]: 1.343 to 1.581, p < 0.001) and T2DM (aOR = 1.804, 95%CI: 1.720 to 1.891, p < 0.001), respectively. The aOR for prediabetes in subjects with the highest quartile of METS-IR was 3.060-fold higher than that in those with the lowest quartile of METS-IR. The aOR for T2DM in subjects with the highest quartile of METS-IR was 6.226-fold higher than that in those with the lowest quartile of METS-IR. Consistent results were obtained in subgroup analyses. Our results suggested that METS-IR was significantly associated with both prediabetes and T2DM. The monitoring of METS-IR may add value to early identification of individuals at risk for glucose metabolism disorders in primary care.

Ship Fire Detection Based on an Improved YOLO Algorithm with a Lightweight Convolutional Neural Network Model.

Ship fire is one of the greatest dangers to ship navigation safety. Nevertheless, typical detection methods have limited detection effectiveness and accuracy due to distance restrictions and ship motion. Although the issue can be addressed by image recognition algorithms based on deep learning, the computational complexity and efficiency for ship detection are tough. This paper proposes a lightweight target identification technique based on the modified YOLOv4-tiny algorithm for the precise and efficient detection of ship fires, taking into account the distinctive characteristics of ship fires and the marine environment. Initially, a multi-scale detection technique is applied to broaden the detection range and integrate deep semantic information, thereby enhancing the feature information of small targets and obscured objects and improving the detection precision. Then, the proposed algorithm employs the SE attention mechanism for inter-channel feature fusion to improve the capability of feature extraction and the precision of ship fire detection. Last but not least, picture transformation and migration learning are added to the small ship fire dataset to accelerate the convergence pace, improve the convergence effect, and reduce dataset dependence. The simulation experiments reveal that the proposed I-YOLOv4-tiny + SE model outperforms the benchmark algorithm in terms of ship fire detection accuracy and detection efficiency and that it satisfies the real-time ship fire warning criteria in demanding maritime environments.

A tandem CCCH type zinc finger protein gene CpC3H3 from Chimonanthus praecox promotes flowering and enhances drought tolerance in Arabidopsis.

BACKGROUND: CCCH-type zinc finger proteins play important roles in plant development and biotic/abiotic stress responses. Wintersweet (Chimonanthus praecox) is a popular ornamental plant with strong resistance to various stresses, which is a good material for exploring gene resource for stress response. In this study, we isolated a CCCH type zinc finger protein gene CpC3H3 (MZ964860) from flower of wintersweet and performed functional analysis with a purpose of identifying gene resource for floral transition and stress tolerance. RESULTS: CpC3H3 was predicted a CCCH type zinc finger protein gene encoding a protein containing 446 amino acids with five conserved C-X8-C-X5-C-X3-H motifs. CpC3H3 was localized in the cell membrane but with a nuclear export signal at the N-terminal. Transcripts of CpC3H3 were significantly accumulated in flower buds at floral meristem formation stage, and were induced by polyethylene glycol. Overexpression of CpC3H3 promoted flowering, and enhanced drought tolerance in transgenic A. thaliana. CpC3H3 overexpression affects the expression level of genes involved in flower inducement and stress responses. Further comparative studies on physiological indices showed the contents of proline and soluble sugar, activity of peroxidase and the rates of electrolyte leakage were significantly increased and the content of malondialdehyde and osmotic potential was significantly reduced in transgenic A. thaliana under PEG stress. CONCLUSION: Overall, CpC3H3 plays a role in flowering inducement and drought tolerance in transgenic A. thaliana. The CpC3H3 gene has the potential to be used to promote flowering and enhance drought tolerance in plants.

CpMAX1a, a Cytochrome P450 Monooxygenase Gene of Chimonanthus praecox Regulates Shoot Branching in Arabidopsis.

Strigolactones (SLs) are a class of important hormones in the regulation of plant branching. In the model plant Arabidopsis, AtMAX1 encodes a cytochrome P450 protein and is a crucial gene in the strigolactone synthesis pathway. Yet, the regulatory mechanism of MAX1 in the shoot branching of wintersweet (Chimonanthus praecox) remains unclear. Here we identified and isolated three MAX1 homologous genes, namely CpMAX1a, CpMAX1b, and CpMAX1c. Quantitative real-time PCR (qRT-PCR) revealed the expression of CpMAX1a in all tissues, being highest in leaves, whereas CpMAX1b was only expressed in stems, while CpMAX1c was expressed in both roots and stem tips. However, CpMAX1a's expression decreased significantly after decapitation; hence, we verified its gene function. CpMAX1a was located in Arabidopsis chloroplasts. Overexpressing CpMAX1a restored the phenotype of the branching mutant max1−3, and reduced the rosette branch number, but resulted in no significant phenotypic differences from the wild type. Additionally, expression of AtBRC1 was significantly upregulated in transgenic lines, indicating that the CpMAX1a gene has a function similar to the homologous gene of Arabidopsis. In conclusion, our study shows that CpMAX1a plays a conserved role in regulating the branch development of wintersweet. This work provides a molecular and theoretical basis for better understanding the branch development of wintersweet.

CpBBX19, a B-Box Transcription Factor Gene of Chimonanthus praecox, Improves Salt and Drought Tolerance in Arabidopsis.

Zinc-finger proteins are important transcription factors in plants, responding to adversity and regulating the growth and development of plants. However, the roles of the BBX gene family of zinc-finger proteins in wintersweet (Chimonanthus praecox) have yet to be elucidated. In this study, a group IV subfamily BBX gene, CpBBX19, was identified and isolated from wintersweet. Quantitative real-time PCR (qRT-PCR) analyses revealed that CpBBX19 was expressed in all tissues and that expression was highest in cotyledons and inner petals. CpBBX19 was also expressed in all flower development stages, with the highest expression detected in early initiating bloom, followed by late initiating bloom and bloom. In addition, the expression of CpBBX19 was induced by different abiotic stress (cold, heat, NaCl, and drought) and hormone (ABA and MeJA) treatments. Heterologous expression of CpBBX19 in Arabidopsis thaliana (Arabidopsis) enhanced the tolerance of this plant to salt and drought stress as electrolyte leakage and malondialdehyde (MDA) concentrations in transgenic Arabidopsis after stress treatments were significantly lower than those in wild-type (WT) plants. In conclusion, this research demonstrated that CpBBX19 plays a role in the abiotic stress tolerance of wintersweet. These findings lay a foundation for future studies on the BBX gene family of wintersweet and enrich understanding of the molecular mechanism of stress resistance in wintersweet.

Two-Phase Robust Target Localization in Ocean Sensor Networks Using Received Signal Strength Measurements.

Target localization plays a vital role in ocean sensor networks (OSNs), in which accurate position information is not only a critical need of ocean observation but a necessary condition for the implementation of ocean engineering. Compared with other range-based localization technologies in OSNs, the received signal strength (RSS)-based localization technique has attracted widespread attention due to its low cost and synchronization-free nature. However, maintaining relatively good accuracy in an environment as dynamic and complex as the ocean remains challenging. One of the most damaging factors that degrade the localization accuracy is the uncertainty in transmission power. Besides the equipment loss, the uncertain factors in the fickle ocean environment may result in a significant deviation between the standard rated transmission power and the usable transmission power. The difference between the rated and actual transmission power would lead to an extra error when it comes to the localization in OSNs. In this case, a method that can locate the target without needing prior knowledge of the transmission power is proposed. The method relies on a two-phase procedure in which the location information and the transmission power are jointly estimated. First, the original nonconvex localization problem is transformed into an alternating non-negativity-constrained least square framework with the unknown transmission power (UT-ANLS). Under this framework, a two-stage optimization method based on interior point method (IPM) and majorization-minimization tactic (MMT) is proposed to search for the optimal solution. In the first stage, the barrier function method is used to limit the optimization scope to find an approximate solution to the problem. However, it is infeasible to approach the constraint boundary due to its intrinsic error. Then, in the second stage, the original objective is converted into a surrogate function consisting of a convex quadratic and concave term. The solution obtained by IPM is considered the initial guess of MMT to jointly estimate both the location and transmission power in the iteration. In addition, in order to evaluate the performance of IPM-MM, the Cramer Rao lower bound (CRLB) is derived. Numerical simulation results demonstrate that IPM-MM achieves better performance than the others in different scenarios.

Influence of health education on clinical parameters in type 2 diabetic subjects with and without hypertension: A longitudinal, comparative analysis in routine primary care settings.

AIM: To assess the influence of health education for type 2 diabetic patients with and without coexisting hypertension in routine primary care where intensive educational consultations were absent. METHODS: A longitudinal cohort was constructed from 342 diabetic subjects who previously had regular exposure to face-to-face health education delivered quarterly during 2016-2017 under the national basic public health (BPH) service provision in an urbanised township in China. Clinical parameters were retrieved electronically from computerised BPH data platform at prior check-ups (2016-2017) and at the most recent check-up (2019). RESULTS: The satisfactory clinical improvements upon health education were not sustained during subsequent observational years among study subjects. A significant increase in total cholesterol (0.28 mmol/L for between-group net changes, 95% confidence interval [CI] = 0.01-0.55 mmol/L, p = 0.039) were observed in diabetic subjects with coexisting hypertension. Older patients (adjusted odds ratio [aOR] = 0.87, 95%CI = 0.83-0.91, p less than 0.001), males (aOR = 0.50, 95%CI = 0.26-0.98, p = 0.043), and subjects with lower education level (aOR = 0.34, 95%CI = 0.17-0.67, p = 0.002) were less likely to maintain improvement of biomedical parameters. CONCLUSION: The influence of face-to-face health education may not be prolonged in routine primary care where intensive provisions of educational consultations were less common. Diabetic patients with coexisting hypertension tend to have more difficulties in maintaining optimal lipid profiles.

An Absorption Mitigation Technique for Received Signal Strength-Based Target Localization in Underwater Wireless Sensor Networks.

Localization is an indispensable technology for underwater wireless sensor networks (UWSNs). In what concerns UWSNs, the accurate location information is not only the requirement of the marine field applications but also the basis of the other corresponding research, for instance, network routing and topology control. Recently, an astonishing surge of interest has been drawn in the received signal strength (RSS)-based scheme due to cost-effectiveness and synchronization-free compared with others. However, unlike the terrestrial wireless sensor networks (WSNs), the acoustic signal may suffer the absorption loss in the underwater environment besides the path loss, which degrades the localization accuracy and limits the capability of the RSS-based technology in UWSNs. In this context, a robust localization method with an absorption mitigation technique (AMT) is developed. First, an RSS-based analytically tractable measurement model is conducted, where the maximum likelihood estimator (MLE) is derived. Nevertheless, it is quite challenging to solve the problem using MLE under a non-convex expression. Therefore, by exploiting certain approximations, the considered localization problem is converted into an optimization expression with a maximum absorption loss involved. A min-max strategy is then presented, with which the problem is turned to minimize the worst situation of the absorption loss. After a simple manipulation, the problem is further investigated as a generalized trust region sub-problem (GTRS) framework. Although the GTRS is a non-convex scheme, the solution can be obtained through an iteration method by introducing a multiplier. In addition, the closed-form expression of the Cramer-Rao lower bound (CRLB) of the analytically tractable measurement model is derived. Numerical simulations demonstrate the effectiveness of the proposed method compared with the state-of-the-art approaches in different scenarios.

Mining ship deficiency correlations from historical port state control (PSC) inspection data.

Early warning on the ship deficiency is crucial for enhancing maritime safety, improving maritime traffic efficiency, reducing ship fuel consumption, etc. Previous studies focused on the ship deficiency exploration by mining the relationships between the ship physical deficiencies and the port state control (PSC) inspection results with statistical models. Less attention was paid to discovering the correlation rules among various parent ship deficiencies and subcategories. To address the issue, we proposed an improved Apriori model to explore the intrinsic mutual correlations among the ship deficiencies from the PSC inspection dataset. Four typical ship property indicators (i.e., ship type, age, deadweight and gross tonnage) were introduced to analyze the correlations for the ship parent deficiency categories and subcategories. The findings of our research can provide basic guidelines for PSC inspections to improve the ship inspection efficiency and maritime safety.

Robust Visual Ship Tracking with an Ensemble Framework via Multi-View Learning and Wavelet Filter.

Maritime surveillance videos provide crucial on-spot kinematic traffic information (traffic volume, ship speeds, headings, etc.) for varied traffic participants (maritime regulation departments, ship crew, ship owners, etc.) which greatly benefits automated maritime situational awareness and maritime safety improvement. Conventional models heavily rely on visual ship features for the purpose of tracking ships from maritime image sequences which may contain arbitrary tracking oscillations. To address this issue, we propose an ensemble ship tracking framework with a multi-view learning algorithm and wavelet filter model. First, the proposed model samples ship candidates with a particle filter following the sequential importance sampling rule. Second, we propose a multi-view learning algorithm to obtain raw ship tracking results in two steps: extracting a group of distinct ship contour relevant features (i.e., Laplacian of Gaussian, local binary pattern, Gabor filter, histogram of oriented gradient, and canny descriptors) and learning high-level intrinsic ship features by jointly exploiting underlying relationships shared by each type of ship contour features. Third, with the help of the wavelet filter, we performed a data quality control procedure to identify abnormal oscillations in the ship positions which were further corrected to generate the final ship tracking results. We demonstrate the proposed ship tracker's performance on typical maritime traffic scenarios through four maritime surveillance videos.

Silencing lncRNA HOTAIR declines synovial inflammation and synoviocyte proliferation and promotes synoviocyte apoptosis in osteoarthritis rats by inhibiting Wnt/ß-catenin signaling pathway.

Osteoarthritis (OA) is a degenerative injury of the articular cartilage and reactive hyperplasia of the articular rim and subchondral bone. Due to its poor prognosis, this study is to research the influences of lncRNA HOTAIR on synovial inflammation and synoviocyte apoptosis and proliferation in OA rats by regulating the Wnt/ß-catenin pathway. Rat OA model was constructed by means of cruciate ligament resection, and the successfully modeled rats were injected with sh-HOTAIR, or Wnt/ß-catenin pathway activator (LiCl), and synoviocytes were transfected with sh-HOTAIR or LiCl for investigation of the influences of HOTAIR and Wnt/ß-catenin pathway on synoviocytes proliferation and apoptosis. ELISA and RT-qPCR were used to detect the levels of IL-1ß, IL-6 and TNF-α in serum, synovial tissue and synoviocytes in rats, respectively. The protein levels of bax, bcl-2, wnt1 and ß-catenin in synovial tissue and synoviocytes were tested by Western blot analysis. Highly expressed HOTAIR existed in synovial tissue and synoviocytes of rats. There were declining arthritis index, inflammation, synoviocytes proliferation, cycle progression and promoted synoviocytes apoptosis by silencing HOTAIR and inhibited Wnt/ß-catenin pathway. Down-regulation of HOTAIR could reverse the effect of LiCl on progression of OA rats. There was strained activation of Wnt/ß-catenin pathway via declining HOTAIR. This study suggests that silencing lncRNA HOTAIR and inhibited Wnt/ß-catenin pathway decline synovial inflammation and synoviocyte proliferation and promote apoptosis in OA rats.

A Robust, Non-Cooperative Localization Algorithm in the Presence of Outlier Measurements in Ocean Sensor Networks.

As an important means of multidimensional observation on the sea, ocean sensor networks (OSNs) could meet the needs of comprehensive information observations in large-scale and multifactor marine environments. In what concerns OSNs, accurate location information is the basis of the data sets. However, because of the multipath effect-signal shadowing by waves and unintentional or malicious attacks-outlier measurements occur frequently and inevitably, which directly degrades the localization accuracy. Therefore, increasing localization accuracy in the presence of outlier measurements is a critical issue that needs to be urgently tackled in OSNs. In this case, this paper proposed a robust, non-cooperative localization algorithm (RNLA) using received signal strength indication (RSSI) in the presence of outlier measurements in OSNs. We firstly formulated the localization problem using a log-normal shadowing model integrated with a first order Taylor series. Nevertheless, the problem was infeasible to solve, especially in the presence of outlier measurements. Hence, we then converted the localization problem into the optimization problem using squared range and weighted least square (WLS), albeit in a nonconvex form. For the sake of an accurate solution, the problem was then transformed into a generalized trust region subproblem (GTRS) combined with robust functions. Although GTRS was still a nonconvex framework, the solution could be acquired by a bisection approach. To ensure global convergence, a block prox-linear (BPL) method was incorporated with the bisection approach. In addition, we conducted the Cramer-Rao low bound (CRLB) to evaluate RNLA. Simulations were carried out over variable parameters. Numerical results showed that RNLA outperformed the other algorithms under outlier measurements, notwithstanding that the time for RNLA computation was a little bit more than others in some conditions.

A very sensitive and highly selective organic selector in CNTs composite chemiresistive for efficient differentiation of organic amine vapours.

With the call for the IoE (Internet of Everything), stable and efficient electric noses/tongues have become the most critical part of the sensor network. Identifying target gases efficiently and rapidly at ambient air becomes a focus on sensor research. We designed a chemiresistive sensor based on a composite of a specific selector and single-walled carbon nanotubes (SWNTs) for the detection and differentiation of organic amine vapours in air (25 ℃, 55% RH). The synergetic combination of F4-TCNQ (2,3,5,6-Tetrafluoro-7,7',8,8'-tetracyanoquinodimethane) and SWCNTs could modulate the electrical properties of sensor leading to the enhancement of response up to ppb-level for primary amine vapor detection. Different from traditional chemiresistive sensor, this sensing materials exhibit unique differences in response to different types of amines thought different mechanisms. We have proven the practical possibilities through the detection of the simulated complexed environmental atmosphere in industrial production. Furthermore, we explored the working mechanism of high-performance sensors, which could provide theoretical guidance for sensor design for more commercial applications. This study provided a simple, convenient, and highly efficient practical method for organic amine detection at ambient air for real-life applications.

A novel cooperative localization algorithm using enhanced particle filter technique in maritime search and rescue wireless sensor network.

Maritime search and rescue (MSR) play a significant role in Safety of Life at Sea (SOLAS). However, it suffers from scenarios that the measurement information is inaccurate due to wave shadow effect when utilizing wireless Sensor Network (WSN) technology in MSR. In this paper, we develop a Novel Cooperative Localization Algorithm (NCLA) in MSR by using an enhanced particle filter method to reduce measurement errors on observation model caused by wave shadow effect. First, we take into account the mobility of nodes at sea to develop a motion model-Lagrangian model. Furthermore, we introduce both state model and observation model to constitute a system model for particle filter (PF). To address the impact of the wave shadow effect on the observation model, we develop an optimal parameter derived by Kullback-Leibler divergence (KLD) to mitigate the error. After the optimal parameter is acquired, an improved likelihood function is presented. Finally, the estimated position is acquired.

Evaluating the impacts of grades on vehicular speeds on interstate highways.

Grade variation on interstate highways affects the roadway geometric design, vehicle performance and driver behavior, thus possibly exerting an unexpected effect on vehicular speed. Hence, determining the internal relationship between grade and speed is important and useful for drivers, traffic regulators and other traffic participants. However, the problem with performing this research is the lack of large-scale gradient and speed data. Google Earth (GE) provides an application programming interface for extracting elevation data worldwide. The elevation dataset from GE can be easily converted to grade data. In addition, our team has collected and stored speed series data for different freeways over several years. Based on the above obtainable grade and speed datasets, we conducted research on the effect of grades on free flow speeds from two perspectives. First, the influence of grades on speed was analyzed from both quantitative and qualitative aspects. The analysis of the distributions of four typical types of speeds demonstrated a decreasing tendency as the speed increased. Steeper grades generated a more intense speed fluctuation in terms of the four types of speeds. Second, a model based on the Student's t-test was developed to evaluate the level of significant difference among speed series under neighboring grades. The Student's t-test demonstrated that adjacent grades do not significantly influence the speeds. In summary, speeds under different grades showed obviously different tendencies. The findings of this study can help transport authorities set more reasonable speed limits and improve the geometric design of interstates with grade variation constraints.

Fuzzy-Logic Based Distributed Energy-Efficient Clustering Algorithm for Wireless Sensor Networks.

Due to the high-energy efficiency and scalability, the clustering routing algorithm has been widely used in wireless sensor networks (WSNs). In order to gather information more efficiently, each sensor node transmits data to its Cluster Head (CH) to which it belongs, by multi-hop communication. However, the multi-hop communication in the cluster brings the problem of excessive energy consumption of the relay nodes which are closer to the CH. These nodes' energy will be consumed more quickly than the farther nodes, which brings the negative influence on load balance for the whole networks. Therefore, we propose an energy-efficient distributed clustering algorithm based on fuzzy approach with non-uniform distribution (EEDCF). During CHs' election, we take nodes' energies, nodes' degree and neighbor nodes' residual energies into consideration as the input parameters. In addition, we take advantage of Takagi, Sugeno and Kang (TSK) fuzzy model instead of traditional method as our inference system to guarantee the quantitative analysis more reasonable. In our scheme, each sensor node calculates the probability of being as CH with the help of fuzzy inference system in a distributed way. The experimental results indicate EEDCF algorithm is better than some current representative methods in aspects of data transmission, energy consumption and lifetime of networks.