Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthesis of a Finite State Machine With Elementary Chains of States

Modified structure and method are proposed for synthesis of a combined finite state machine (CFSM) which forms the output functions of both types FSM – Mealy and Moore. Similar algorithms are used mainly for controlling the blocks of in-built systems in cases when the sensors that describe the system are not numerous, but it is necessary to perform certain sequences of microoperations. We consider a case when initial control algorithm is represented with a linear flowchart. Such flowcharts have a significant number of operator vertices, exceeding 75% of the total number of vertices. In the case of linear flowchart we propose to replace the register RG with a counter of states CT. It allows simplification of Boolean functions representing a combined FSM logic circuit. Also this allows to simplify the system of memory-excitation functions. The method uses existence of elementary linear chains of states in the initial flowchart. There is an example of synthesis and results of experimental study given in the article. The study of the proposed method based on the real control algorithm of the onboard computing device. Experiments and testing performed for various Intel (Altera) microchips. The studies carried out for different families of FPGAs, in different price ranges. A comparative analysis of time characteristics for the basic and proposed structures allows us to notice a tendency to some acceleration of the operation. In general, research results have shown that proposed structures of combined FSM can be useful for design of embedded systems.
Mobility Aware and Traffic Driven Neighbor Discovery For FANET

In FANET, neighbor discovery is achieved by frequently broadcasting beacon messages. However, the high-speed movement of UAVs will lead to drastic changes in the network topology, and the energy they carry is usually limited. Therefore, realizing fast, efficient, and low-energy ad hoc network technology between UAVs is an urgent problem that needs to be solved. In this paper, we propose UMBND, a UAV-Mobility and Traffic-Driven Adaptive Neighbor Discovery method. First, we analyze the discovery scene of FANET. Then, we statistically analyze the neighbor discovery process and derive the relationship between neighbor discovery performance and mobility and traffic-driven. Based on our theoretical analysis, the optimal neighbor discovery can adaptively adjust the beacon broadcast frequency. Finally, the simulation results show that the scheme has better performance in terms of overhead and accuracy and can be adapted to FANET scenarios.
Synchronous Reluctance Machines with and without Ferrite Assistance for Lifting Systems

This paper deals with the comparative design and testing of synchronous reluctance machines with and without the addition of ferrite magnets for lifting applications. First, an efficient design process is proposed and showcased for both machine types. Given the application specifications of peak torque and maximum speed, the process permits quickly determining the cross-section of the motors, as well as the corresponding inverter current rating. Once the final design is defined, the optimal step-skewing angle is defined and applied in seamless computational time; for the ferrite-assisted machine, the demagnetization limit is assessed. After comparing the two designs in terms of key performance indexes and expected cost of production, the synchronous reluctance machine prototype was built and tested. The experimental tests include the measurement of the flux linkage maps and tests under load with related thermal measurements; the experimental findings are compared with the model results demonstrating an excellent match.
The Capacity-Achieving Input of Optical Wireless Channels: The Number of Mass Points

This paper investigates a scalar optical wireless channel (OWC) corrupted by additive Gaussian noise. When the channel is peak- and average-intensity constrained, Chan et al. have proposed a necessary and sufficient condition for the probability distribution function (PDF) of the capacity-achieving input. We show that such a capacity-achieving input PDF is supported on finite mass points. Moreover, we provide new bounds on the number of its mass points. The upper and lower bounds are in the orders of A2 and A, respectively, where A denotes the maximum instantaneous intensity.
Significance of Intraplaque Hemorrhage for the Development of High-Risk Vulnerable Plaque: Current Understanding from Basic to Clinical Points of View

Acute coronary syndromes due to atherosclerotic coronary artery disease are a leading cause of morbidity and mortality worldwide. Intraplaque hemorrhage (IPH), caused by disruption of Intraplaque leaky microvessels, is one of the major contributors of plaque progression, causing a sudden increase in plaque volume and eventually plaque destabilization. IPH and its healing processes are highly complex biological events that involve interactions between multiple types of cells in the plaque, including erythrocyte, macrophages, vascular endothelial cells and vascular smooth muscle cells. Recent investigations have unveiled detailed molecular mechanisms by which IPH leads the development of high-risk “vulnerable” plaque. Current advances in clinical diagnostic imaging modalities, such as magnetic resonance image and intra-coronary optical coherence tomography, increasingly allow us to identify IPH in vivo. To date, retrospective and prospective clinical trials have revealed the significance of IPH as detected by various imaging modalities as a reliable prognostic indicator of high-risk plaque. In this review article, we discuss recent advances in our understanding for the significance of IPH on the development of high-risk plaque from basic to clinical points of view.
Road Plant Image Classification Algorithm Based on Segmentation Prior

As an important part of urban ecology, it is of great significance to grasp and analyze the detailed distribution of road plants for both scientific research and landscape planning. However, due to the advancement of urbanization, the types and numbers of road plants are increasing, and it is difficult to obtain accurate and timely plant distribution information by traditional field research and remote sensing techniques. In order to solve this problem, we proposes a deep learning-based road plant image classification algorithm, which identifies plants in streetscape data by multi- label image classification to obtain the distribution of road plants. In order to solve the problem of feature information loss in the network, we increases the channel capacity of the bottleneck structure in the network, and introduces the group convolution structure to optimize the network performance. Due to the complexity of the image background, we proposes a road plant image classification algorithm based on a priori segmentation by training the binary classification segmentation model of plants and background through transfer learning. This makes the classification model pay more attention to the area of road plants in the image and improves the classification accuracy by 5.16%.
e Audit: A Fast, Scalable and Deployable Audit Data Collection System ∗

—Today’s advanced cyber attack campaigns can often bypass all existing protections. The primary defense against them is after-the-fact detection, followed by a forensic analysis to understand their impact. Such an analysis requires audit logs (also called provenance logs) that faithfully capture all activities and data flows on each host. While the Linux auditing daemon (auditd) and sysdig are the most popular tools for audit data collection, a number of other systems, authored by researchers and practitioners, are also available. Through a motivating experimental study, we show that these systems impose high overheads, slowing workloads by 2 × to 8 × ; lose a majority of events under sustained workloads; and are vulnerable to log tampering that erases log entries before they are committed to persistent storage. We present a new approach that overcomes these challenges. By relying on the extended Berkeley Packet Filter (eBPF) framework built into recent Linux versions, we avoid changes to the kernel code, and hence our data collector works out of the box on most Linux distributions. We present new design, tuning and optimization techniques that enables our system to sustain workloads that are an order of magnitude more intense than those causing major data loss with existing systems. Moreover, our system incurs only a fraction of the overhead of previous systems, while considerably reducing data volumes, and shrinking the log tampering window by ∼ 100 × .
Research on Vehicle Fault Diagnosis Performance of GBDBN Model

Vehicle traditional fault diagnosis technology consumes a lot of manpower, it is difficult to excavate the internal rules of fault signals and to guarantee the accuracy of diagnosis results for a large number of data. Deep learning method has been widely used in fault diagnosis. This paper proposes a new method based on Gaussian-Bernoulli deep belief network. Aiming at the problem that there is no theoretical basis for parameter setting in the process of model training, taking the vibration acceleration signal of rolling bearing as the research object, the applicability of some parameters is experimentally studied, and the influence of different parameters on the fault diagnosis performance of GBDBN model is discussed. To some extent, the experimental results provide a reference for setting the initial parameters of GBDBN model in the training process.
Chemical characterization and source apportionment of PM 2 . 5 in Beijing : seasonal perspective

In this study, 121 daily PM2.5 (aerosol particle with aerodynamic diameter less than 2.5 μm) samples were collected from an urban site in Beijing in four months between April 2009 and January 2010 representing the four seasons. The samples were determined for various compositions, including elements, ions, and organic/elemental carbon. Various approaches, such as chemical mass balance, positive matrix factorization (PMF), trajectory clustering, and potential source contribution function (PSCF), were employed for characterizing aerosol speciation, identifying likely sources, and apportioning contributions from each likely source. Our results have shown distinctive seasonality for various aerosol speciations associated with PM 2.5 in Beijing. Soil dust waxes in the spring and wanes in the summer. Regarding the secondary aerosol components, inorganic and organic species may behave in different manners. The former preferentially forms in the hot and humid summer via photochemical reactions, although their precursor gases, such as SO2 and NOx, are emitted much more in winter. The latter seems to favorably form in the cold and dry winter. Synoptic meteorological and climate conditions can overwhelm the emission pattern in the formation of secondary aerosols. The PMF model identified six main sources: soil dust, coal combustion, biomass burning, traffic and waste incineration emission, industrial pollution, and secondary inorganic aerosol. Each of these sources has an annual mean contribution of 16, 14, 13, 3, 28, and 26 %, respectively, to PM2.5. However, the relative contributions of these identified sources significantly vary with changing seasons. The results of trajectory clustering and the PSCF method demonstrated that regional sources could be crucial contributors to PM pollution in Beijing. In c nclusion, we have unraveled some complex aspects of the pollution sources and formation processes of PM 2.5 in Beijing. To our knowledge, this is the first systematic study that comprehensively explores the chemical characterizations and source apportionments of PM2.5 aerosol speciation in Beijing by applying multiple approaches based on a completely seasonal perspective.
Chemical characterization and source apportionment of PM 2.5 in Beijing: seasonal perspective

Abstract. In this study, 121 daily PM2.5 (aerosol particle with aerodynamic diameter less than 2.5 μm) samples were collected from an urban site in Beijing in four months between April 2009 and January 2010 representing the four seasons. The samples were determined for various compositions, including elements, ions, and organic/elemental carbon. Various approaches, such as chemical mass balance, positive matrix factorization (PMF), trajectory clustering, and potential source contribution function (PSCF), were employed for characterizing aerosol speciation, identifying likely sources, and apportioning contributions from each likely source. Our results have shown distinctive seasonality for various aerosol speciations associated with PM2.5 in Beijing. Soil dust waxes in the spring and wanes in the summer. Regarding the secondary aerosol components, inorganic and organic species may behave in different manners. The former preferentially forms in the hot and humid summer via photochemical reactions, although their precursor gases, such as SO2 and NOx, are emitted much more in winter. The latter seems to favorably form in the cold and dry winter. Synoptic meteorological and climate conditions can overwhelm the emission pattern in the formation of secondary aerosols. The PMF model identified six main sources: soil dust, coal combustion, biomass burning, traffic and waste incineration emission, industrial pollution, and secondary inorganic aerosol. Each of these sources has an annual mean contribution of 16, 14, 13, 3, 28, and 26%, respectively, to PM2.5. However, the relative contributions of these identified sources significantly vary with changing seasons. The results of trajectory clustering and the PSCF method demonstrated that regional sources could be crucial contributors to PM pollution in Beijing. In conclusion, we have unraveled some complex aspects of the pollution sources and formation processes of PM2.5 in Beijing. To our knowledge, this is the first systematic study that comprehensively explores the chemical characterizations and source apportionments of PM2.5 aerosol speciation in Beijing by applying multiple approaches based on a completely seasonal perspective.
GC-MS Analysis and Biological Effects of Essential Oil from Trachyspermum ammi L

Trachyspermum ammi L, local seed’s essential oil and its crude methanolic extract were evaluated for antioxidant, antibacterial, and urease inhibition activities. Largely, 18 constituents were identified in the essential oil using The GC-MS. The promising constituent reported was terpeniodes; which seem to be responsible for the reported biological activities. DPPH Free radica1 scavenging assay was done to evaluate the antioxidant activities of both oil and crude extract. The DPPH scavenging activity results in lC50 values of 75 and#177; 0.05 and 300 and#177; 0.03. Antibacterial activity of the oil was carried out by using strains of Escherichia coli (89%), Bacillus subtilis (85%), and Streptococcus aureus (80%), while for the crude extract strains of E. coli (86%) B. subtle (89%), and S. aureus; (85%) were used. E. coil of T. ammi exhibits promising results with Aspergillus. flavus1 (86%) and Aspergillus1 Niger1 (87%). The phytochemical analysis of the T. ammi, crude extract showed the presence of saponins, flavonoids, steroids, terpenoids, and cardiac glycosides in the phytochemical investigation. Urease inhibition showed potent inhibition against B.P and J.B Urease with IC50 values of 85 and#177; 0.06 and 67.45 and#177; 0.8, respectively. Plant crude showed optimal results for B.P and Jack bean urease with IC50 values of 55.77 and#177; 0.01 and 35.56 and#177; 0.04, respectively.
Peranan Pengelompokan Samar dalam Prediksi Kekeringan di Indonesia Berkaitan dengan ENSO dan IOD

Secara umum kekeringan di Indonesia dapat diprediksi berdasarkan intensitas El Nino yang didefinisikan berdasarkan deret waktu anomali suhu permuaan lout di Pacific (SSTA 3.4). Hasil penelitian menunjukkan bahwa untuk intensitas El Nino kuat lebih dai 65% daerah di Indonesia mengalami hujan di bawah normal (Indonesia kering). Korelasi antara intensitas El Nino yang kuat dengan persentase hujan di bawah normal di Indonesia sangat besar, namun ketika intensitasnya rendah korelasinya menurun. Ini menunjukan bahwa ketika intensitas El Nino rendah mekanisme lain, misalnya Dipole Mode di lautan India dapat memberi kontribusi besar. Pengaruh lebih rinci terhadap wilayah iklim di Indonesia masih harus dikembangkan. Pengelompokan wilayah iklim berdasarkan himpunan samar atau relasi samar pola curah hujan bulanan yang dikenal sebagai fuzzy clustering atau pengelompokan dengan JNA seperti yang dilakukan Kohonen dapat dipokai di sini. Ternyata daerah Indonesia dapat dikelompokan berdasarkan pola hujan yang dipengaruhi kuat oleh Monsun Australia Utara Indonesia (MAIM), dan pola hujan yang khas daerah khatulistiwa yang dikenal sebagai Benua Maritim (MC). Gagasan pengelompokan wilayah iklim ini dilandasi pemikiran bahwa untuk membahas pengaruh ENSO dan IOD kita harus membahas dinamika atmosfir dalam skala regional sehingga pengelompokan wilayah iklim harus berlandaskan tinggi geopotensial atau pola hujan bulanan rata-rata. Ternyata Benua Maritim (MC) timur dan NAIM dipengaruhi kuat oleh ENSO, MC barat khususnya Sumatra Selatan dan Jawa Barat dipengaruhi juga oleh Dipole Mode yang terjadi di lautan India. In general drought in Indonesia can be predicted from intensities of El Nino that can be defined by using time series of sea surface anomaly on Pacific Ocean (SSTA 3.4). It can be shown that when El Nino with strong intensities occur then more than 65% regions in Indonesia the precipitations are below normal (drought in Indonesia). The correlation between strong El Nino intensities and percentages of regions in Indonesia with precipitations below normal are high, but when the intensities are weak the correlations are low. In this case other phenomena such as on Indian Ocean Dipole Mode (IOD) can contribute to drought in Indonesia. Clustering of climatic regions in Indonesia based on monthly rainfall pattern using fuzzy set, fuzzy relations or Kohonen's neural network will help to clarify drought on these regions. It can be shown that climatic regions in Indonesia can be clustered based on monthly rainfall patterns that are strongly influence by Australian monsoon which is known as North Australia Indonesian Monsoon (MAIM) and Maritime Continent (MC) which has equatorial precipitation characteristic. The climatic clustering is based on the ground that ENSO and IOD are regional atmospheric dynamic so the clustering should be based on average monthly pattern or geopotential height. The east MC and NAIM will be influence strongly by ENSO and the western MC especially south Sumatra and west Java is influence also by IOD.
Meta analysis of ultrasound-guided posterior and paravertebral block for postoperative analgesia and adverse reactions in thoracic surgery

: This article used meta-analysis and systematic evaluation methods to compare the analgesic effect and incidence of adverse reactions of ultrasound-guided posterior block (RLB) and paravertebral block (PVB) in patients undergoing thoracic surgery. Pub Med, Cochrane Library, EMBase, CNKI, Wanfang Database, Vip Database, and China Biomedical Full-text Database were searched from the establishment of the library to 2022-12-01, and randomized controlled trials (RCTs) on the analgesic effect of ultrasound-guided RLB and PVB in thoracic surgery were collected and compared. Among them, the patients in the control group used ultrasound-guided RLB composite general anesthesia, and the patients in the experimental group used ultrasound-guided PVB combined general anesthesia. The primary outcomes were 2h, 6h, 12h, 24h, and 48h postoperative resting state and motor state NRS scores; Secondary outcomes were the number of analgesia and the incidence of adverse effects such as nausea, vomiting, and dizziness after surgery. Data were meta-analysed using Rev Man 5.3 software. A total of 220 patients in 4 RCT studies were included, including 110 cases in the RLB block group and 110 cases in the PVB group. The results of meta-analysis showed that compared with the PVB group (control group), the NRS score in the RLB group (experimental group) at rest at 6(MD=0.45,95%CI 0.16~-0.75,P=0.003) hand 24 h (MD=0.26, 95%CI 0.05~0.47, P=0.01) and NRS at 24h (MD=0.25, 95%CI 0.02~-0.48, P=0.03) exercise were significantly increased. There were no significant differences in NRS scores of resting state at 2h (MD=0.32,95%CI -0.22~0.66,P=0.06), 12h (MD=0.17,95%CI -0.03~0.36,P=0.09) and 48h (MD=0.07,95%CI -0.09~0.24,P=0.37) after surgery, NRS scores of exercise status at 2h (MD=0.26,95%CI -0.68~0.17,P=0.24), 6h (MD=0.09,95%CI -0.22~0.40,P=0.57), 12h (MD=0.04,95%CI -0.25~0.32,P=0.80) and 48h (MD=0.04,95%CI 0.22~0.30,P=0.74) after surgery, the number of postoperative remedial analgesia (RR=1.12,95%CI 0.45~2.79,P=0.82), postoperative vomiting nausea (RR=0.64,95%CI 0.34~1.19,P=0.16), dizziness (RR=0.61,95%CI 0.22~1.64,P=0.32) and other adverse reactions. The available clinical evidence shows that ultrasound-guided PVB is better than RLB in the early postoperative (24h) analgesia of thoracic surgery, and there is no significant difference between the two in the long-term (48h) analgesia after surgery, and the incidence of adverse reactions such as nausea, vomiting and dizziness does not increase compared with RLB.
The chimpanzees of the Budongo Forest : ecology, behaviour, and conservation

Dedication Acknowledgments Contents Introduction by Jane Goodall 1. The Budongo Forest 2. The Sonso community 3. Morbidity and mortality 4. Diet and culture at Sonso 5. Social organization 6. Social behaviour and relationships 7. Infanticide 8. Intra-community killing 9. The problem of snares 10. The human foreground 11. The Kasokwa Forest chimpanzees 12. The future of Budongo's chimpanzees and of the chimpanzees of Uganda as a whole References Appendix 1. The Sonso chimpanzee community Appendix 2. Plant food species Appendix 3. Genetics of the Sonso community Appendix 4. Reports on (a) necropsy of Ruda (b) outbreak of respiratory disease Appendix 5. Other primate species of the Budongo Forest Appendix 6. The Budongo Forest Project