HaoLap: a Hadoop based OLAP system for big data

International audienceIn recent years, facing information explosion, industry and academia have adopted distributed file system and MapReduce programming model to address new challenges the big data has brought. Based on these technologies, this paper presents HaoLap (Hadoop based oLap), an OLAP (OnLine Analytical Processing) system for big data. Drawing on the experience of Multidimensional OLAP (MOLAP), HaoLap adopts the specified multidimensional model to map the dimensions and the measures; the dimension coding and traverse algorithm to achieve the roll up operation on dimension hierarchy; the partition and linearization algorithm to store dimensions and measures; the chunk selection algorithm to optimize OLAP performance; and MapReduce to execute OLAP. The paper illustrates the key techniques of HaoLap including system architecture, dimension definition, dimension coding and traversing, partition, data storage, OLAP and data loading algorithm. We evaluated HaoLap on a real application and compared it with Hive, HadoopDB, HBaseLattice, and Olap4Cloud. The experiment results show that HaoLap boost the efficiency of data loading, and has a great advantage in the OLAP performance of the data set size and query complexity, and meanwhile HaoLap also completely support dimension operations

100 Drivers, 2200 km: A Natural Dataset of Driving Style toward Human-centered Intelligent Driving Systems

Effective driving style analysis is critical to developing human-centered intelligent driving systems that consider drivers' preferences. However, the approaches and conclusions of most related studies are diverse and inconsistent because no unified datasets tagged with driving styles exist as a reliable benchmark. The absence of explicit driving style labels makes verifying different approaches and algorithms difficult. This paper provides a new benchmark by constructing a natural dataset of Driving Style (100-DrivingStyle) tagged with the subjective evaluation of 100 drivers' driving styles. In this dataset, the subjective quantification of each driver's driving style is from themselves and an expert according to the Likert-scale questionnaire. The testing routes are selected to cover various driving scenarios, including highways, urban, highway ramps, and signalized traffic. The collected driving data consists of lateral and longitudinal manipulation information, including steering angle, steering speed, lateral acceleration, throttle position, throttle rate, brake pressure, etc. This dataset is the first to provide detailed manipulation data with driving-style tags, and we demonstrate its benchmark function using six classifiers. The 100-DrivingStyle dataset is available via https://github.com/chaopengzhang/100-DrivingStyle-Datase

Enhancing High-Speed Cruising Performance of Autonomous Vehicles through Integrated Deep Reinforcement Learning Framework

High-speed cruising scenarios with mixed traffic greatly challenge the road safety of autonomous vehicles (AVs). Unlike existing works that only look at fundamental modules in isolation, this work enhances AV safety in mixed-traffic high-speed cruising scenarios by proposing an integrated framework that synthesizes three fundamental modules, i.e., behavioral decision-making, path-planning, and motion-control modules. Considering that the integrated framework would increase the system complexity, a bootstrapped deep Q-Network (DQN) is employed to enhance the deep exploration of the reinforcement learning method and achieve adaptive decision making of AVs. Moreover, to make AV behavior understandable by surrounding HDVs to prevent unexpected operations caused by misinterpretations, we derive an inverse reinforcement learning (IRL) approach to learn the reward function of skilled drivers for the path planning of lane-changing maneuvers. Such a design enables AVs to achieve a human-like tradeoff between multi-performance requirements. Simulations demonstrate that the proposed integrated framework can guide AVs to take safe actions while guaranteeing high-speed cruising performance

Shareable Driving Style Learning and Analysis with a Hierarchical Latent Model

Driving style is usually used to characterize driving behavior for a driver or a group of drivers. However, it remains unclear how one individual's driving style shares certain common grounds with other drivers. Our insight is that driving behavior is a sequence of responses to the weighted mixture of latent driving styles that are shareable within and between individuals. To this end, this paper develops a hierarchical latent model to learn the relationship between driving behavior and driving styles. We first propose a fragment-based approach to represent complex sequential driving behavior, allowing for sufficiently representing driving behavior in a low-dimension feature space. Then, we provide an analytical formulation for the interaction of driving behavior and shareable driving style with a hierarchical latent model by introducing the mechanism of Dirichlet allocation. Our developed model is finally validated and verified with 100 drivers in naturalistic driving settings with urban and highways. Experimental results reveal that individuals share driving styles within and between them. We also analyzed the influence of personalities (e.g., age, gender, and driving experience) on driving styles and found that a naturally aggressive driver would not always keep driving aggressively (i.e., could behave calmly sometimes) but with a higher proportion of aggressiveness than other types of drivers

Laser-Induced Plasma Effects on Bond Breaking in High-Density Polyethylene Pyrolysis

The conventional use of Laser-Induced Breakdown Spectroscopy (LIBS) for elemental analysis in high-density polyethylene (HDPE) limits the exploration of bond behavior in Physics and Chemistry. A suitable combination of process parameters, exceeding the bond dissociation threshold, enables LIBS to break HDPE bonds, facilitating laser-induced pyrolysis. However, understanding bond behavior post-breakage, yield formation pathways, and the role of plasma and ionization across laser harmonics is crucial. An experiment is conducted using three laser harmonics (1064, 532, and 266 nm) at 20 Hz with pulse energies ranging from 3 to 100 mJ. An intense Hα peak at 656.3 nm suggests bond breaking due to extensive C-H breaking and hydrogen production. Interestingly, lower photon energies of 1.17 and 2.3 eV for 1064 and 532 nm broke the bonds, attributed to plasma effects. Numerical models are used to calculate plasma temperatures and electron density, classifying plasma types. Plasma parameters such as cooling time, ionization rate, energy density, and expansion velocity are analyzed. Results show that all laser harmonics contributed to bond breaking: 1064 nm induced field-induced plasma, 532 nm favored intermediate multiphoton plasma, and 266 nm is dominated by photon-induced plasma. These findings help optimize laser-induced HDPE pyrolysis

A hypothetical approach toward laser-induced high-density polyethylene pyrolysis

Laser-induced breakdown spectroscopy (LIBS) is a commonly employed technique in commercial plastic recycling for purposes including classification, sorting, identification, and elemental analysis. However, understanding the molecular-level kinetics, thermodynamic interactions, bonding cleavage, and process parameter impacts is crucial for identifying necessary modifications to enhance plastic recycling. A review of the literature revealed that LIBS can also facilitate plastic pyrolysis, a significant research area that remains largely unexplored. Based on theoretical hypotheses, it can be concluded that laser-induced pyrolysis may offer advantages over traditional pyrolysis, which requires understanding the chemistry of plastic bond-breaking during degradation, identifying resistant bonds, and uncovering the root causes of these challenges. This approach is described in detail in sections 9 and 10, focusing on high-density polyethylene (HDPE) under controlled conditions. The identified research gaps could be further investigated, and advancements could be made toward establishing efficient plastic recycling and designing laser-induced pyrolysis reactors

Timing of surgery and risk of postoperative thrombotic complications after recovery from Covid-19 in breast cancer patients

Background and purpose: The incidence of deep venous thrombosis (DVT) for patients with breast cancer after surgery is 2.00%-6.40%. The purpose of this study was to evaluate the correlation between postoperative DVT complications and surgical timing in breast cancer patients with coronavirus disease 2019 (COVID-19) infection, in order to guide clinical decision-making. Methods: From December 20, 2022 to March 20, 2023, 317 patients with breast cancer diagnosed with COVID-19 and with signs and symptoms of infection turning negative in Shandong Institute of Cancer Prevention and Treatment (Shandong Cancer Hospital), Shandong First Medical University (Shandong Academy of Medical Sciences) were enrolled. The control group included 329 patients with breast cancer who underwent surgery between May 1, 2019 and September 30, 2019 in the same hospital. Patients were grouped according to the interval between the date of COVID-19 infection and the date of surgery, and the interval time and postoperative DVT occurrence were analyzed. Results: Among 317 patients with breast cancer who underwent surgery after COVID-19 infection, 17 (5.36%), 29 (9.15%), 31 (9.78%), 50 (15.78%) and 190 (59.90%) underwent surgery after 0-2, 3-4, 5-6, 7-8 and 8 weeks, respectively. The incidence of postoperative DVT was 11.76%, 3.45%, 3.23%, 6.00% and 1.58%, respectively. The incidence of postoperative DVT in 329 patients without COVID-19 infection was 1.21%, and the incidence of postoperative DVT in patients receiving operation within 2 weeks of COVID-19 infection was significantly higher (OR=10.556; 95% CI: 1.095-303.313, P=0.03), the incidence of postoperative DVT in patients undergoing operation 3-8 weeks following COVID-19 infection was 4.55%. Multivariate analysis showed that COVID-19 infection interval was an independent predictor of DVT (OR=2.795; 95% CI: 0.692-11.278, P=0.024). All 10 patients with DVT after breast cancer surgery were recovered without serious complications such as pulmonary embolism, and the follow-up adjuvant anti-tumor therapy was not affected after symptomatic treatment. Conclusion: The incidence of DVT after breast cancer surgery within 8 weeks of COVID-19 infection is significantly higher than that of uninfected patients, especially the incidence of DVT in patients undergoing surgery within 2 weeks of COVID-19 infection is as high as 11.76%. Elective surgery for breast cancer within 2 weeks of COVID-19 infection should be avoided. Although the incidence of DVT in patients undergoing surgery 3 weeks after COVID-19 infection is still slightly high, surgical treatment can be recommended considering the urgency of breast cancer treatment, the good prognosis of DVT and the lack of influence on subsequent adjuvant therapy. However, detailed records of COVID-19 infection history of patients, early prevention and close monitoring should be made, and postoperative DVT should be treated

Effects of victim’s body posture and attacker’s gender on slashing attacks: a biomechanical study

ObjectiveSharp force injury has been and will remain to be a major cause of violent death; however, scientific evaluations on the impact of body posture of the victim and gender of the perpetrator on sharp force injury have been scarce. The purpose of this study was to evaluate the biomechanical characteristics found in individuals (male and female) when using a Chinese kitchen knife to slash the neck of a dummy while it was in the standing and supine positions. This work offers a solid basis for forensic identifications, criminal investigations, and court trials.MethodsA total of 12 male and 12 female college students participated in this study. Kinematic, kinetic, and surface electromyography (sEMG) data were evaluated when slashing the neck of a dummy while it was in the standing and supine positions using a Chinese kitchen knife.ResultsWhen slashing the neck of a standing dummy, participants showed shorter contact time (19.5%) and slower shoulder velocities (30.9%) as well as higher hip velocity (26.0%) and increased root mean square (RMS) and integral electromyography (iEMG) for the anterior deltoid (51.3% and 51.2%, respectively) compared to those while the dummy was in the supine position (all p < 0.05), regardless of gender. When slashing a dummy’s neck while it was in standing and supine positions, male participants showed higher shoulder, elbow, and wrist velocities (22.6%, 22.7%, and 24.4%, respectively) and higher slashing velocity (19.8%), slashing force (24.2%), and energy (46.2%) than female participants (all p < 0.05). In addition, male participants showed shorter contact time (17.8%), and the values of RMS and iEMG of the anterior deltoid, biceps brachii, extensor carpi radialis longus, and flexor carpi ulnaris were less than those of female participants (98.9%, 47.3%, 65.6%, and 33.4% for RMS and 115.1%, 59.4%, 80.1%, and 47.8% for iEMG, respectively).ConclusionThere was no difference in slashing speed, slashing force, and energy when using a Chinese kitchen knife to slash the dummy’s neck while it was in different body postures (standing and supine), suggesting a similar level of injury severity. However, there were significant differences in slashing action patterns between the two body postures, with longer contact time, smaller hip velocity, greater shoulder velocity, and less muscle activation level of the deltoid exertion when slashing the dummy’s neck in the supine position. Gender may have a greater effect on the severity of slashing, and the gender difference may be partly related to the body weight difference. The findings from this study may provide quantitative indicators and references for analyzing the motive behind the crime, as well as for case reconstruction, and for the court’s conviction and sentencing processes

Opioid−free anesthesia attenuates perioperative immunosuppression by regulating macrophages polarization in gastric cancer patients treated with neoadjuvant PD-1 inhibitor

BackgroundOpioid anesthesia can modulate the impaired immune response and opioid-sparing anesthesia may preserve immune functions. This study was performed to assess the effects of opioid-free anesthesia (OFA) and opioid-based anesthesia (OA) on perioperative macrophages differentiation, cytokine changes, and perioperative complications in locally advanced GC (LAGC) patients.MethodsWe used quality of recovery-15 (QoR-15) questionnaire scores and visual analog scale (VAS) scores to compare postoperative quality of recovery and pain level. In addition, the adverse reactions of patients in the two groups were compared. The perioperative serum level of inflammatory cytokines and the ratio of macrophage subtypes were detected.ResultsThe OFA group had significantly longer extubation time and PACU stay, whereas the OA group had significantly higher rate of hypotension, higher doses of norepinephrine, higher PONV and dizziness rate, and delayed flatus passage time. The QoR-15 score on postoperative 24 h was significantly higher in OFA group than in OA group. At the end of or after the surgery, the OFA group had higher levels of interleukin (IL)-12, IL-1β, tumor necrosis factor (TNF)-α, CD68+CD163− macrophage rate, but lower levels of IL-10, transforming growth factor (TGF)-β, and CD68+CD163+ macrophage rate, indicating OFA attenuated perioperative immunosuppression by diminishing M2 and promoting M1 macrophage polarization. And the reversal tendency is more obvious in LAGC patients with neoadjuvant PD-1 inhibitor.ConclusionsThe OFA may attenuate perioperative immunosuppression by diminishing M2 and promoting M1 macrophage polarization in LAGC patients with neoadjuvant PD-1 inhibitor.Clinical trial registrationhttp://gcpgl.sysucc.org.cn, identifier 2022-FXY-001