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Background. General anesthesia (GA) is the core means of surgical intervention, mainly used for analgesia and anxiety relief. Therefore, it is necessary to understand the laboratory and clinical research results during induction of GA. Penehyclidine hydrochloride (PHCD) combined with atropine sulfate (Atr) has the potential to induce GA. However, the role of PHCD combined with Atr during tracheal intubation under GA remains unclear. Objective. The research is aimed at exploring the effects of preoperative PHCD or Atr on adverse reactions (ARs) in patients during tracheal intubation under general anesthesia (GA). Methods. This study retrospectively enrolled 473 patients who underwent surgery under GA induction and divided them into a research group ( n = 234 ) and a control group ( n = 239 ) according to preoperative use of PHCD (with or without). Both groups of patients were given Atr postoperatively and nursing intervention. Anesthesia-related indexes, ARs, and hemodynamics were observed and compared between the two groups. Results. There were no significant differences in anesthesia-related indexes and hemodynamics between the research group and the control group. The incidence of blurred vision and diplopia in the research group was higher than that in the control group. Conclusion. Preoperative PHCD combined with postoperative Atr should be avoided in clinical practice, or Atr rather than PHCD should be used preoperatively, so as to reduce the occurrence of blurred vision, diplopia, and other ARs.

Static analysis tools are widely used for vulnerability detection as they understand programs with complex behavior and millions of lines of code. Despite their popularity, static analysis tools are known to generate an excess of false positives. The recent ability of Machine Learning models to understand programming languages opens new possibilities when applied to static analysis. However, existing datasets to train models for vulnerability identification suffer from multiple limitations such as limited bug context, limited size, and synthetic and unrealistic source code. We propose D2A, a differential analysis based approach to label issues reported by static analysis tools. The D2A dataset is built by analyzing version pairs from multiple open source projects. From each project, we select bug fixing commits and we run static analysis on the versions before and after such commits. If some issues detected in a before-commit version disappear in the corresponding after-commit version, they are very likely to be real bugs that got fixed by the commit. We use D2A to generate a large labeled dataset to train models for vulnerability identification. We show that the dataset can be used to build a classifier to identify possible false alarms among the issues reported by static analysis, hence helping developers prioritize and investigate potential true positives first., Accepted to the 43rd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP '21)

Objective There were COVID-19 patients with SARS-COV-2 nucleic acid long-term positive. This article aims to understand the relevant factors that affect SARS-COV-2 clearance time. Methods The clinical data of 115 COVID-19 patients with SARS-COV-2 nucleic acid positive time exceeding 14 days were collected retrospectively, and the relationship between clinical characteristics, chest CT scans, blood cells, biochemical indicators, and the time of viral nucleic acid turning negative were analyzed. Results The time from symptom onsets to nucleic acid turning negative was (32.5 ± 8.7) days in this group of patients. The time of nucleic acid turning negative: no fever group was longer than fever group, diabetes group was longer than no comorbidity group, elevated levels of ALT (alanine aminotransferase), or GLU (fasting blood glucose) group, decreased levels of ALB (albumin) group or HDLC (high-density lipoprotein cholesterol) group was longer than it's normal group separately (P, Highlights • There were some COVID-19 patients whose virus clearance time exceeded 15 days. • Liver damage and hyperglycemia are risk factors that prolong virus clearance time. • Some COVID-19 Patients have abnormal lipid metabolism. • Reduced HDLC was a risk factor contributing to the prolonged virus clearance time.

This work explores the signal awareness of AI models for source code understanding. Using a software vulnerability detection use case, we evaluate the models' ability to capture the correct vulnerability signals to produce their predictions. Our prediction-preserving input minimization (P2IM) approach systematically reduces the original source code to a minimal snippet which a model needs to maintain its prediction. The model's reliance on incorrect signals is then uncovered when the vulnerability in the original code is missing in the minimal snippet, both of which the model however predicts as being vulnerable. We measure the signal awareness of models using a new metric we propose- Signal-aware Recall (SAR). We apply P2IM on three different neural network architectures across multiple datasets. The results show a sharp drop in the model's Recall from the high 90s to sub-60s with the new metric, highlighting that the models are presumably picking up a lot of noise or dataset nuances while learning their vulnerability detection logic. Although the drop in model performance may be perceived as an adversarial attack, but this isn't P2IM's objective. The idea is rather to uncover the signal-awareness of a black-box model in a data-driven manner via controlled queries. SAR's purpose is to measure the impact of task-agnostic model training, and not to suggest a shortcoming in the Recall metric. The expectation, in fact, is for SAR to match Recall in the ideal scenario where the model truly captures task-specific signals., Authors Sahil Suneja, Yunhui Zheng, and Yufan Zhuang contributed equally to this research. FSE 2021

The intriguing features of two-dimensional (2D) materials such as better charge carrier separation and abundant surface reaction sites endow them with potential applications as cocatalysts in photocatalysis. In this paper, a ternary 2D nickel hydroxide/sulfides nanosheet composed of Ni(OH)2, Ni3S2 and NixS6 was loaded on CdS nanospheres by a simple chemical deposition route. The composition of nickel hydroxide/sulfides was determined clearly through an overall analysis using X-ray diffraction, transmission electron microscopy and X-ray photoelectron spectroscopy. Mott-Schottky, electrochemical impedance, steady-state and time-resolved photoluminescence spectroscopy were used to investigate the charge transfer process in CdS and Ni(OH)2/Ni3S2/NixS6-CdS. The results confirm that a synergistic effect of Ni(OH)2/Ni3S2/NixS6 on CdS has occurred under light irradiation, where the Ni(OH)2 and nickel sulfides act as hole storage and surface reaction sites, respectively, to promote the charge transfer on CdS. The improved charge transfer and separation efficiency as well as the increased surface reaction sites in Ni(OH)2/Ni3S2/NixS6-CdS finally result in a dramatically improved photocatalytic performance. The photocatalytic H2 evolution rate of Ni(OH)2/Ni3S2/NixS6-CdS is ca. 46 times higher than that of CdS, and the photocatalytic stability of CdS is also improved substantially under visible light irradiation.

In recent years, string solvers have become an essential component in many formal verification, security analysis, and bug-finding tools. Such solvers typically support a theory of string equations, the length function, and the regular-expression membership predicate. These enable considerable expressive power, which comes at the cost of slow solving time, and in some cases even non-termination. We present three techniques, designed for word-based SMT string solvers, to mitigate these problems: (1) detecting overlapping variables, which is essential to avoiding common cases of non-termination; (2) pruning of the search space via bi-directional integration between the string and integer theories, enabling new cross-domain heuristics; and (3) a binary search based heuristic, allowing the procedure to skip unnecessary string length queries and converge on consistent length assignments faster for large strings. We have implemented above techniques atop the Z3-str solver, resulting in a significantly more robust and efficient solver, dubbed Z3str2, for the quantifier-free theory of string equations, the regular-expression membership predicate, and linear arithmetic over the length function. We report on a series of experiments over four sets of challenging real-world benchmarks, where we compare Z3str2 with five different string solvers: S3, CVC4, Kaluza, PISA and Stranger. Each of these tools utilizes a different solving strategy and/or string representation (based e.g. on words, bit vectors or automata). The results point to the efficacy of our proposed techniques, which yield dramatic performance improvement. We also demonstrate performance improvements enabled by Z3str2 in the context of symbolic execution for string-manipulating programs. We observe that the techniques presented here are of broad applicability, and can be integrated into other string solvers to improve their performance.

Following the previous efforts of INTERACT (INTERcomparison of Aerosol and Cloud Tracking), the INTERACT-II campaign used multi-wavelength Raman lidar measurements to assess the performance of an automatic compact micro-pulse lidar (MiniMPL) and two ceilometers (CL51 and CS135) in providing reliable information about optical and geometric atmospheric aerosol properties. The campaign took place at the CNR-IMAA Atmospheric Observatory (760 ma.s.l.; 40.60∘ N, 15.72∘ E) in the framework of ACTRIS-2 (Aerosol Clouds Trace gases Research InfraStructure) H2020 project. Co-located simultaneous measurements involving a MiniMPL, two ceilometers and two EARLINET multi-wavelength Raman lidars were performed from July to December 2016. The intercomparison highlighted that the MiniMPL range-corrected signals (RCSs) show, on average, a fractional difference with respect to those of CNR-IMAA Atmospheric Observatory (CIAO) lidars ranging from 5 to 15 % below 2.0 km a.s.l. (above sea level), largely due to the use of an inaccurate overlap correction, and smaller than 5 % in the free troposphere. For the CL51, the attenuated backscatter values have an average fractional difference with respect to CIAO lidars < 20–30 % below 3 km and larger above. The variability of the CL51 calibration constant is within ±46 %. For the CS135, the performance is similar to the CL51 below 2.0 kma.s.l., while in the region above 3 kma.s.l. the differences are about ±40 %. The variability of the CS135 normalization constant is within ±47 %. Finally, additional tests performed during the campaign using the CHM15k ceilometer operated at CIAO showed the clear need to investigate the CHM15k historical dataset (2010–2016) to evaluate potential effects of ceilometer laser fluctuations on calibration stability. The number of laser pulses shows an average variability of 10 % with respect to the nominal power which conforms to the ceilometer specifications. Nevertheless, laser pulses variability follows seasonal behavior with an increase in the number of laser pulses in summer and a decrease in winter. This contributes to explain the dependency of the ceilometer calibration constant on the environmental temperature hypothesized during INTERACT.

The INTERACT-II (INTERcomparison of Aerosol and Cloud Tracking) campaign, performed at the CNR-IMAA Atmospheric Observatory (760 m a.s.l., 40.60° N, 15.72° E), aims to evaluate the performances of commercial automatic lidars and ceilometers for atmospheric aerosol profiling, through the comparison with Potenza EARLINET (European Aerosol Research Lidar NETwork) lidars. The results of the campaign and the overall lesson learnt within INTERACT-I and INTERACT-II ACTRIS campaigns will be presented.

In this paper, we introduce StringFuzz: a modular SMT-LIB problem instance transformer and generator for string solvers. We supply a repository of instances generated by StringFuzz in SMT-LIB 2.0/2.5 format. We systematically compare Z3str3, CVC4, Z3str2, and Norn on groups of such instances, and identify those that are particularly challenging for some solvers. We briefly explain our observations and show how StringFuzz helped discover causes of performance degradations in Z3str3.

In this paper, we present a new ad budget draining attack. By repeatedly pulling ads from targeted advertisers using crafted browsing profiles, we are able to reduce the chance of showing their ads to real-human visitors and trash the ad budget. From the advertiser profiles collected by an automated crawler, we infer advertising strategies, train satisfying browsing profiles and launch large-scale attacks. We evaluate our methods on 291 public advertisers selected from Alexa Top 500, where we successfully reveal the targeting strategies used by 87% of the advertisers we considered. We also executed a series of attacks against a controlled advertiser and 3 real-world advertisers within the ethical and legal boundary. The results show that we are able to fetch 40,958 ads and drain up to $155.89 from the targeted advertisers within an hour.

The intriguing features of two-dimensional (2D) materials such as better charge carrier separation and abundant surface reaction sites endow them with potential applications as cocatalysts in photocatalysis. In this paper, a ternary 2D nickel hydroxide/sulfides nanosheet composed of Ni(OH)

Many JavaScript applications perform HTTP requests to web APIs, relying on the request URL, HTTP method, and request data to be constructed correctly by string operations. Traditional compile-time error checking, such as calling a non-existent method in Java, are not available for checking whether such requests comply with the requirements of a web API. In this paper, we propose an approach to statically check web API requests in JavaScript. Our approach first extracts a request's URL string, HTTP method, and the corresponding request data using an inter-procedural string analysis, and then checks whether the request conforms to given web API specifications. We evaluated our approach by checking whether web API requests in JavaScript files mined from GitHub are consistent or inconsistent with publicly available API specifications. From the 6575 requests in scope, our approach determined whether the request's URL and HTTP method was consistent or inconsistent with web API specifications with a precision of 96.0%. Our approach also correctly determined whether extracted request data was consistent or inconsistent with the data requirements with a precision of 87.9% for payload data and 99.9% for query data. In a systematic analysis of the inconsistent cases, we found that many of them were due to errors in the client code. The here proposed checker can be integrated with code editors or with continuous integration tools to warn programmers about code containing potentially erroneous requests., International Conference on Software Engineering, 2017

Nowadays, a rapidly increasing number of web users are using Ad-blockers to block online advertisements. Ad-blockers are browser-based software that can block most Ads on the websites, speeding up web browsers and saving bandwidth. Despite these benefits to end users, Ad-blockers could be catastrophic for the economic structure underlying the web, especially considering the rise of Ad-blocking as well as the number of technologies and services that rely exclusively on Ads to compensate their cost. In this paper, we introduce WebRanz that utilizes a randomization mechanism to circumvent Ad-blocking. Using WebRanz, content publishers can constantly mutate the internal HTML elements and element attributes of their web pages, without affecting their visual appearances and functionalities. Randomization invalidates the pre-defined patterns that Ad-blockers use to filter out Ads. Though the design of WebRanz is motivated by evading Ad-blockers, WebRanz also benefits the defense against bot scripts. We evaluate the effectiveness of WebRanz and its overhead using 221 randomly sampled top Alexa web pages and 8 representative bot scripts.

A group of functionalized oxazoles were synthesized in moderate to good yields from enamides via phenyliodine diacetate (PIDA)-mediated intramolecular cyclization. The main advantageous features of the present method include its broad substrate scope and the heavy-metal-free characteristic of the oxidative carbon-oxygen bond formation process.

Calling contexts (CCs) are very important for a wide range of applications such as profiling, debugging, and event logging. Most applications perform expensive stack walking to recover contexts. The resulting contexts are often explicitly represented as a sequence of call sites and hence are bulky. We propose a technique to encode the current calling context of any point during an execution. In particular, an acyclic call path is encoded into one number through only integer additions. Recursive call paths are divided into acyclic subsequences and encoded independently. We leverage stack depth in a safe way to optimize encoding: If a calling context can be safely and uniquely identified by its stack depth, we do not perform encoding. We propose an algorithm to seamlessly fuse encoding and stack depth-based identification. The algorithm is safe because different contexts are guaranteed to have different IDs. It also ensures contexts can be faithfully decoded. Our experiments show that our technique incurs negligible overhead (0-6.4 percent). For most medium-sized programs, it can encode all contexts with just one number. For large programs, we are able to encode most calling contexts to a few numbers. We also present our experience of applying context encoding to debugging crash-based failures.

Well-dispersed and long-term stable nano-CaCO3/polyol dispersions were prepared by a mechanochemical approach with the aid of poly (propylene glycol) phosphate ester (PPG-P). Polyurethane (PU)/CaCO3 nanocomposites were prepared by further in situ polymerization with 6 wt% nano-CaCO3. The microstructure and dispersion of nano-CaCO3 in the nanocomposites were investigated. It was found that well dispersion was obtained up to 6 wt% of the surface treated CaCO3 loading for PU/CaCO3 nanocomposite. The segmented structures of PU were not interfered by the presence of nano-CaCO3 in these nanocomposites as evidenced by Fourier transform infrared. Compared with the pure PU, a significant improvement in thermal stability was observed with the addition of 6 wt% of the surface treated CaCO3. The experimental results suggested that the properties of nanocomposites were correlated with the dispersion of nano-CaCO3 in PU and the interfacial interactions between nano-CaCO3 and polymer matrix.

Zinc borate (2ZnO·3B2O3·3.5H2O) has relatively high dehydration on-set temperature which property permits processing in a wide range of polymer system. But zinc borate particles are hardly dispersed in a polymer matrix so that they prevent their using in industry. To address this problem, we synthesized hydrophobic zinc borate (2ZnO·3B2O3·3.5H2O) nanoflakes by employing solid–liquid reaction of zinc oxide (ZnO) and boric acid (H3BO3) in the presence of oleic acid. This method does not bring pollution. By conducting morphological and microscopic analyses, we found that this compound displayed nanoflake morphology with particle size of around 100–200 nm, thickness less than 100 nm and there were uniform mesopores with the diameter about 10 nm within the particles. Furthermore, our products had an effect on flame retardant of polyethylene, especially when the zinc borate was modified by oleic acid.

Copper borate particles are synthesized via one-step precipitation reaction in aqueous solution of sodium borate (Na2B4O7 10H2O) and copper sulfate (CuSO4 5H2O) with phosphate ester as the modifying agent. One-dimension (1D) and two-dimension (2D) copper borate nano/microstructures with different morphologies are well translated through thermally induced crystal growth processes with the special surfactant at different synthetic temperatures. The 2D copper borate particles are hydrophobic, and the friction coefficients of basic oil can be decreased with adding the hydrophobic copper borate.

Zinc borate (α-Zn(BO 2 ) 2 ) nanocrystalline powders doped with Eu 3+ ions had been prepared using co-precipitation method. α-Zn(BO 2 ) 2 :Eu 3+ had been characterized by X-ray diffraction, TEM, FT-IR and luminescence spectra. They showed different emissions of Eu 3+ corresponding to different transitions which lied in the wavelength range from 530 to 850 nm. One particular transition 5 D 0 → 7 F 2 which was centered at 615 nm had been known as a hypersensitive emission which obeyed the selection rule of (Δ J = 2) resulting in a strong and intense red emission. The results showed that the luminescence intensity of Eu 3+ had been enhanced greatly.

Hydrophobic magnesium hydroxide (Mg(OH)2) nanoparticles were successfully synthesized via a one-step solution precipitation method with octadecyl dihydrogen phosphate (n-C18H37OPO3H2, ODP) as a surface modifier. The ODP was used to control the growth of crystal in the c direction (direction perpendicular to the layers) and to modify the surface property of the Mg(OH)2 particles produced from the precipitation. Measurements of relative contact angle and active ratio indicated that Mg(OH)2 samples were hydrophobic. The samples were characterized by using field emission scanning electron microscope (SEM), X-ray diffraction (XRD), infrared analysis (IR). The mechanism of influence of ODP on the surface property and morphology of Mg(OH)2 was discussed. Furthermore, from the results of the thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) tests, it was confirmed that small amount of magnesium alkyl phosphate on the surface of Mg(OH)2 particles enhanced the thermal stability of the low density polyethylene(LDPE)/Mg(OH)2.

The single crystal surface-modified barium borate uniform nanorods were successfully synthesized via a wet method using Na 2 B 4 O 7 ·10H 2 O and Ba(NO 3 ) 2 as raw materials in situ aqueous solution, with oleic acid (OA) as the modifying agent. Oleic acid was used to control the size and modify the surface property of the nanorods prepared from the precipitation reaction. X-ray diffraction (XRD), infrared (IR) spectra, scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were used to characterize the microstructure and morphology of the products. It can be inferred that the prepared materials exhibited nanorod morphology with a length of about 200–400 nm and the average diameter about 20 nm. Moreover, the modified BaB 2 O 4 nanorods as oil additives which improve the load-carrying capacity and antiwear property of the base oil can decrease the friction coefficient very well.

The polycrystalline and hydrophobic zinc borate (Zn 2 B 6 O 11 ·3H 2 O) nanoplatelets were in situ successfully synthesized via one-step precipitation reaction in aqueous solution of Na 2 B 4 O 7 ·10H 2 O and ZnSO 4 ·7H 2 O with oleic acid as the modifying agent. The microstructures and morphology of the as-obtained samples were studied by X-ray diffraction (XRD), scanning electron microscopy (SEM) equipped with an energy-dispersive X-ray spectrometer (EDS), transmission electron microscopy (TEM) and thermogravimetric analysis (TGA). Measurements of the relative water contact angle and the active ratio indicated that Zn 2 B 6 O 11 ·3H 2 O samples were hydrophobic. It had been found that the as-prepared materials displayed nanoplatelet morphology with average diameters 100–500 nm and thickness 30 ± 5 nm and the morphology and size of the samples were controlled effectively.

Deep learning (DL) training-as-a-service (TaaS) is an important emerging industrial workload. The unique challenge of TaaS is that it must satisfy a wide range of customers who have no experience and resources to tune DL hyper-parameters, and meticulous tuning for each user's dataset is prohibitively expensive. Therefore, TaaS hyper-parameters must be fixed with values that are applicable to all users. IBM Watson Natural Language Classifier (NLC) service, the most popular IBM cognitive service used by thousands of enterprise-level clients around the globe, is a typical TaaS service. By evaluating the NLC workloads, we show that only the conservative hyper-parameter setup (e.g., small mini-batch size and small learning rate) can guarantee acceptable model accuracy for a wide range of customers. We further justify theoretically why such a setup guarantees better model convergence in general. Unfortunately, the small mini-batch size causes a high volume of communication traffic in a parameter-server based system. We characterize the high communication bandwidth requirement of TaaS using representative industrial deep learning workloads and demonstrate that none of the state-of-the-art scale-up or scale-out solutions can satisfy such a requirement. We then present GaDei, an optimized shared-memory based scale-up parameter server design. We prove that the designed protocol is deadlock-free and it processes each gradient exactly once. Our implementation is evaluated on both commercial benchmarks and public benchmarks to demonstrate that it significantly outperforms the state-of-the-art parameter-server based implementation while maintaining the required accuracy and our implementation reaches near the best possible runtime performance, constrained only by the hardware limitation. Furthermore, to the best of our knowledge, GaDei is the only scale-up DL system that provides fault-tolerance.

Copper borate particles had been successfully coated with spherical SiO2 particles through a self-assembly process. The resulted Cu(BO2)2/SiO2 particles were characterized by X-ray diffraction (XRD), infrared spectra (IR), transmission electron microscopy (TEM) and scanning electron microscopy (SEM). SiO2 particles deposited onto copper borate surface. The Cu(BO2)2 particles, with an average size of 200 nm, these or four particles were aggregated with disc distribution. On effects of the silica, copper borate particles were like rod with the diameters about 100 nm and the lengths of 1000 nm. The obtained SiO2 particles had perfect spherical shape with narrow size distribution (average diameters 30 nm). SiO2 particles deposited onto copper borate surface. At present, the reaction mechanism is still under discussion.

Logging and replay is important to reproducing software failures and recovering from failures. Replaying a long execution is time consuming, especially when replay is further integrated with runtime techniques that require expensive instrumentation, such as dependence detection. In this paper, we propose a technique to reduce a replay log while retaining its ability to reproduce a failure. While traditional logging records only system calls and signals, our technique leverages the compiler to selectively collect additional information on the fly. Upon a failure, the log can be reduced by analyzing itself. The collection is highly optimized. The additional runtime overhead of our technique, compared to a plain logging tool, is trivial (2.61% average) and the size of additional log is comparable to the original log. Substantial reduction can be cost-effectively achieved through a search based algorithm. The reduced log is guaranteed to reproduce the failure.

Program control dependence has substantial impact on applications such as dynamic information flow tracking and data lineage tracing (a technique tracking the set of inputs that affects individual outputs). Without considering control dependence, information can leak via implicit channels without being tracked; important inputs may be absent from output lineage. However, considering control dependence may lead to a large volume of false alarms in information flow tracking or undesirably large lineage sets. We identify a special type of control dependence called strict control dependence (SCD). The nature of SCDs highly resembles that of data dependences, reflecting strong correlations between statements and hence should be considered the same way as data dependences in various applications. We formally define the semantics. We also describe a cost-effective design that allows tracing only strict control dependence. Our empirical evaluation shows that the proposed technique has very low overhead and it greatly improves the effectiveness of lineage tracing and taint analysis.

The Slope Imaging Multi-polarization Photon-counting Lidar is an airborne instrument developed to demonstrate laser altimetry measurement methods that will enable more efficient observations of topography and surface properties from space. The instrument was developed through the NASA Earth Science Technology Office Instrument Incubator Program with a focus on cryosphere remote sensing. The SIMPL transmitter is an 11 KHz, 1064 nm, plane-polarized micropulse laser transmitter that is frequency doubled to 532 nm and split into four push-broom beams. The receiver employs single-photon, polarimetric ranging at 532 and 1064 nm using Single Photon Counting Modules in order to achieve simultaneous sampling of surface elevation, slope, roughness and depolarizing scattering properties, the latter used to differentiate surface types. Data acquired over ice-covered Lake Erie in February, 2009 are documenting SIMPL's measurement performance and capabilities, demonstrating differentiation of open water and several ice cover types. ICESat-2 will employ several of the technologies advanced by SIMPL, including micropulse, single photon ranging in a multi-beam, push-broom configuration operating at 532 nm.

Polarization-sensitive detection of elastic backscattered light is useful for detection of cloud phase and depolarizing aerosols. The U.S. Department of Energy's Atmospheric Radiation Measurement Program has deployed micropulse lidar (MPL) for over a decade, but without polarized detection. Adding an actively-controlled liquid crystal retarder provides the capability to identify depolarizing particles by alternately transmitting linearly and circularly polarized light. This represents a departure from established techniques, which transmit exclusively linear polarization or exclusively circular polarization. Mueller matrix calculations yield simple relationships between the well-known linear depolarization ratio delta(linear), the circular depolarization ratio delta(circ), and this MPL depolarization ratio delta(MPL).

Following on from the previous efforts of INTERACT (INTERcomparison of Aerosol and Cloud Tracking), the INTERACT-II campaign used multi-wavelength Raman lidar measurements to assess the performance of an automatic compact micro-pulse lidar (MiniMPL) and two ceilometers (CL51 and CS135), respectively, to provide reliable information about optical and geometric atmospheric aerosol properties. The campaign took place at the CNR-IMAA Atmospheric Observatory (760 m asl, 40.60° N, 15.72° E), in the framework of the ACTRIS-2 (Aerosol Clouds Trace gases Research InfraStructure) H2020 project. Co-located simultaneous measurements involving a MiniMPL, two ceilometers, and two EARLINET multi-wavelength Raman lidars (MUSA and PEARL) were performed from July to December 2016. Range-corrected signals (RCS) of MiniMPL showed an average difference with respect to MUSA/PEARL RCS of less than 10–15 % below 3.0 km above sea level, largely due to the use of an inaccurate overlap correction, and smaller than 5 % in the free troposphere. For the CL51, the average difference with respect to MUSA/PEARL attenuated backscatter is