A Unique Approach of Using the Odor Profile Method and Persistency Curves to Evaluate Odor Nuisance

Odor complaints have become the majority of all air quality complaints in recent years. Evaluating an odor nuisance requires sensory analyses to determine the cause of the problem; and chemical analyses to control the problem since chemicals cause odors. The best choice of sensory methods is the Odor Profile Method (OPM), using an Odor Wheel to identify the odor(s). The best choice of chemical analysis is based upon the chemicals that caused the perceived odors and were identified by the odor wheel.The OPM can determine odor character, intensity, frequency, and duration for each odor within an odorous mixture at a specific time and location. The OPM is based upon the Weber-Fechner law that states:Odor Intensity = k Log (concentration of the chemical(s) causing the odor) + bPersistency curves can determine odor persistence upon dilution. Persistency curves include two types, the Weber-Fechner Curve and the Odor Dilution Curve. Weber-Fechner curve indicates the persistence of each odorous compound and the Odor Dilution Curve indicates the persistence of each odor type that are perceived in an odor mixture.An odor panel using the OPM has successfully evaluated if the closure of a Landfill from 6-9 AM has minimized the odor complaints related to this Landfill. The OPM results have shown the odor characters of the possible odors produced by the Landfill to be rancid, rancid-sweet, rotten vegetable, and sewery/fecal. The intensities of these odor characters were between 1 - 4. Only 1.3% of the samples produced an odor related to the Landfill, and the duration of odor events was all less than 40 minutes. Reproduction of the Weber-Fechner curves of the selected six odorants was done by four panelists. The determined Weber-Fechner curves were similar to the previous study, but the regression lines were not identical. The determined Weber-Fechner curves were used as the reference for the same panelist to understand odor persistency at the Energy Development Facility. The Odor Dilution Curve at the EDF biofilter confirmed the information provided by the Weber-Fechner curves. The musty odor (caused by IPMP, MIB etc.) dissipates slower than the fecal odor (caused by skatole, indole, etc.) and the sulfur odor (caused by dimethyl sulfide, dimethyl disulfide, etc.). The masking effect that the musty odor was covered by sulfur odor and fecal odor also can be explained by the cross-over of Weber-Fechner curves. The slope, k of the Weber-Fechner curves upon dilution of each odor type using the OPM method can provide an insight into the persistency of each specific odorant. This information was used to evaluate the performance of an odor masking agent. Lower k value, higher application concentration or application at a further distance of the masking agent might solve the poor performance of the odor masking agent. An odor attribution study was conducted to evaluate three facilities: an Energy Development Facility, a Resource Recovery Park and a Wastewater Treatment Plant. Results of the OPM data only shown one out of three facilities has consistency between the air sample from the inside locations of the facility with the air sample from the downwind location. The Odor Dilution Curve results showed that with higher dilution ratios, most of the odor characteristics disappeared. However, in some locations, the Odor Dilution Curve showed the unmasking effect of musty odor. The musty odor was not detected at the OPM level or at the lower dilutions but started to appear after the higher intensities of fecal, rancid or rotten vegetable odor have disappeared. The Odor Dilution Curves successfully predict odor types that were not shown buy the OPM. The comparison of the Odor Activity Value data and the OPM data proved that OPM as a sensory method works well on telling odor characters and intensities. However, not all the data correlate with each other due to the unknown synergistic or antagonistic effect for odor mixtures. Above all, it has been proved that using only chemical analysis or only odor sensory data is not enough. The combination of the two should be performed for any type of odor attribution study. The OPM have provided predictions on odor nuisances from the source level, the Weber-Fechner curves have provided predictions on the persistence of an odorant and gave valuable suggestion on masking agent selection. Odor Dilution Curves have provided odor predictions at the level of the receptors. This thesis, for the first time, have used the OPM, the Persistency Curves and the combination of the two to evaluate odor nuisance

The Use of the Odor Profile Method with an “Odor Patrol” Panel to Evaluate an Odor Impacted Site near a Landfill

A third-party-trained “Odor Patrol” program was conducted at a school that is about a one-mile distance from a landfill to clarify the odor nuisance problems from the landfill. Every 20 min from 6 to 9 a.m. on school days, the “Odor Profile Method” (OPM) was used with the landfill odor wheel to identify the odor type and intensity of each odor type. This study showed that an Odor Patrol using the OPM can accurately define odor nuisance changes over time and can be used as a method to confirm changes of odor nuisances in a field study. The Odor Patrol only found 13 data inputs of the 1000 data inputs (1.3%) for the 100-day odor monitoring with a landfill odor or trash odor that could cause odor complaints. The Odor Patrol data and the Odor Complaint data compared well. The OPM by an “Odor Patrol” could determine the contribution of the nuisance odors from 6 to 9 a.m. at the school site, about one mile away from the landfill. The study demonstrated a novel approach for odor monitoring by using the Odor Profile Method with an Odor Patrol. The OPM not only confirmed the mitigation of a landfill odor problem, but it also determined odor character, odor intensity, odor frequency and odor duration during this study period. “Landfill gas” was determined to be primarily a rotten vegetable odor with a secondary sewery/fecal odor of lower intensity, and “trash odors” were primarily a rancid and sweet odor with a secondary sewery/fecal and/or rotten vegetable odor of lower intensities generated from trash reaching the landfill. The order of intensity observed from high to low was: Trash odor (Rancid–Sweet) > Rotten vegetable > Sewery/Fecal > Rancid. Thus, trash odor is the major problematic odor from the landfill site. Quality assurance methods were used to remove local odors from the evaluation

TMA-Net: A Transformer-based Multi-scale Attention Network for Surgical Instrument Segmentation

The ability to accurately and automatically segment surgical instruments is one of the important prerequisites for reasonable and stable operation of surgical robots. The utilization of deep learning in medical image segmentation has gained widespread recognition in recent years, leading to the proposition of multiple network models designed for the segmentation of diverse medical images, among which the most effective one is U-Net and its variants. Nevertheless, these existing networks also have various drawbacks, such as limited contextual representation capability, insufficient local feature processing, etc. In order to solve the above problems so that more accurate surgical instrument segmentation performance can be obtained, a transformer-based multi-scale attention network is proposed, referred to as TMA-Net, for surgical instrument segmentation from endoscopic images to serve robot-assisted surgery. To enable more accurate extraction of image features, a dual-branch encoder structure is proposed to obtain stronger contexts. Further, to address the problem that the simple skip connection is insufficient for local feature processing, an attention feature fusion (AFF) module and an additive attention and concatenation (AAC) module are proposed for effective feature learning to filter out the irrelevant information in the low-level features. Furthermore, a multi-scale context fusion (MCF) block is introduced to enhance the local feature maps and capture multi-scale contextual information. The efficacy of proposed TMA-Net is demonstrated through experimentation on publicly available surgical instrument segmentation datasets, including Endovis2017 and UW-Sinus-Surgery-C/L. The results show that proposed TMA-Net outperforms existing methods in terms of surgical instrument segmentation accuracy

Observation of Ξb0→Ξc+Ds−\Xi_b^0 \rightarrow \Xi_c^+ D_s^- and Ξb−→Ξc0Ds−\Xi_b^- \rightarrow \Xi_c^0 D_s^- decays

International audienceThe $\Xi_b^0 \rightarrow \Xi_c^+ D_s^-$ and $\Xi_b^- \rightarrow \Xi_c^0 D_s^-$ decays are observed for the first time using proton-proton collision data collected by the LHCb experiment at a centre-of-mass energy of $\sqrt{s}=13\mathrm{TeV}$, corresponding to an integrated luminosity of $5.1\mathrm{fb}^{-1}$. The relative branching fractions times the beauty-baryon production cross-sections are measured to be \begin{align*} \mathcal{R}\left(\frac{\Xi_b^0}{\Lambda_b^0}\right) \equiv \frac{\sigma\left(\Xi_b^0\right)}{\sigma\left(\Lambda_b^0\right)} \times \frac{\mathcal{B}\left(\Xi_b^0 \rightarrow \Xi_c^+ D_s^-\right)}{\mathcal{B}\left(\Lambda_b^0 \rightarrow \Lambda_c^0 D_s^-\right)} =(15.8\pm1.1\pm0.6\pm7.7)\%, \mathcal{R}\left(\frac{\Xi_b^-}{\Lambda_b^0}\right) \equiv \frac{\sigma\left(\Xi_b^-\right)}{\sigma\left(\Lambda_b^0\right)} \times \frac{\mathcal{B}\left(\Xi_b^- \rightarrow \Xi_c^0 D_s^-\right)}{\mathcal{B}\left(\Lambda_b^0 \rightarrow \Lambda_c^0 D_s^-\right)} =(16.9\pm1.3\pm0.9\pm4.3)\%, \end{align*} where the first uncertainties are statistical, the second systematic, and the third due to the uncertainties on the branching fractions of relevant charm-baryon decays. The masses of $\Xi_b^0$ and $\Xi_b^-$ baryons are measured to be $m_{\Xi_b^0}=5791.12\pm0.60\pm0.45\pm0.24\mathrm{MeV}/c^2$ and $m_{\Xi_b^-}=5797.02\pm0.63\pm0.49\pm0.29\mathrm{MeV}/c^2$, where the uncertainties are statistical, systematic, and those due to charm-hadron masses, respectively