Conditional Independence by Typing

A central goal of probabilistic programming languages (PPLs) is to separate modelling from inference. However, this goal is hard to achieve in practice. Users are often forced to re-write their models to improve efficiency of inference or meet restrictions imposed by the PPL. Conditional independence (CI) relationships among parameters are a crucial aspect of probabilistic models that capture a qualitative summary of the specified model and can facilitate more efficient inference. We present an information flow type system for probabilistic programming that captures conditional independence (CI) relationships and show that, for a well-typed program in our system, the distribution it implements is guaranteed to have certain CI-relationships. Further, by using type inference, we can statically deduce which CI-properties are present in a specified model. As a practical application, we consider the problem of how to perform inference on models with mixed discrete and continuous parameters. Inference on such models is challenging in many existing PPLs, but can be improved through a workaround, where the discrete parameters are used implicitly , at the expense of manual model re-writing. We present a source-to-source semantics-preserving transformation, which uses our CI-type system to automate this workaround by eliminating the discrete parameters from a probabilistic program. The resulting program can be seen as a hybrid inference algorithm on the original program, where continuous parameters can be drawn using efficient gradient-based inference methods, while the discrete parameters are inferred using variable elimination. We implement our CI-type system and its example application in SlicStan: a compositional variant of Stan. 1

Enabling Safe and Sustainable Medical Deliveries by Connected Autonomous Freight Vehicles Operating within Dangerous Goods Regulations

Health service providers in developed nations are responsible for 5% of their national carbon emissions, much of which originate from transport and supply chains. Connected autonomous freight vehicles (CAV-Fs) offer the potential to reduce this impact and enable lower cost operations, with trials being explored across the world. Transportation and carriage regulations, particularly in relation to the movement of dangerous goods (DG) such as medicines and diagnostic specimens, have not been developed for and applied to this new transport mode, particularly where loads are unaccompanied. Through an audit of current legislation and practice, this paper evaluates current DGs regulations applied to the transportation of medical products and medicines by autonomous road vehicles. Where existing regulations are not appropriate for CAV-Fs, recommendations and adaptations have been proposed to support safe and practical application. Remote monitoring and tracking of vehicles are critical for ensuring load security, quick and effective incident response, and management of documents and communications between parties. Loading/unloading procedures are significantly more important than in crewed vehicles, with load segregation and recording of all activity being of key importance. Other recommendations relate to training provision, vehicle specifications, and product health monitoring.

Improving Humidity Sensing of Black Phosphorus Nanosheets by Co-Doping Benzyl Viologen and Au Nanoparticles

Black phosphorus (BP) is a two-dimensional and layered elemental semiconductor that is very sensitive to the subtle fluctuation of relative humidity (RH). However, the practical application of BP material was undesirably plagued by the irreversible degradation under moisture/oxygen atmospheres. To circumvent this limitation, here we prepared BP co-doped with benzyl viologen (BV) and Au nanoparticles as the sensing layer and explored the humidity-sensing performance at room temperature (20 oC). Unlike BP (BP-BV) counterparts, BP-Au (BP-BV-Au) sensors demonstrated unvaried response polarity with increasing RH. And BV introduction improved the recovery characteristics. Additionally, the ternary BP-BV-Au sensors delivered decent selectivity and negligible hysteresis. On the one hand, the in situ reduction of Au nanoparticles consumed lone electron pairs within BP, suppressed the interaction with ambient moisture/oxygen, and improved the operation stability and recovery. On the other hand, hydrophobic BV as the protection layer further hindered water attachment. This co-doping behavior reduced the hole density and ensured the predominant interaction between low-energy sorption sites within BP and water molecules, thus leading to a larger resistance modulation (i.e., stronger response) and quicker reaction kinetics. This work offered a feasible method to propel the practical application and enriched the sensing mechanisms of BP-based humidity sensors.

Entropy Variations of Multi-Scale Returns of Optimal and Noise Traders Engaged in “Bucket Shop Trading”

In this paper a comparative, coarse grained, entropy data analysis of multi-scale log-returns distribution, produced by an ideal “optimal trader” and one thousand “noise traders” performing “bucket shop” trading, by following four different financial daily indices, is presented. A sole optimal trader is assigned to each one of these four analyzed markets, DJIA, IPC, Nikkei and DAX. Distribution of differential entropies of the corresponding multi-scale log-returns of the optimal and noise traders are calculated. Kullback-Leiber distances between the different optimal traders returns distributions are also calculated and results discussed. We show that the entropy of returns distribution of optimal traders for each analyzed market indeed reaches minimum values with respect to entropy distribution of noise traders and we measure this distance in σ units for each analyzed market. We also include a discussion on stationarity of the introduced multi-scale log-returns observable. Finally, a practical application of the obtained results related with ranking markets by their entropy measure as calculated here is presented.

Physiological, Transcriptomic Investigation on the Tea Plant Growth and Yield Motivation by Chitosan Oligosaccharides

Chitosan oligosaccharides (COS) has been abundantly studied for its application on regulating plant growth of many horticultural and agricultural crops. We presented here the effect of COS on tea plant growth and yield by physiological and transcriptomic checking. The results showed that COS treatment can enhance the antioxidant activity of superoxide dismutase (SOD) and peroxidase (POD) and increase the content of chlorophyll and soluble sugar in tea plants. The field trail results show that COS treatment can increase tea buds’ density by 13.81–23.16%, the weight of 100 buds by 15.94–18.15%, and the yield by 14.22–21.08%. Transcriptome analysis found 5409 COS-responsive differentially expressed genes (DEGs), including 3149 up-regulated and 2260 down-regulated genes, and concluded the possible metabolism pathway that responsible for COS promoting tea plant growth. Our results provided fundamental information for better understanding the molecular mechanisms for COS’s acting on tea plant growth and yield promotion and offer academic support for its practical application in tea plant.

The Fusion of MRI and CT in the Planning of Brachytherapy for Cancer of the Uterine Cervix

Introduction: tumors of the uterine cervix are among the most common carcinomas in women. Intracervical brachytherapy is an indispensable part of curative treatment. Although the tumor is significantly more recognizable in MRI than in CT, the practical application of MRI in brachytherapy planning is still difficult. The present study examines the technical possibilities of merging CT and MRI. Materials and Methods: the treatment files and imaging of all 53 patients who had been irradiated by image-guided adaptive brachytherapy (IGABT) between January 2019 and August 2021 at the Department of Radiotherapy of the Hannover Medical School were evaluated, retrospectively. Patients were treated first with an external beam radiotherapy (EBRT) combined with simultaneous chemotherapy. After an average of 4.2 weeks, the preparation for IGABT began. The clinical target volume (CTV) for brachytherapy was contoured first in an MRI acquired before starting EBRT (MRI 1) and once more in a second MRI just before starting IGABT (MRI 2). Then, after inserting the intravaginal applicator, a CT-scan was acquired, and the CTV was contoured in the CT. Finally, the recordings of MRI 1, MRI 2, and the CT were merged, and the congruence of CTVs was quantitatively evaluated. Results: the CTV delineated in MRI 2 was, on average, 28% smaller than that in MRI 1 after an average applied radiation dose of 42 Gy. The CTV delineated in the CT covered an average of no more than 80.8% of the CTV delineated in MRI 2. The congruence of CTVs was not superior in patients with a smit sleeve in the cervical channel, with a 3D-volumetric MRI or with a contrast-enhanced sequence for MRI. Conclusion: the anatomical shape and position of the uterus is significantly changed by introducing a vaginal applicator. Despite the superior delimitability of the tumor in MRI, brachytherapy cannot be reliably planned by the image fusion of an MRI without a vaginal applicator.

Group decision-making with Fermatean fuzzy soft expert knowledge

With the rapid growth of population, the global impact of solar technology is increasing by the day due to its advantages over other power production technologies. Demand for solar panel systems is soaring, thus provoking the arrival of many new manufacturers. Sale dealers or suppliers face an uncertain problem to choose the most adequate technological solution. To effectively address such kind of issues, in this paper we propose the Fermatean fuzzy soft expert set model by combining Fermatean fuzzy sets and soft expert sets. We describe this hybrid model with numerical examples. From a theoretical standpoint, we demonstrate some essential properties and define operations for this setting. They comprise the definitions of complement, union and intersection, the OR operation and the AND operation. Concerning practice in this new environment, we provide an algorithm for multi-criteria group decision making whose productiveness and authenticity is dutifully tested. We explore a practical application of this approach (that is, the selection of a suitable brand of solar panel system). Lastly, we give a comparison of our model with certain related mathematical tools, including fuzzy and intuitionistic fuzzy soft expert set models.