Criticality Condition Research Articles

Comparison of Neutronic Analysis for 250 MWth Molten Salt Reactor using Monte Carlo OpenMC Code with Different Nuclear Data Libraries of JENDL 5.0, ENDF/B-VII.1, ENDF/B-VIII.0, and JEFF 3.3

Abstract Nuclear data libraries playian important role in the accuracy of neutronic aspect calculations, which determine various factors in a nuclear reactor design. Likewise, deterministic or Monte Carlo methods significantly affect the calculation results. Thus, the combination of nuclear data library and calculation methods needs to be studied carefully. This research will conduct neutronic analysis for the Molten Salt Reactor (MSR) design, one of the Generation IV reactor types with advanced features for future energy resources, using the Monte Carlo-based program OpenMC and different nuclear data libraries. MSR will be designed for a 250 MWth power capacity, operable for five years. The fuel used will be LiF-BeF2 as a coolant and ThF4-UF4 as the fuel. The compositions for each fuel will be optimized to achieve critical conditions during five years of operation. This research aims to evaluate the use of differentinuclear data libraries on reactor criticality conditions, using the nuclear data libraries ENDF/VIII-B, ENDF/VII.0, JEFF 3.3, and JENDL 5.0. To perform the calculation and analysis, MSR chain depletion modification has to be done for different nuclear data libraries. Then, evaluation will be conducted on effective multiplicationifactor (k-eff), neutron flux spectrum, and cross-section. The results of this research show that the use of JENDL 5.0 provides more optimal critical conditions. This is due to the completeness of JENDL 5.0 nuclear data covering 795 nuclide data and higher fission cross-section values. While ENDF/VIII-B has 557 nuclide data evaluations compared to ENDF/VII.0’s 423 nuclide data, more stable k-eff values are shown for the use of ENDF nuclear data libraries. This research is expected to significantly contribute to meeting Indonesia’s energy needs by providing efficient solutions in MSR design in neutronic aspects using the most accurate nuclear data library.

Hydrodynamics coupled circulating-fuel reactor equations in comoving frame and their analytical solutions for molten salt reactors

Effects of relative motion between moving fuel and neutrons are usually not considered in the analysis of Circulating Fuel Reactors (CFRs). In this study, we formulate neutron transport equation for CFRs in a hydrodynamic representation in terms of velocities relative to moving fuel. Using the P1 approximation in the comoving transport equation, the diffusion equation for CFRs is obtained. Mass transport of precursors is considered in the formulations. Comoving frame CFRs equations are analytically solved for critical slab problem and a closed form criticality condition is obtained. Comoving representation has introduced corrections to Eulerian cross sections arising from the acceleration term. Hydrodynamics coupling has posed density modifications to cross sections. A parametric study of corrective terms is carried out to calculate effects of these corrections on a generic MSR and on Molten Salt Breeder Reactor (MSBR).

GPU parallel processing to enable extensive criticality analysis in state estimation

SummaryPower system monitoring relies on the reliability of state estimation (SE) results. SE plays a dominant role in data debugging if sufficient data is available. Criticality analysis (CA) integrates SE as a module in which measurements—taken one‐by‐one or in groups (tuples) of minimal cardinality—are designated crucial. The combinatorial nature of extensive CA (not restricted to identifying low‐cardinality critical tuples) characterizes its computational complexity and imposes challenging limits to go beyond. In simple terms, these limits are established by the number of measurements to be combined, the cardinality of tuples, and the computing time required to check the criticality condition. This paper proposes an innovative computational solution to expand CA limits found to date in the literature. A framework with multi‐threads designed cleverly on a graphics processing unit (GPU) parallel processing environment is built. The conceived architecture favors evaluating massive measurement combinations of diverse cardinality in extensive CA. Numerical results reveal significant speed‐ups with the proposed approach, contrasting with those reported in research efforts published so far.

Effects of feed conditioning temperature on pellet quality, growth performance, intestinal development and blood parameters of geese from 1 to 28 d of age

A 28-d experiment was conducted to investigate the effects of feed-conditioning temperature on the pellet quality, growth performance, intestinal development, and blood parameters of geese. A total of 180 one-day-old White Yuzhou goslings were randomly allotted to 5 treatment groups, with 6 replicates containing 6 birds each. Five diets were conditioned at 65, 70, 75, 80, and 85°C. Body weight and feed intake per pen basis were recorded from the arrival to the end of the trial. Blood and small intestine samples were collected on d 28 for analysis. The results showed that the pellet durability index (PDI), pellet hardness, and gelatinisation degree of starch (GDS) increased with increasing conditioning temperature (P < 0.05). The final body weight (FBW), average daily gain (ADG) and average daily feed intake (ADFI) of goslings significantly increased when conditioning temperature increased from 65 or 70°C to 80 or 85°C (P < 0.05), accompanied by unaffected feed conversion ratio (FCR) (P > 0.05). The villus height to crypt depth ratio (VH/CD) in the duodenum and ileum improved with increasing conditioning temperature (P < 0.05). Additionally, trypsin and amylase activity were enhanced when the conditioning temperature increased from 65 to 85°C (P < 0.05). No significant differences in the carcass traits and blood parameters of goslings were observed among the groups (P > 0.05). Overall, under the present experimental conditions, increasing the steam-conditioning temperature of pelleted feed improved pellet quality, growth performance, intestinal morphology, and digestive enzyme activity in goslings. Based on broken-line regression analysis, the lower critical conditioning temperature for ADG in geese from 1 to 28 d of age was 80.95°C.

On the Transport of Currents

AbstractIn this work, we consider some evolutionary models for k-currents in $$\mathbb {R}^d$$ R d . We study a transport-type equation which can be seen as a generalisation of the transport/continuity equation and can be used to model the movement of singular structures in a medium, such as vortex points/lines/sheets in fluids or dislocation loops in crystals. We provide a detailed overview of recent results on this equation obtained mostly in (Bonicatto et al. Transport of currents and geometric Rademacher-type theorems. arXiv:2207.03922, 2022; Bonicatto et al. Existence and uniqueness for the transport of currents by Lipschitz vector fields. arXiv:2303.03218, 2023). We work within the setting of integral (sometimes merely normal) k-currents, covering in particular existence and uniqueness of solutions, structure theorems, rectifiability, and a number of Rademacher-type differentiability results. These differentiability results are sharp and can be formulated in terms of a novel condition we called “Negligible Criticality condition” (NC), which turns out to be related also to Sard’s Theorem. We finally provide a new stability result for integral currents satisfying (NC) in a uniform way.

A differential equation for inverse point kinetics

The reactivity that represents the percentage departure of a nuclear reactor from its criticality condition is perhaps the most important individual property since it is directly related to the nuclear reactor’s safe operation. Control systems are used to limit the rate of increase in reactivity. The development of systems for monitoring the reactivity, from the nuclear power data acquired from the plant's onboard electronics, requires a constant improvement of the nuclear kinetic models.The transient situations that emerge in nuclear reactors can be predicted from the changes in the neutron flux and, therefore, it is possible to make a sufficiently accurate prediction about the consequences of the disturbances relating to the magnitude of the neutron flux, which varies over time, by using the integral parameters that represent the nuclear reactor core. The point kinetic equations relate these parameters and, in this way, allow the transient situations that can occur in a nuclear reactor to be studied.The reactivity is usually determined by solving the inverse point kinetic equation, which is an integral equation that depends on the nuclear power variation history. In this paper, we present a method to calculate the reactivity through the solution of an ordinary differential equation that does not depend on the nuclear power variation history, then allows the prompt recovery of the reactimeter in case of loss of the nuclear power data acquisition or malfunction of such equipment (Zarei, 2022; Ansari, 1991).The presented paper is organized as follows: Section 1 contains the basic concepts of point kinetics equations and the integral formalism for the reactivity calculation. The mathematical formalism that leads to the differential equation for the reactivity is presented in Section 2. The prompt recovery of the reactivity using the proposed method is discussed in Section 3. The results are shown in Section 4 and the conclusion is presented in Section 5.

Hybrid Integration of Bagging and Decision Tree Algorithms for Landslide Susceptibility Mapping

Landslides represent a significant global natural hazard, threatening human settlements and the natural environment. The primary objective of the study was to develop a landslide susceptibility modeling approach that enhances prediction accuracy and informs land-use planning decisions. The study utilized a hybrid ensemble-based methodology to improve prediction accuracy and effectively capture the complexity of landslide susceptibility patterns. This approach harnessed the power of ensemble models, employing a bagging algorithm with base learners, including the reduced error pruning decision tree (REPTree) and functional tree (FT) models. Ensemble models are particularly valuable because they combine the strengths of multiple models, enhancing the overall performance and robustness of the landslide susceptibility prediction. The study focused on Yanchuan County, situated within the hilly and gully region of China’s Loess Plateau, known for its susceptibility to landslides, using sixteen critical landslide conditioning factors, encompassing topographic, environmental, and geospatial variables, namely elevation, slope, aspect, proximity to rivers and roads, rainfall, the normalized difference vegetation index, soil composition, land use, and more. Model performances were evaluated and verified using a range of metrics, including receiver operating characteristic (ROC) curves, trade-off statistical metrics, and chi-square analysis. The results demonstrated the superiority of the integrated models, particularly the bagging FT (BFT) model, in accurately predicting landslide susceptibility, as evidenced by its high area under the curve area (AUC) value (0.895), compared to the other models. The model excelled in both positive predictive rate (0.847) and negative predictive rate (0.886), indicating its efficacy in identifying landslide and non-landslide areas and also in the F-score metric with a value of 0.869. The study contributes to the field of landslide risk assessment, offering a significant investigation tool for managing and mitigating landslide hazards in Yanchuan County and similar regions worldwide.

Persistence in Perturbed Contact Models in Continuum

Can a local disaster lead to extinction? We answer this question in this work. In the paper [19] we considered contact processes on locally compact metric spaces with state dependent birth and death rates and formulated suf- ficient conditions on the rates that ensure the existence of invariant measures. One of the crucial conditions in [19] was the critical regime condition, which meant the existence of a balance between birth and death rates in average. In the present work, we reject the criticality condition and suppose that the bal- ance condition is violated. This implies that the evolution of the correlation functions of the contact model under consideration is determined by a nonlocal convolution type operator perturbed by a (negative) potential. We show that local peaks in mortality do not typically lead to extinction. We prove that a family of invariant measures exists even without the criticality condition and these measures can be described using the Feynman-Kac formula.

The Descent–Ascent Algorithm for DC Programming

We introduce a bundle method for the unconstrained minimization of nonsmooth difference-of-convex (DC) functions, and it is based on the calculation of a special type of descent direction called descent–ascent direction. The algorithm only requires evaluations of the minuend component function at each iterate, and it can be considered as a parsimonious bundle method as accumulation of information takes place only in case the descent–ascent direction does not provide a sufficient decrease. No line search is performed, and proximity control is pursued independent of whether the decrease in the objective function is achieved. Termination of the algorithm at a point satisfying a weak criticality condition is proved, and numerical results on a set of benchmark DC problems are reported. History: Accepted by Antonio Frangioni, Area Editor for Design &amp; Analysis of Algorithms – Continuous. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0142 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0142 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Alternatives of plant species for forest and land rehabilitation and their potential carbon dioxide absorption in the Wain Watershed, East Kalimantan, Indonesia

This research was conducted in the Wain Watershed in East Kalimantan, Indonesia. The area of the Wain Watershed is around 11,225.68 ha, which includes the Sungai Wain Protection Forest area of 10,025 ha. The objective of this research was to obtain some alternatives of plant species for Forest & Land Rehabilitation and they have the potential to absorb carbon dioxide (CO2) based on the conditions of land cover types, land systems, and land criticality in the Wain watershed. The results of this research indicated that the types of land cover in the non-critical land category were primary dryland forest, secondary dryland forest, and primary mangrove forest; while the potentially critical land cover categories were secondary dryland forest, secondary mangrove forest, mixed dryland agriculture, dryland agriculture, and settlements; slightly-critical land categories, namely shrubs, mixed dryland agriculture, and open land; critical land category was open land. The alternatives of recommended plant species suitable for Forest and Land Rehabilitation, which have the potential to absorb carbon dioxide in the Wain watershed include groups of endemic plant species, multi-purpose tree species, and fast growing species.

Assessment of Groundwater Potential in a Mountainous Area Using Machine Learning and GIS, Rherhaya Basin, High Atlas, Morocco

Mountainous regions are vital for recharging aquifers in plain areas downstream, but understanding the geological, hydrogeological, and climatic factors is crucial to comprehend groundwater processes in these regions. Several parameters, including lithology, topography, secondary porosity, geological structures, and climatic conditions, affect the potential of groundwater in mountainous aquifers. Traditional groundwater modeling tools face several challenges in handling large amounts of real-time data, such as extracting useful features, quantifying uncertainty, and identifying links between different variables. Recent technological advances in artificial intelligence, particularly machine learning, provide solutions for hydrogeological research and applications. This paper focuses on modeling potential zones of groundwater sources using various methodologies based on GIS, spatial remote sensing, and machine learning. The study evaluated three models, Random Forest, Support Vector Machine, and Logistic Regression, in identifying potential groundwater zones in the Rherhaya watershed. More than 200 localized spring points were needed to ensure efficient model learning. The Support Vector Machine model demonstrated the highest performance during the 70/30% split, with a ROC-AUC of 84.4% for the test data. The study identified four critical conditioning factors of groundwater potentiality, including Topographic position index, River Distance, Valley Depth, and Plane Curvature. The models also highlighted the distance to rivers as a significant factor, particularly in the upstream portion of the watershed. The very low potentiality class occupied the largest area (over 32%), followed by low (between 24 and 29%), moderate (12–19%), high (10–14%), and very high (only 9–12%) classes. Only the Support Vector Machine model predicted that 12% of the catchment area had high potential for groundwater resources, indicating its superior performance in identifying high-potential zones. The results offer valuable insights that can aid decision-makers in effectively managing water resources in vulnerable areas.

Component Criticality Analysis for Improved Ship Machinery Reliability

Redundancy in ship systems is provided to ensure operational resilience through equipment backups, which ensure system availability and offline repairs of machinery. The electric power generation system of ships provides the most utility of all systems; hence, it is provided with a good level of standby units to ensure reliable operations. Nonetheless, the occurrence of undesired blackouts is common onboard ships and portends a serious danger to ship security and safety. Therefore, understanding the contributing factors affecting system reliability through component criticality analysis is essential to ensuring a more robust maintenance and support platform for efficient ship operations. In this regard, a hybrid reliability and fault detection analysis using DFTA and ANN was conducted to establish component criticality and related fault conditions. A case study was conducted on a ship power generation system consisting of four marine diesel power generation plants onboard an Offshore Patrol Vessel (OPV). Results from the reliability analysis indicate an overall low system reliability of less than 70 percent within the first 24 of the 78 operational months. Component criticality-using reliability importance measures obtained through DFTA was used to identify all components with more than a 40 percent contribution to subsystem failure. Additionally, machine learning was used to aid the reliability analysis through feature engineering and fault identification using Artificial Neural Network classification. The ANN has identified a failure pattern threshold at about 200 kva, which can be attributed to overheating, hence establishing a link between component failure and generator performance.

The delayed fracture test for viscoelastic elastomers

In a recent contribution, Shrimali and Lopez-Pamies (Extreme Mech Lett 58, 101944, 2023a) have shown that the Griffith criticality condition that governs crack growth in viscoelastic elastomers can be reduced—from its ordinary form involving a historically elusive loading-history-dependent critical tearing energy $$T_c$$ —to a fundamental form that involves exclusively the intrinsic fracture energy $$G_c$$ of the elastomer. The purpose of this paper is to make use of this fundamental form to explain one of the most telltale fracture tests for viscoelastic elastomers, the so-called delayed fracture test.

Abstract 1260: Tumor hypoxia conditions glioblastoma cells for immunosuppression

Abstract Glioblastoma (GBM) is the most common and lethal malignant brain tumor that invariably recurs after standard therapy, with a median survival of only ~16 months. A major determinant of high GBM lethality is the overall immunosuppressive tumor microenvironment and the presence of treatment resistant hypoxic niches. In a recent study, we engineered murine GBM cell line GL261 with a lentiviral HRE driven fluorescent reporter, HRE-UnaG-PEST to study the relationship between tumor hypoxia and immunosuppression. By 3-4 weeks post-transplant in mice, we observed an abundance of UnaG+ GBM cells that were organized as pseudopalisading patterns around necrotic cores, similar to histological features observed in GBM patient specimens. We also observed an accumulation of TAMs and cytotoxic T cells inside hypoxic cores surrounded by UnaG+ GBM cells, which coincided with a gradual increase in vascular disarray and development of hypoxic niches. Single cell RNA sequencing (scRNA-seq) revealed that UnaG+ GBM cells were negatively enriched for neuroinflammation, Type II IFN signaling, TNFα and NF-κB signaling pathways, and IFNα and IFNγ responses. Furthermore, the gene ontology term “Negative Regulation of Immune System Process” (including genes such as Mif, Lgals3, Sdc4, Cd44, Dusp1) was predominantly upregulated in UnaG+ GBM cells. Hypoxia transcriptional program in GL261 GBM thus favors immunosuppression. To longitudinally study the immunosuppressive transcriptional programs of hypoxic GBM cells, we combined lentiviral HRE-CreERT2 with a knock-in of CAG-loxP-STOP-loxP-tdTomato (LSL-tdTom) reporter into the Rosa26 locus as a lineage tracing reporter for hypoxia-exposed GBM cells. A subclone of GL261-HRE-CreERT2; LSL-tdTom cells that displayed low baseline expression and faithful induction of tdTomato upon exposure to 1% O2 and 4-hydroxytamoxifen was identified and intracranially transplanted in mice. After a single tamoxifen injection day 21 post-transplant, mice were traced for 2 days or 7 days before analysis. At both timepoints, we observed abundant tdTom+ GBM cells spread throughout the tumor. Few tdTom+ GBM cells showed GLUT1 expression, more so in 7-day tracing than 2-day tracing. Hence, as the tumor progressed, most tdTom+ GL261 cells no longer experienced hypoxia, likely due to new angiogenesis or migration away from hypoxic areas. No tdTom+ GL261 cells were observed in parallel control experiments where tamoxifen was not injected. To investigate if hypoxia-induced immunosuppressive adaptations are preserved along tumor progression, further scRNA-seq and FACS studies will be performed to characterize the transcriptional profile of tdTom+ GBM cells. To test if hypoxic exposure is a critical conditioning event that promotes an immunosuppressive environment in GBM, we plan to genetically eliminate hypoxic GBM cells by inducing expression of Diptheria Toxin and evaluate effect on functional state of immune cells. Citation Format: Anirudh Sattiraju, Sangjo Kang, Hongyan Zou, Roland Friedel. Tumor hypoxia conditions glioblastoma cells for immunosuppression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1260.

The Trousers Fracture Test for Viscoelastic Elastomers

Abstract Shrimali and Lopez-Pamies (2023, “The ‘Pure-Shear’ Fracture Test for Viscoelastic Elastomers and Its Revelation on Griffth Fracture,” Extreme Mech. Lett., 58, p. 101944) have recently shown that the Griffith criticality condition that governs crack growth in viscoelastic elastomers can be reduced to a fundamental form that involves exclusively the intrinsic fracture energy Gc of the elastomer, and, in so doing, they have brought resolution to the complete description of the historically elusive notion of critical tearing energy Tc. The purpose of this article—which can be viewed as the third installment of a series—is to make use of this fundamental form to explain one of the most popular fracture tests for probing the growth of cracks in viscoelastic elastomers, the trousers test.

Prospective criticism: on private and public things

ABSTRACT Waxing about the futures of criticism endogenously clangs hollow in the midst of exogenous omnicrisis. Instead of worrying about the next big thing in literary studies, this essay suggests, we should be asking if there are particular tools that criticism offers that may allow us to transform the conditions of criticism’s demise. This essay offers two possible answers: ‘objectivity’, defined as a form of thought and knowledge particular to both criticism and literature, and ‘composition’, the critical understanding of the constructive function of ideas and forms. This essay deploys these concepts in an argument for the need of critics to recognise and put to work their common work and visions together with other publics whose survival is at stake in the destruction of the university, the enclosure of reason and reflection, and the rendering disposable of ever wider swathes of humanity. The question of how we make critical arguments emerges here as a crucial aspect of the ways in which critics and their ideas are able to engage with the foundational questions of their work precisely by aligning it with wider publics and thus by probing their function as a public good and for public interest.

The “pure-shear” fracture test for viscoelastic elastomers and its revelation on Griffith fracture

Strikingly, “pure-shear” fracture tests have repeatedly shown that fracture nucleation in (common hydrocarbon and other types of) viscoelastic elastomers occurs at a critical stretch that is independent of the stretch rate at which the test is carried out. In this Letter, we demonstrate that this remarkable – yet overlooked – experimental finding implies that the Griffith criticality condition that governs nucleation of fracture from large pre-existing cracks in viscoelastic elastomers can be written in fact as an expression not in terms of an elusive loading-history-dependent critical tearing energy Tc, as ordinarily done, but as one exclusively in terms of the intrinsic fracture energy Gc of the elastomer.

The Vertical Middepth Ocean Density Profile: An Interplay between Southern Ocean Dynamics and Interior Vertical Diffusivity

Abstract The middepth ocean temperature profile was found by Munk in 1966 to agree with an exponential profile and shown to be consistent with a vertical advective–diffusive balance. However, tracer release experiments show that vertical diffusivity in the middepth ocean is an order of magnitude too small to explain the observed 1-km exponential scale. Alternative mechanisms suggested that nearly all middepth water upwells adiabatically in the Southern Ocean (SO). In this picture, SO eddies and wind set SO isopycnal slopes and therefore determine a nonvanishing middepth interior stratification even in the adiabatic limit. The effect of SO eddies on SO isopycnal slopes can be understood via either a marginal criticality condition or a near-vanishing SO residual deep overturning condition in the adiabatic limit. We examine the interplay between SO dynamics and interior mixing in setting the exponential profiles of σ2 and ∂zσ2. We use eddy-permitting numerical simulations, in which we artificially change the diapycnal mixing only away from the SO. We find that SO isopycnal slopes change in response to changes in the interior diapycnal mixing even when the wind forcing is constant, consistent with previous studies (that did not address these near-exponential profiles). However, in the limit of small interior mixing, the interior ∂zσ2 profile is not exponential, suggesting that SO processes alone, in an adiabatic limit, do not lead to the observed near-exponential structures of such profiles. The results suggest that while SO wind and eddies contribute to the nonvanishing middepth interior stratification, the exponential shape of the ∂zσ2 profiles must also involve interior diapycnal mixing.

When does service employee’s accent matter? Examining the moderating effect of service type, service criticality and accent service congruence

PurposeThe purpose of this study is to further our understanding of the effects of service employees’ accents on service outcomes and to investigate the boundary conditions of service type, service criticality and accent-service congruence.Design/methodology/approachThis study reports on three scenario-based experiments with between-subject designs to assess customer reactions to service employees with nonstandard accents.FindingsThe findings revealed that the three service-related extraneous factors investigated in this study influence the direction and strength of accent’s impact. Service employees’ nonstandard accents generally negatively influence customers’ satisfaction with a service provider and purchase intentions. This effect is stronger for credence services than for experience services. While customer satisfaction with the service encounter tends to stay the same regardless of service criticality, they have less purchase intention in high service criticality situations when they deal with service employee with a nonstandard accent. Accent-service congruence enhances both satisfaction and purchase intention.Research limitations/implicationsThis study makes contributions to the accent in service interaction literature by enabling the authors to have a more complete understanding of how accent effects drive customer satisfaction and purchase intention. Future studies can take customer-related factors such as customer-service employee relationships, customers’ ethnic affiliation and ethnocentrism into consideration when examining the effects of accent in service interactions.Practical implicationsService managers need to be aware when nonstandard accents’ negative effects will elevate – credence service and service with higher criticality are better provided by service employee with a standard accent. A nonstandard accent that matches the service improves customer satisfaction and purchase intention and could be used to its advantage.Originality/valueThis study contributes to the service literature about service employees’ interaction with customers and is particularly relevant in multicultural societies with increasingly diverse workforces.

Scale and conformal invariance in higher derivative shift symmetric theories

The critical behavior of infinite families of shift symmetric interacting theories with higher derivative kinetic terms (non unitary) is considered. Single scalar theories with shift symmetry are classified according to their upper critical dimensions and studied at the leading non trivial order in perturbation theory. For two infinite families, one with quartic and one with cubic interactions, beta functions, criticality conditions and universal anomalous dimensions are computed. At the order considered, the cubic theories enjoy a one loop non renormalization of the vertex, so that the beta function depends non trivially only on the anomalous dimension. The trace of the energy momentum tensor is also investigated and it is shown that these two families of QFTs are conformally invariant at the fixed point of the RG flow.