Exact Prediction Research Articles

Seasonal differences in larval sea lamprey (Petromyzon marinus) sensitivity to the pesticide TFM

Invasive sea lamprey (Petromyzon marinus) are controlled in the Great Lakes with 4-nitro-3-(trifluoromethyl)phenol (commonly 3-trifluoromethyl-4-nitrophenol or TFM). The proper amount of TFM must be applied during treatments to effectively kill larval sea lamprey while minimizing impacts to non-target species. In this study, bioassay tests were conducted in May, July, and September in a portable test trailer at six larval sea lamprey infested rivers in Michigan to determine potential seasonal changes in sensitivity of larval sea lamprey to TFM. Larvae greater than 60 mm were collected from each stream and exposed for 12 h in TFM-treated stream water using two independent continuous-flow diluter systems. A suite of water chemistries and larval physiological parameters were collected during the tests and modeled as potential predictors of seasonal changes in the sensitivity of larval sea lamprey to TFM. The observed minimum lethal concentrations to larval sea lamprey were 0–40% lower (May), 8% lower–59% higher (July), and 49–117% higher (September) than sea lamprey control personnel treatment prediction charts. Water temperature, liver glycogen content, and time of year were strongly associated with seasonal differences in TFM sensitivity, offering sea lamprey control personnel more exact predictions to limit potential residual lamprey surviving future treatments.

Rethinking tourism: where is it going and how will it impact people and planet? A personal perspective

PurposeThis paper aims to discuss the complexity of predicting the future and of making tourism futuristic predictions. It argues that tourism cannot be separated from the world context in which it operates and explores the various impacts of tourism on people and planet both in a near and more distant future.Design/methodology/approachThis paper revises several reports and research by world economic and tourism authorities as they provide arguments for the study of trends and the complexity of the evolution of the travel and tourism industry.FindingsThere is no certain way to know what the future will bring to the tourism industry or how it will interact with society and the physical environment. What the author does know is that from the beginning of time, events do not occur in isolation and that travel and tourism will impact everything that it touches. It is impossible to make exact predictions as to what tourism will be like in the near and distant future, what the author does know is that humans will want to travel, to explore and to learn, and this desire will interact not only with tourism but with the course of history.Originality/valueBy exploring the evolution of tourism in the context of science, the author cannot even be certain where tourism will occur, if only on the same planet, within the other planets of the solar system or in the vastness of space. Science fiction predicts the latter, but only the unfolding of history will teach of the accuracy of future predictions and how the future of travel will be.

Performance prediction of the centrifugal air compressor for fuel cells considering degradation characteristics based on the hierarchical evolutionary model

The performance of the centrifugal air compressor is closely related to the efficiency and energy density of the fuel cell system, and extensive work has been done in this study to achieve precise performance predictions of the compressor considering degradation characteristics. The durability test of the compressor was carried out for 5000 h, and degradation characteristics with break-in time were discussed in detail. On this basis, the hierarchical evolutionary model was constructed to perform the performance predictions based on the Light Gradient Boosting Machine (LightGBM) and Multi-objective Whale Optimization Algorithm (MOWOA). The results demonstrate that the outlet pressure of the compressor decreases by 9.4 kPa and the power consumption increases by 0.71 kW at rated volume flow rate after the break-in of 5000 h, with attenuation ratios of 4.7 % and 12.2 %, respectively. The final non-dominated solution sets have excellent uniformity, diversity and convergence, proving that the developed MOWOA has high adaptability and strong optimization capability. The hierarchical evolutionary model achieves exact predictions for the outlet temperature, flow rate and pressure of the compressor, with absolute relative errors (AREs) below 1 % in outlet temperature and pressure while 5 % in outlet flow rate. All of these provide an efficient multi-objective optimization algorithm and an auto-calibrated hierarchical evolutionary model for further performance optimization of the air compressor.

Analysis and prediction of evaporation rates using random forest models: a case study of Almaty city

In this study, Climate Research Unit (CRU) data crossing from 1901 to 2022 was utilized to analyze and predict evaporation rates in Almaty, Kazakhstan. The data was prepared utilizing Python, leveraging the Random Forest (RF) machine learning Techniques for its capability to handle complex and non-linear information. The essential measurements for assessing the model's execution were Cruel Supreme Blunder (MAE), Root Cruel Square Mistake (RMSE), and Cruel Squared Mistake (MSE). The Random Forest model was utilized in prediction and data examination. This model is recognized by its capacity to process non-linear and complex data by making a set of choice trees and joining their comes about to get exact and steady predictions. The model is based on arbitrary testing with bootstrap testing, which makes a difference decrease change and increment the precision of the demonstrate.

Critical Minerals in the Context of Canada: Concepts, Challenges and Contradictions

Increased use of renewable energy, coupled with electrification of the economy, is considered important in efforts to limit future climate change. This energy transition is predicted to increase demands for some commodities, many of which are now labelled as critical mineral. The quest for such commodities is now a persistent theme for the resource industry and emerging government policies. This review for non-specialists explains several key concepts but also explores some challenges and apparent contradictions in the context of Canada. Canada now has a list of 31 critical minerals, but this includes some major commodities for which domestic production is significant and supply risk is low. The differences between our list and those of other jurisdictions reflect our more specific definitions. Most other commodities on Canada’s list are also identified by other countries and some are specifically linked to the energy transition. These include cobalt, lithium, manganese, nickel, graphite and vanadium (used in electric vehicle batteries and static energy storage), rare earth elements (REE; used for magnets in EV motors and wind turbines) and some rarer elements (e.g. germanium, gallium, indium and tellurium) used in photovoltaic (solar) energy systems. Some of these are potential primary products (e.g. lithium, graphite and REE) but many others (e.g. cobalt, platinum group elements and the photovoltaic elements) are byproducts from the production of major commodities, notably nickel, copper and zinc. The REE represent coproducts that are closely associated in nature and very hard to separate from each other; they are produced as a group. There are some specific challenges in exploring for and developing critical mineral resources. The end-use technology driving demand evolves on a timescale of years, but mineral exploration and development now typically take multiple decades. Material substitutions and unpredictable developments in technology complicate the exact prediction of future demands. The forecasts of overall relative demand growth are impressive, but for some key commodities global production will remain small in absolute terms, which may limit the potential for new discoveries. Simple measures of grade and tonnage are not always guarantees of viability, because deposits of some commodities (e.g. the REE) are mineralogically complex. Byproduct commodities cannot be produced in isolation, and many of these are only extracted in smelting and refining. Domestic production of these commodities is effectively lost if concentrates are exported for processing. The emissions and environmental impacts associated with production of critical mineral resources will also become important if such activity is to be linked to wider climate goals. This may present challenges in northern Canada, where renewable or low-carbon energy options are limited. Most draft Land Use Plans in the north presently emphasize large-scale land conservation, which could limit future exploration access before resource potential is fully assessed. Given the strong divisions of opinion about resource development, especially in the north, controversy and polarized debate will not easily be avoided. There are no simple answers to challenges that are political or jurisdictional rather than technical, but there is definitely a need for more public geoscientific information. This will help to identify areas of greatest potential, evaluate known deposits and contribute to future sustainable development. For many of the commodities on our critical mineral resources list, data for Canada remains incomplete, especially in more remote regions that are generally considered to have the highest potential.

Soft computing approach on estimating the lateral confinement coefficient of CFRP veiled circular columns

For any structures, Reinforced concrete (RC) columns are the utmost carping members. RC columns jacketed with CFRP membrane enhance resistance, deformation aptitude, and the auspicious confining impacts. Sundry scrutiny has been implemented about circular columns wrapping or veiled with CFRP layers. This scrutiny will manifest about Genetic Programming (GP) and Artificial Neural Networks (ANN) as soft computing models, mobbing exploited to ascertain the lateral confinement coefficient (Ks) valuate of RC circular columns jacketed with CFRP membrane. For the purpose to ascertain Ks value, manipulate the corresponding ambit such as the diameter of the column (b), column length (L), thickness layer of CFRP (tw), the elastic modulus of CFRP membrane (Ef) along with the compressive strength of unconfined concrete (fcc). Total collection of 142 datasets for the RC circular column was employed to decipher the prediction of Ks. Various statistical metrics (R2 = 0.933; RMSE = 0.054; NMBE = -0.001) compared with the ANN model and exhibits the superiority of GP in predicting the Ks. Taylor diagram and Rank analysis were also carried out in this article for justification. The lucidity of the GP machine enlightenment model overtures a greater sensible and exact prediction of Ks value for RC circular columns in accordance with versatile loading conditions.

Accuracy of Personalized Computed Tomographic 3D Templating for Acetabular Cup Placement in Revision Arthroplasty.

Background: Revision hip arthroplasty presents a surgical challenge, necessitating meticulous preoperative planning to avert complications like periprosthetic fractures and aseptic loosening. Historically, assessment of the accuracy of three-dimensional (3D) versus two-dimensional (2D) templating has focused exclusively on primary hip arthroplasty. Materials and Methods: In this retrospective study, we examined the accuracy of 3D templating for acetabular revision cups in 30 patients who underwent revision hip arthroplasty. Utilizing computed tomography scans of the patients' pelvis and 3D templates of the implants (Aesculap Plasmafit, B. Braun; Aesculap Plasmafit Revision, B. Braun; Avantage Acetabular System, Zimmerbiomet, EcoFit 2M, Implantcast; Tritanium Revision, Stryker), we performed 3D templating and positioned the acetabular cup implants accordingly. To evaluate accuracy, we compared the planned sizes of the acetabular cups in 2D and 3D with the sizes implanted during surgery. Results: An analysis was performed to examine potential influences on templating accuracy, specifically considering factors such as gender and body mass index (BMI). Significant statistical differences (p < 0.001) in the accuracy of size prediction were observed between 3D and 2D templating. Personalized 3D templating exhibited an accuracy rate of 66.7% for the correct prediction of the size of the acetabular cup, while 2D templating achieved an exact size prediction in only 26.7% of cases. There were no statistically significant differences between the 2D and 3D templating methods regarding gender or BMI. Conclusion: This study demonstrates that 3D templating improves the accuracy of predicting acetabular cup sizes in revision arthroplasty when compared to 2D templating. However, it should be noted that the predicted implant size generated through 3D templating tended to overestimate the implanted implant size by an average of 1.3 sizes.

A Modified Stochastic Model for Rainfall Prediction Using Fuzzy Aquila Optimization

In recent years, rainfall prediction has received major attention in research areas because of its demanding applications in pollution control management and flood control management. Despite having numerous learning-based approaches to calculate future rainfall trends, it remains inefficient to predict rainfall occurrences by learning linear and nonlinear data patterns of historical weather information (i.e., exact prediction value is complicated to be predicted). These complications are addressed with the evolution of stochastic models which have a greater ability to minimize prediction bias and represent long-term weather variability. Therefore, this paper proposes a novel modified stochastic fuzzy Aquila (MSFA) algorithm to make precise predictions regarding future trends by evaluating rainfall time series data. The proposed MSFA algorithm is applied in rainfall prediction applications in evaluating the effectiveness of the proposed stochastic model. Here, 10 features of the open weather dataset collected from Tamil Nadu are provided as input for the proposed rainfall prediction design. The data inconsistencies such as undesirable format and missing values are structured using preprocessing procedures, namely data arrangement, null value removal, and data partitioning. The preprocessed data are fed into the proposed MSFA algorithm which learns the data features more precisely and predicts the probable occurrence of rainfall. To evaluate the performances of the proposed MSFA algorithm, the metrics such as mean absolute error (MAE), coefficient of determination, root mean squared logarithmic error (RMSLE), and root mean square error (RMSE) are analyzed. The experimental results illustrate that the proposed MSFA algorithm achieves superior performance in terms of all metrics.

A Multi-Field Coupled PEMFC Model with Force-Temperature-Humidity and Experimental Validation for High Electrochemical Performance Design

PEMFCs (Proton Exchange Membrane Fuel Cells) are commonly used in fuel cell vehicles, which facilitates energy conversation and environmental protection. The fuel cell electrochemical performance is significantly affected by the contact resistance and the GDL (Gas Diffusion Layer) porosity due to ohmic and concentration losses. However, it is difficult to obtain the exact performance prediction of the electrochemical reaction for a fuel cell design, resulting from the complex operating conditions of fuel cells coupled with the assembly force, operating temperature, relative humidity, etc. Considering the compression behavior of porosity and the contact pressure in GDLs, a force-temperature-humidity multi-field coupled model is established based on FEA (Finite Element Analysis) and CFD (Computational Fluid Dynamics) for the fuel cell electrochemical performance. Aside from that, the characteristics between the contact resistance and the contact pressure are measured and fitted through the experiments in this study. Finally, the numerical model is validated by the experiment of the fuel cell stack, and the error rate between the presented model and the experimentation of the full-dimensional stack being a maximum of 3.37%. This work provides important insight into the force-temperature-humidity coupled action as less empirical testing is required to identify the high fuel cell performance and optimize the fuel cell parameters in a full-dimensional fuel cell stack.

Critical ignition conditions for propane/air premixtures impinging on electric heated surfaces: An experimental study

To contribute to exact predictions of the ignition of flammable mixture by a hot surface and ensure safety against fire, this study aims to clarify the critical ignition condition of a propane/air premixture impinging on a hot surface with external heating. The ignition threshold of heat flux depends on the flow velocity, but that of temperature hardly depends on flow velocity and equivalence ratio. The ignition occurred at the position where the residence time of species was the longest. It corresponds to the position of the lowest heat transfer coefficient. The temperature boundary layer thickness played a significant role in the ignition process as only the heat flux from the top, bottom, and back surfaces of the heater contributed to ignition in the presence of forced convection. The prediction model of the ignition threshold of heat flux and temperature was derived through dimensional analysis. This model effectively explains the ignition characteristics, regardless of the differences in thermal convection mode, dimensions, and geometric arrangements of a heater, as confirmed by comparison with reference data. Additionally, the ignition threshold of temperature can be predicted if the external heating power is known, and it is useful to assess the fire risk.

Li-Ion Battery State of Charge Prediction for Electric Vehicles Based on Improved Regularized Extreme Learning Machine

Battery state of charge prediction is one of the most essential state quantities of a battery management system. It is a prerequisite for the operation of a battery management system, but it becomes difficult to make an exact prediction of its state due to its characteristics, which cannot be measured directly. For the exact assessment of the Li-ion battery state of charge, the research proposes an extreme learning machine algorithm based on the alternating factor multiplier method with improved regularization. This method constructs a suitable online Li-ion battery state of charge prediction model using the alternating factor multiplier method in gradient form. The experiment demonstrates that the algorithm in the study has a reduction in the number of nodes in the implicit layer relative to the traditional extreme learning machine algorithm. The error fluctuations of the algorithm under two different excitation functions range from [−0.005, 0.005] and [0.082, 0.265]; The root mean square error of the data set in which the algorithm performs well is 1.9516 and 0.6157, respectively. The real simulation scenario created the predicted values of the state of charge in the realistic simulation scenario that fit the real value curve by 99.99%. The average and maximum errors of the proposed state of charge prediction model are the smallest compared to the long and short-term memory networks and gated cyclic units, 0.58% and 2.97%, respectively. The experiment demonstrates that the presented algorithm can reduce the computational burden while guaranteeing the state of charge model prediction.

Emergence of Hydrodynamic Spatial Long-Range Correlations in Nonequilibrium Many-Body Systems.

At large scales of space and time, the nonequilibrium dynamics of local observables in extensive many-body systems is well described by hydrodynamics. At the Euler scale, one assumes that each mesoscopic region independently reaches a state of maximal entropy under the constraints given by the available conservation laws. Away from phase transitions, maximal entropy states show exponential correlation decay, and independence of fluid cells might be assumed to subsist during the course of time evolution. We show that this picture is incorrect: under ballistic scaling, regions separated by macroscopic distances "develop long-range correlations as time passes." These correlations take a universal form that only depends on the Euler hydrodynamics of the model. They are rooted in the large-scale motion of interacting fluid modes and are the dominant long-range correlations developing in time from long-wavelength, entropy-maximized states. They require "the presence of interaction" and "at least two different fluid modes" and are of a fundamentally different nature from well-known long-range correlations occurring from diffusive spreading or from quasiparticle excitations produced in far-from-equilibrium quenches. We provide a universal theoretical framework to exactly evaluate them, an adaptation of the macroscopic fluctuation theory to the Euler scale. We verify our exact predictions in the hard-rod gas, by comparing with numerical simulations and finding excellent agreement.

Noise prediction from the measured surface-vibration of a casing structure by using compressed sensing and finite element method

In a complex structural system, such as a gearbox or a motor, with partially or completely unknown set of excitation forces, the exact prediction of the vibrations of a casing encapsulating the complex system, is not possible, especially at high frequencies. For the spatial recording of the complete vibration field on the casing, it would be necessary to scan the casing surface using several vibration sensors, especially at higher frequencies. Such a high-resolution rate of sensors or recording points on the casing surface is rarely achievable in industrial tests. Moreover, usage of several optical scanning devices to scan the vibration field from every possible direction is very expensive, cumbersome and mostly ineffective. Therefore, a novel technique is developed here, which enables the calculation of the participation factors of the casing eigenmodes using a reduced number of vibration sensors on the casing, with the aim to estimate the radiated noise levels generated by the vibrating casing. This technique is based on a Compressed Sensing approach which solves an under-determined system of equations. The calculated participation factors of the eigenmodes are then used as inputs into a Finite Element Method (FEM) to calculate the radiated noise levels. The noise predictions are then compared to the measurements to achieve enough confidence level in this method for its industrial usage.

Scaled fatigue cracks under service loads

Scaled experimentation is a powerful engineering tool yet its application to fatigue is limited by significant changes in behaviour with scale. The recent discovery of new similitude rules however opens new possibilities that are explored in this work through numerical experimentation for industrially representative fatigue-crack growth scenarios. It is shown for the first time, how the geometric size effects present in mixed mode fatigue tests are eliminated by performing two appropriately designed scaled experiments. The first order finite similitude theory is applied to mixed mode (modes I and II) fatigue crack growth in three case studies, viz., compact tension shear (CTS) specimen, wing fuselage attachment lug, and a T-joint with an inclined semi elliptical crack. Both planar and non-planar crack growth feature, along with surface and through thickness cracks. Low and high cycle fatigue (up to circa 60,000 cycles) is examined for three different materials (Al-6061 T6 alloy, structural steel, and AISI 316 stainless steel) under a variety of load ratios. The new rules return near exact crack-path predictions whereas lifecycle predictions have errors ranging between 1 % and 9 %. Paris law constants C and m are predicted with up to 99.9 % accuracy supporting the theory that Paris law is a first order similitude rule. Geometric size effects afflicting industrial type scaled-fatigue studies employing a damage tolerant design approach are confirmed to be eliminated on combination of information from two appropriately designed scaled models.

Overall Accuracy of Radiological Digital Planning for Total Hip Arthroplasty in a Specialized Orthopaedics Hospital

Preoperative radiological planning is a key factor in the prediction of implant size and positioning that influences surgical time, the risk of complications, and functional outcomes. We have tested the accuracy of the digital templating performed in our hospital for a sample of 215 patients that underwent total hip arthroplasty. We assessed the accuracy of correctly predicting implant size for the femoral and acetabular components, as well as the stem neck length. We found that our method of templating proved accurate (within one size) in 95.8% of cases for the stem and 94.9% for the cup when using the anteroposterior view only, while the lateral view was accurate in 95.8% of cases for the stem and 97.2% for the cup. Exact prediction of the stem size was obtained in 77.7% of cases using the anteroposterior view and 67.0% of cases on the lateral view, and 73.0% and 74.4% of cases for the cup on the AP and LL views, respectively. Stem neck size was predicted exactly in 75.35% of cases and within one size in 93.49% of cases. We concluded that our method of digital templating using dedicated software is highly effective in accurately predicting implant size.

Planned Trajectory Classification for Wheeled Mobile Robots to Prevent Rollover and Slip

In this paper, a novel planned trajectory classification method (PTCM) is proposed to evaluate the safety of the car- like four-wheeled mobile robots (4-WMRs) with Ackermann steering and high-stiffness suspensions. To classify a planned trajectory to be safe or unsafe before the 4-WMR actually follows it, the conditions of the wheel forces (WFs: longitudinal, lateral, and normal forces for each wheel) necessary to execute the planned trajectory without rollover and slip are calculated using the passive decomposition of the WMR dynamics with the Pfaffian constraints of the no-rollover and no-slip conditions. Similar to the case of Navier's table problem, only nine-dimensional WFs projected onto the constrained space are identifiable among the twelve-dimensional WFs. This indeterminacy turns out not to affect the rollover prediction, yet does so for the slip prediction. For this, we propose novel optimistic and pessimistic methods, together upper and lower bounding the exact slip prediction. The proposed PTCM classifies the planned trajectory as safe if rollover and slip are not predicted and unsafe otherwise. The proposed PTCM is demonstrated and validated by simulations and outdoor experiments.

Combining Slow and Fast: Complementary Filtering for Dynamics Learning

Modeling an unknown dynamical system is crucial in order to predict the future behavior of the system. A standard approach is training recurrent models on measurement data. While these models typically provide exact short-term predictions, accumulating errors yield deteriorated long-term behavior. In contrast, models with reliable long-term predictions can often be obtained, either by training a robust but less detailed model, or by leveraging physics-based simulations. In both cases, inaccuracies in the models yield a lack of short-time details. Thus, different models with contrastive properties on different time horizons are available. This observation immediately raises the question: Can we obtain predictions that combine the best of both worlds? Inspired by sensor fusion tasks, we interpret the problem in the frequency domain and leverage classical methods from signal processing, in particular complementary filters. This filtering technique combines two signals by applying a high-pass filter to one signal, and low-pass filtering the other. Essentially, the high-pass filter extracts high-frequencies, whereas the low-pass filter extracts low frequencies. Applying this concept to dynamics model learning enables the construction of models that yield accurate long- and short-term predictions. Here, we propose two methods, one being purely learning-based and the other one being a hybrid model that requires an additional physics-based simulator.

Byzantine attack detection using optimized dual‐channel capsule generative adversarial network for securing cognitive radio network

AbstractCognitive Radio Networks (CRN) is emerged as a solution for the problem of spectrum scarcity as it achieves transmission opportunities in under‐utilized spectrum bands for primary users (PUs). Therefore, in this manuscript, an efficient Byzantine attack detection using optimized Dual‐Channel Capsule Generative Adversarial Network and Auto‐Metric Graph Neural Network (AMGNN) to secure Cognitive Radio Network. Here, two types of layers are utilized that is processing layer and decision layer. In processing layer, the dataset generation based on emission matrix from Hidden Markov Model (HMM) contains labeled data for the legitimate secondary users including malicious Byzantine attackers. In decision layer, the generated dataset is given to Dual‐Channel Capsule Generative Adversarial Network (DCCGAN) to classify secondary users (SUs) as Normal or Attacker. Normally, DCCGAN not exposes any optimization techniques adoption to compute optimum parameters for assuring exact prediction. Thus, capuchin search algorithm (CSA) is employed for optimizing DCCGAN weight parameters. Once the attack is detected, then alert information is fed to AMGNN opportunistic spectrum access. Proposed model executed at MATLAB and performance analyzed with performance metrics like system utility rate Vs available spectrum range, Attack detection rate, Accuracy, Packet Delivery Ratio (PDR), and throughput. The proposed method attains higher accuracy of 96.31%, 97.06%, 93.52%, higher system utility rate of 97.22%, 91.39%, 96.41%, and higher attack detection rate of 96.25%, 94.12%, 97.15% analyzed to the existing models, such as BAD‐AESA‐SCRN, BAD‐BMHHO‐ENN‐SCRN, and BAD‐HCM‐EELM‐SCRN respectively.

A novel machine learning-based model predictive control framework for improving the energy efficiency of air-conditioning systems

The dynamic optimization of key setpoints (e.g., supply water temperatures of chillers and cooling towers, or indoor temperature and humidity) can track the efficient performance point of the air-conditioning system (ACS), thus obtaining a great energy-saving effect. The model predictive control is an effective way to precisely adjust these setpoints. However, most of the existing studies focused on simple linear strategies, e.g., experience-based, rule-based, or component-based, which results in unsatisfactory energy efficiency. Aiming at this, the paper proposed a novel machine learning-based model predictive control (MLB-MPC) framework with predictive inputs of disturbances. The framework first employed the grid search and cross-validation to optimize the total energy consumption prediction models (TECPMs), which were built by data-driven models (e.g., multiple linear regression, artificial neural network, support vector regression (SVR), and random forest). And then the optimization performances of each TECPM to the controlled variables were obtained by using different optimization algorithms (e.g., genetic algorithm, particle swarm optimization (PSO), and simulated annealing). Finally, the optimal match of TECPMs and optimization algorithms was achieved by trade-off among prediction accuracies, optimization accuracies, and optimization time. The case studies demonstrated that the optimal match for the MLB-MPC is SVR and PSO as they had the highest prediction accuracy (mean absolute percentage error of 2.5%) and shortest optimization time (41 ms/per) and optimization accuracies with little difference. After adopting the optimal match, the MLB-MPC with predictive disturbing inputs achieved a great energy-saving ratio of 7.1% for the ACS, which was only less than the MLB-MPC with ideal disturbing inputs by 0.4%. This indicated that exact predictions of disturbances (e.g., cooling load and outdoor wet-bulb temperature) are important for the MLB-MPC. The proposed MLB-MPC framework would provide a method for improving the energy efficiency of ACSs.

Understanding the Structure-Activity Relationship through Density Functional Theory: A Simple Method Predicts Relative Binding Free Energies of Metalloenzyme Fragment-like Inhibitors.

Despite being involved in several human diseases, metalloenzymes are targeted by a small percentage of FDA-approved drugs. Development of novel and efficient inhibitors is required, as the chemical space of metal binding groups (MBGs) is currently limited to four main classes. The use of computational chemistry methods in drug discovery has gained momentum thanks to accurate estimates of binding modes and binding free energies of ligands to receptors. However, exact predictions of binding free energies in metalloenzymes are challenging due to the occurrence of nonclassical phenomena and interactions that common force field-based methods are unable to correctly describe. In this regard, we applied density functional theory (DFT) to predict the binding free energies and to understand the structure-activity relationship of metalloenzyme fragment-like inhibitors. We tested this method on a set of small-molecule inhibitors with different electronic properties and coordinating two Mn2+ ions in the binding site of the influenza RNA polymerase PAN endonuclease. We modeled the binding site using only atoms from the first coordination shell, hence reducing the computational cost. Thanks to the explicit treatment of electrons by DFT, we highlighted the main contributions to the binding free energies and the electronic features differentiating strong and weak inhibitors, achieving good qualitative correlation with the experimentally determined affinities. By introducing automated docking, we explored alternative ways to coordinate the metal centers and we identified 70% of the highest affinity inhibitors. This methodology provides a fast and predictive tool for the identification of key features of metalloenzyme MBGs, which can be useful for the design of new and efficient drugs targeting these ubiquitous proteins.