Qualitative Rules Research Articles

Development and validation of intelligent load control for VRF air-conditioning system with deep learning based load forecasting

Variable refrigerant flow (VRF) air-conditioning systems have seen significant growth in Asia and its application is expanding globally. Despite the expanded application, most previous studies have focused on developing fault detection and diagnostics to achieve energy-efficient operations than on predicting power consumption. It's very difficult to predict the electrical consumption owing to its complex system configuration and various control strategies. A new control strategy is described for optimal adjustment of the desired target level based on time series forecasting using the optimized sequence to sequence model for VRF systems. Sequence to sequence (seq2seq) model with attention mechanism and Bayesian optimization is developed to predict accurate hourly and daily forecasts and rapid feedback control for fluctuating power consumption for VRF systems. The optimized seq2seq model is integrated into the intelligent load control (ILC). ILC can be used to manage VRF systems by dynamically prioritizing indoor units for curtailment using both quantitative inputs and qualitative rules. Overall, the results demonstrate that the deep learning based control allows coordination of the controllable loads of VRF systems in three commercial buildings. ILC with deep learning manages the power consumption within a desired target level, as well as indoor temperature reflecting the status of controlled indoor units, as objective functions of the control algorithm.

Combining Zhegalkin Polynomials and SAT Solving for Context-specific Boolean Modeling of Biological Systems.

Large amounts of knowledge regarding biological processes are readily available in the literature and aggregated in diverse databases. Boolean networks are powerful tools to render that knowledge into models that can mimic and simulate biological phenomena at multiple scales. Yet, when a model is required to understand or predict the behavior of a biological system in given conditions, existing information often does not completely match this context. Networks built from only prior knowledge can overlook mechanisms, lack specificity, and just partially recapitulate experimental observations. To address this limitation, context-specific data needs to be integrated. However, the brute-force identification of qualitative rules matching these data becomes infeasible as the number of candidates explodes for increasingly complex systems. Here, we used Zhegalkin polynomials to transform this identification into a binary value assignment for exponentially fewer variables, which we addressed with a state-of-the-art SAT solver. We evaluated our implemented method alongside two widely recognized tools, CellNetOptimizer and Caspo-ts, on both artificial toy models and large-scale models based on experimental data from the HPN-DREAM challenge. Our approach demonstrated benchmark-leading capabilities on networks of significant size and intricate complexity. It thus appears promising for the in silico modeling of ever more comprehensive biological systems.

Electron Transport through Linear-, Broken-, and Cross-Conjugated Polycyclic Compounds.

Quantum interference (QI) effects offer unique opportunities to modulate charge transport through single molecules. In recent years, several transmission selection rules have been developed to determine constructive and destructive QIs in an intuitive and simple manner, although some of these rules fail for cross-conjugated systems. In this work, we evaluate the performance of distinct transmission rules on a broad series of anthracene and fluorene derivatives with distinctive structural features including linear-, broken-, and cross-conjugation, heteroatoms, and five-membered rings as such species affords a predictive challenge for the qualitative selection rules for QI effects. The electron transport properties and local transmission plots are first evaluated by combining DFT and the nonequilibrium Green function method allowing for an equal-footing comparison of the conductance of the different polycyclic compounds. Our findings are in line with experimental observations on the influence of the type of conjugation and the connectivity to the metallic electrodes on the transport properties. Thus, cross-conjugated systems exhibit reduced conductance values as compared to the linear-conjugated ones, although the transmission is enhanced in the meta-connected junctions. Remarkably, our study reveals that aromatic cores exhibit generally larger zero-bias conductance for a given connectivity, in contrast to the negative aromaticity-conductance relationship found in literature.

Singlet Fission in Pechmann Dyes: Planar Chromophore Design and Understanding.

Singlet fission in organic chromophores holds the potential for enhancing photovoltaic efficiencies beyond the single-junction limit. The most basic requirement of a singlet fission material is that it has a large energy gap between its first singlet and triplet excited states. Identifying such compounds is not simple and has been accomplished either through computational screening or by subtle modifications of previously known fission materials. Here, we propose an approach that leverages ground and excited-state aromaticity combined with double-bond conformation to establish simple qualitative design rules for predicting fundamental optical properties without the need for computational modeling. By investigating two Pechmann dye isomers, we demonstrate that although their planarity and degree of charge transfer are similar, singlet fission is active in the isomer with a trans-conformation, while the cis-isomer exhibits greater favorability for polaronic processes, experimentally validated using ultrafast and electron spin resonance spectroscopy. Our results offer a new design perspective that provides a rational framework for tailoring optoelectronic systems to specific applications such as singlet fission or triplet-triplet annihilation.

A Theoretical Perspective on the Photochemistry of Boron-Nitrogen Lewis Adducts.

Boron-Nitrogen (B-N) Lewis adducts form a versatile family of compounds with numerous applications in functional molecules. Despite the growing interest in this family of compounds for optoelectronic applications, little is currently known about their photophysics and photochemistry. Even the electronic absorption spectrum of ammonia borane, the textbook example of a B-N Lewis adduct, is unavailable. Given the versatility of the light-induced processes exhibited by these molecules, we propose in this work a detailed theoretical study of the photochemistry and photophysics of simple B-N Lewis adducts. We used advanced techniques in computational photochemistry to identify and characterize the possible photochemical pathways followed by ammonia borane and extended this knowledge to the substituted B-N Lewis adducts pyridine-borane and pyridine-boric acid. The photochemistry observed for this series of molecules allows us to extract qualitative rules to rationalize the light-induced behavior of more complex B-N-containing molecules.

Offset backing path planning of tractor-semitrailer vehicles based on qualitative rules and geometric methods considering uncertainties

The backward behavior of tractor-semitrailer vehicles is particularly challenging compared to passenger vehicles, causing many difficulties not only for drivers but also for autonomous driving. Offset backing, which is one of the most important backward scenarios for articulated vehicles, can be applied to various path planning in a confined space, such as parking, and can be used for Advanced Driver Assistance Systems (ADAS) and autonomous driving. First of all, by analyzing the slow movement of a tractor-semitrailer vehicle in a confined space, standard unit motions and compound motions for general posture adjustment were defined. By approximating the offset backing driving paths of expert tractor-semitrailer drivers with a geometric method, qualitative path planning was completed by combining compound and unit motions. In addition, complementary motions were added to respond to errors that occur due to the simplicity of the qualitative path planning based on the geometric method. The usefulness of the developed offset backing path planning for tractor-semitrailer vehicles was demonstrated through repeated experimental tests with a model autonomous vehicle.

Harmonizing pesticides environmental quality standards: A fate-pathway perspective

Regulatory agencies worldwide set pesticide environmental quality standards, which are proposed independently in each dependent environmental media rather than across the complete fate route. Thus, lacking the fate-pathway perspective in defining pesticide environmental quality standards might cause undesirable pesticide residue from the upper compartment (e.g., soil) to the lower compartment (e.g., water). This study aimed to harmonize the self-consistency of pesticide environmental quality standards across environmental media via the fate-pathway analysis. The introduced qualitative and quantitative rules defined environmental quality standards of pesticides in six major environmental scenarios in the soil and water system based on related regulatory objectives. Fate factors simulated via USEtox were used to create a preliminary quantitative link between theoretical maximum legal masses of pesticides across environmental compartments. Using chlorpyrifos and 2,4-D as examples, their standard values were comparatively assessed in selected environmental media in China and the United States. According to the investigative findings, missing the respective environmental quality standards of pesticides in the agricultural soil could significantly influence the implementation of those in freshwater. Taking a fate-pathway perspective, the self-consistency test highlighted that defining pesticide environmental quality standards for freshwater was the most challenging task, as the freshwater compartment typically comprises multiple lower environmental compartments with diverse regulatory objectives. Overall, this theoretical study has the potential to illuminate the harmonization of pesticide environmental quality standards throughout the entire environmental fate pathway, ultimately leading to improved regulatory efficiency and communication. Future research should focus on risk-based model implementation, regulatory response evaluation, and legal limit interpretation to better integrate environmental pesticide management under a variety of regulatory goals.

Understanding of sailing rule based on COLREGs: Comparison of navigator survey and automated collision-avoidance algorithm

The Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) comprises rules to prevent collisions at sea and is based on the qualitative rules and ordinary practice of seamen. However, because the criteria of the sailing rule interpretation are different, the problem of misunderstanding occurs owing to the difference in the interpretation between navigators and collision-avoidance algorithm designers. Therefore, this study aims to clarify the collision situations by understanding in the sailing rule interpretation of COLREGs. To identify the understanding of collision situations, we surveyed the navigators’ understanding of collision situations and reviewed the collision-avoidance algorithms used in recent studies. The survey results were analyzed based on head-on and crossing (HC) and crossing and overtaking (OC) situations. Results showed that navigators were unsure about whether the HC situation sailing rule should be applied to incoming vessels from the 008° and whether the overtaking or crossing situation should be applied to coming vessels at 160°. In OC situations, as the angle increased from 130°, it became difficult for navigators to interpret the navigation rules. A notable disparity emerged between the navigators’ understanding and the automated collision-avoidance algorithms. Moreover, it was confirmed that practical navigators exhibited divergent interpretations of these regulations. This study contributes to provide an understanding of COLREGs sailing rules based on the understanding of navigators and researchers.

Two generalizations of the semi-graphoid rule of probabilistic independence and more

Probabilistic independence is a key concept in probability theory and statistics. For probabilistic independence a set of well known qualitative rules exists, the so-called semi-graphoid rules, which can be summarized into a single semi-graphoid rule. This rule system was conjectured to be complete, it is however incomplete and an additional five rules were formulated. The generalization of one of those rules subsequently showed that no finite rule system exists and in recent work even all five additional rules were (further) generalized. In this paper, two new generalized rules are stated, both involving n, n≥1 variable sets Ci. These rules generalize the semi-graphoid rule for n is odd and generalize one of the additional rules for n is even. Furthermore two new rules of probabilistic independence are given. The paper thereby contributes to the insights into the structural properties of probabilistic independence and provides an enhanced description of probabilistic independence by means of rules.

Accelerating materials-space exploration for thermal insulators by mapping materials properties via artificial intelligence

Reliable artificial-intelligence models have the potential to accelerate the discovery of materials with optimal properties for various applications, including superconductivity, catalysis, and thermoelectricity. Advancements in this field are often hindered by the scarcity and quality of available data and the significant effort required to acquire new data. For such applications, reliable surrogate models that help guide materials space exploration using easily accessible materials properties are urgently needed. Here, we present a general, data-driven framework that provides quantitative predictions as well as qualitative rules for steering data creation for all datasets via a combination of symbolic regression and sensitivity analysis. We demonstrate the power of the framework by generating an accurate analytic model for the lattice thermal conductivity using only 75 experimentally measured values. By extracting the most influential material properties from this model, we are then able to hierarchically screen 732 materials and find 80 ultra-insulating materials.

Towards an objective theory of subjective liking: A first step in understanding the sense of beauty.

The study of the electroencephalogram signals recorded from subjects during an experience is a way to understand the brain processes that underlie their physical and emotional involvement. Such signals have the form of time series, and their analysis could benefit from applying techniques that are specific to this kind of data. Neuroaesthetics, as defined by Zeki in 1999, is the scientific approach to the study of aesthetic perceptions of art, music, or any other experience that can give rise to aesthetic judgments, such as liking or disliking a painting. Starting from a proprietary dataset of 248 trials from 16 subjects exposed to art paintings, using a real ecological context, this paper analyses the application of a novel symbolic machine learning technique, specifically designed to extract information from unstructured data and to express it in form of logical rules. Our purpose is to extract qualitative and quantitative logical rules, to relate the voltage at specific frequencies and in specific electrodes, and that, within the limits of the experiment, may help to understand the brain process that drives liking or disliking experiences in human subjects.

CSM-Toxin: A Web-Server for Predicting Protein Toxicity

Biologics are one of the most rapidly expanding classes of therapeutics, but can be associated with a range of toxic properties. In small-molecule drug development, early identification of potential toxicity led to a significant reduction in clinical trial failures, however we currently lack robust qualitative rules or predictive tools for peptide- and protein-based biologics. To address this, we have manually curated the largest set of high-quality experimental data on peptide and protein toxicities, and developed CSM-Toxin, a novel in-silico protein toxicity classifier, which relies solely on the protein primary sequence. Our approach encodes the protein sequence information using a deep learning natural languages model to understand "biological" language, where residues are treated as words and protein sequences as sentences. The CSM-Toxin was able to accurately identify peptides and proteins with potential toxicity, achieving an MCC of up to 0.66 across both cross-validation and multiple non-redundant blind tests, outperforming other methods and highlighting the robust and generalisable performance of our model. We strongly believe the CSM-Toxin will serve as a valuable platform to minimise potential toxicity in the biologic development pipeline. Our method is freely available as an easy-to-use webserver.

Digital Rock Mechanical Properties by Simulation of True Triaxial Test: Impact of Microscale Factors

Complex fractures and pore structures in the rock strongly influence the mechanical properties, and the process from compression to failure is complicated. Under the action of rock stress, pore structure deformation and fractures close or propagate, easily leading to deterioration in the rock mechanical properties until rock failure. Thus, the effects of microscale factors are critical in mechanical properties such as rock strength, elastic modulus, and stress–strain state under the triaxial stress state. It is difficult for physical and mechanical experiments to obtain the qualitative rules of regular structures, but numerical simulation can make up for this defect. In this work, the accuracy of the model was proven through a comparison with previous experimental results. The true triaxial numerical simulation experiments were conducted on representative rocks and natural pore structures. These simulated results revealed that the pore and throat parameters will change abruptly when the particle model volumetric strain is between 0.0108 and 0.0157. When the fracture angle is between 45° and 75°, the fracture has a great influence on the peak stress. The angle between the natural fracture and the fracturing direction should be less than 45° as much as possible. Clay affects the rock strength by influencing the force chains formed by the rock skeleton. Fracturing is easier when the structural clay content is higher than 25%. It is easier to fracture in a direction parallel to the laminated clay when the clay content is below 27%. This work indicates the effects of rock particles, fractures, and clay on the mechanical parameters, providing key fundamental data for further quantifying the fracturing patterns.

Mechanism to model: a physical organic chemistry approach to reaction prediction.

The application of mechanistic generalizations is at the core of chemical reaction development and application. These strategies are rooted in physical organic chemistry where mechanistic understandings can be derived from one reaction and applied to explain another. Over time these techniques have evolved from rationalizing observed outcomes to leading experimental design through reaction prediction. In parallel, significant progression in asymmetric organocatalysis has expanded the reach of chiral transfer to new reactions with increased efficiency. However, the complex and diverse catalyst structures applied in this arena have rendered the generalization of asymmetric catalytic processes to be exceptionally challenging. Recognizing this, a portion of our research has been focused on understanding the transferability of chemical observations between similar reactions and exploiting this phenomenon as a platform for prediction. Through these experiences, we have relied on a working knowledge of reaction mechanism to guide the development and application of our models which have been advanced from simple qualitative rules to large statistical models for quantitative predictions. In this feature article, we describe the models acquired to generalize organocatalytic reaction mechanisms and demonstrate their use as a powerful approach for accelerating enantioselective synthesis.

Frontal Vehicular Crash Energy Management Using Analytical Model in Multiple Conditions

When it comes to frontal vehicular crash development, matching the stiffness of the front-end structures reasonably, i.e., impact energy management, can effectively improve the safety of the vehicle. A multi-condition analytical model for a frontal vehicular crash is constructed by a three-dimensional decomposition theory. In the analytical model, the spring is used to express the equivalent stiffness of the local energy absorption space at the front-end structure. Then based on the analytical model, the dynamic responses and evaluation indexes of the vehicle in MPDB and SOB conditions are derived with the input of the crash pulse decomposition scheme. Comparing the actual vehicle crash data and the calculation results of the proposed solution method, the error is less than 15%, which verifies validity of the modeling and the accuracy of the solution. Finally, based on the solution method in the MPDB and the SOB conditions, the sensitivities of the crash pulse decomposition scheme to evaluation indexes are analyzed to obtain qualitative rules which guide crash energy management. This research reveals the energy absorption principle of the front-end structure during the frontal impact process, and provides an effective optimization method to manage the multiple conditions of the vehicle crash energy such as the FRB (frontal rigid barrier), the MPDB (mobile progressive deformable barrier), and the SOB (small overlap barrier).

Dynamical modeling of the H3K27 epigenetic landscape in mouse embryonic stem cells.

The Polycomb system via the methylation of the lysine 27 of histone H3 (H3K27) plays central roles in the silencing of many lineage-specific genes during development. Recent experimental evidence suggested that the recruitment of histone modifying enzymes like the Polycomb repressive complex 2 (PRC2) at specific sites and their spreading capacities from these sites are key to the establishment and maintenance of a proper epigenomic landscape around Polycomb-target genes. Here, to test whether such mechanisms, as a minimal set of qualitative rules, are quantitatively compatible with data, we developed a mathematical model that can predict the locus-specific distributions of H3K27 modifications based on previous biochemical knowledge. Within the biological context of mouse embryonic stem cells, our model showed quantitative agreement with experimental profiles of H3K27 acetylation and methylation around Polycomb-target genes in wild-type and mutants. In particular, we demonstrated the key role of the reader-writer module of PRC2 and of the competition between the binding of activating and repressing enzymes in shaping the H3K27 landscape around transcriptional start sites. The predicted dynamics of establishment and maintenance of the repressive trimethylated H3K27 state suggest a slow accumulation, in perfect agreement with experiments. Our approach represents a first step towards a quantitative description of PcG regulation in various cellular contexts and provides a generic framework to better characterize epigenetic regulation in normal or disease situations.

Menganalisis Efektifitas Sistem Pengendalian Internal terhadap Pelayanan Publik pada Bidang Perizinan di Dinas Penanaman Modal dan Pelayanan Terpadu Satu Pintu Kota Medan

Public services in goverment agencies, especially in the field of licensing services will not be separated from the practice of maladministration. To overcome this practice, a system that can control the implementation of public services is needed, namely the internal control system. The purpose of this research is to analyze the effectiveness internal control system for public services in the field of licensing at the invesment office and one- stop integrated service in the city of Medan. This type of research uses a qualitative description rules and all information obtained from the mechanism of observation, interviews and documentation. The results of this study indicate that the internal control system implemented in DPMPTSP Medan city has not been effective and optimal because there are still many complaints report from the public regarding the procedure system. Which is long- winded, takes a long time in obtaining permits, and it is found that Medan city DPMPTSP officers who work not yet competent and responsible for their work.
 Keywords: Public Service, Licensing, Internal Control System

Menganalisis Efektifitas Sistem Pengendalian Internal terhadap Pelayanan Publik pada Bidang Perizinan di Dinas Penanaman Modal dan Pelayanan Terpadu Satu Pintu Kota Medan

Public services in goverment agencies, especially in the field of licensing services will not be separated from the practice of maladministration. To overcome this practice, a system that can control the implementation of public services is needed, namely the internal control system. The purpose of this research is to analyze the effectiveness internal control system for public services in the field of licensing at the invesment office and one- stop integrated service in the city of Medan. This type of research uses a qualitative description rules and all information obtained from the mechanism of observation, interviews and documentation. The results of this study indicate that the internal control system implemented in DPMPTSP Medan city has not been effective and optimal because there are still many complaints report from the public regarding the procedure system. Which is long- winded, takes a long time in obtaining permits, and it is found that Medan city DPMPTSP officers who work not yet competent and responsible for their work.
 Keywords: Public Service, Licensing, Internal Control System

Strategies to Educate Traditional Market Traders Based on Information Technology in Beringharjo Market, Yogyakarta City

The research strategy educates traditional market traders based on information technology using a qualitative approach to case studies. Data were collected through observation, interviews and documentation studies. The validity of the data is processed through tests of the degree of trust, transferability, dependence and certainty. Informance is determined by purposive sampling techniques, then the data is analyzed following qualitative rules. The research aims to explore how the Trade Office educates market traders in Pasar Beringharjo through a digitalization program. The results showed that the Department of Trade in educating market traders based on information technology uses strategies: a) external party cooperation, training, mentoring, developing insights and involving trader family members for traders who are constrained by brainware. The driving factors in educating information technology-based traders include: high participation of information technology companies, demands for government and merchant needs, government awareness of IT developments to preserve and develop the market, covid 19 outbreak, high merchant commitment supported by mobile phone communication tools. The inhibiting factors in educating information technology-based traders include the hardware and software owned by merchants, not all smartphones, as well as merchant culture that is still conventional. The results of the merchant market digitization program maintain commitment and are able to carry out online transaction systems with various application models such as Gopay or e-wallet, QRIS, E-Retribution, LinkAja, Grocery, InMobi, Titipku, and Semar by scanning barcodes and other eknik t.

Attribute coordinate system‐based representation and application method of affective cognition

The traditional fuzzy Petri nets (FPNs) rarely contain the affective features when making reasoning decisions, primarily because the existing affective computing model is not suitable to be described directly in FPNs. In order to solve this problem, this paper introduces an attribute coordinate system and puts forward the affective representation and computing model based on the extraction and integration relationship between the attributes of things, which describes the affective change curve according to the cognition of the thinking subject to the attributes of the decision object. With this new model of affective computing, it is more convenient to utilize a qualitative mapping model and fuzzy qualitative judgment rules to express affective decisions and conveniently describe affective attributes in FPNs. A reasoning decision case illustrates the proposed method's feasibility and advantages.