Rule-based Research Articles

Applying inductive logic programming to automate the function of an intelligent natural language interfaces for databases

Applying inductive logic programming to automate the function of an intelligent natural language interfaces for databases

Digital stabilization of an I/Q modulator in the carrier suppressed single-sideband mode for atom interferometry

We present an all-digital method for stabilizing the phase biases in an electro-optic I/Q modulator for carrier-suppressed single-sideband (CS-SSB) modulation. Using programmable logic on the Red Pitaya STEMlab 125-14 platform, we digitally generate and demodulate an auxiliary radio-frequency tone whose beat with the optical carrier probes the I/Q modulator’s phase imbalances. We implement a multiple-input, multiple-output integral feedback controller that accounts for unavoidable cross-couplings in the phase biases to lock the error signals at exactly zero, where optical power fluctuations have no impact on phase stability. We demonstrate >23dB suppression of the optical carrier relative to the desired sideband at +3.4GHz over a period of 15 h and over temperature variations of 20°C.

CON-FOLD Explainable Machine Learning with Confidence

Abstract FOLD-RM is an explainable machine learning classification algorithm that uses training data to create a set of classification rules. In this paper, we introduce CON-FOLD which extends FOLD-RM in several ways. CON-FOLD assigns probability-based confidence scores to rules learned for a classification task. This allows users to know how confident they should be in a prediction made by the model. We present a confidence-based pruning algorithm that uses the unique structure of FOLD-RM rules to efficiently prune rules and prevent overfitting. Furthermore, CON-FOLD enables the user to provide preexisting knowledge in the form of logic program rules that are either (fixed) background knowledge or (modifiable) initial rule candidates. The paper describes our method in detail and reports on practical experiments. We demonstrate the performance of the algorithm on benchmark datasets from the UCI Machine Learning Repository. For that, we introduce a new metric, Inverse Brier Score, to evaluate the accuracy of the produced confidence scores. Finally, we apply this extension to a real-world example that requires explainability: marking of student responses to a short answer question from the Australian Physics Olympiad.

Evaluating health system resilience testing; what makes this practice successful?

Abstract Issue Health system resilience is an important and desirable attribute of health systems. We have evaluated the operationalization of our step-by-step guide for conducting a resilience test. Description of the problem Preparedness planning is often siloed and does not take the entire health system into account. The objective was to develop a new methodology for policy making that is adaptable to any health system context and shock scenario. Resilience testing uses a mixed-method approach to assess resilience by understanding health system strengths and weaknesses in relation to a hypothetical shock scenario. A programme logic model, semi-structured interviews and an online questionnaire were used to evaluate the methodology and to answer the following questions: Can resilience tests be used to identify strengths and weaknesses in different health system contexts? What are the determinants for successful resilience testing? Results Three resilience tests were conducted in Finland, Greece and the Asturias region of Spain. All resilience tests identified health system strengths and weaknesses. 97% of participants agreed that the test identified broader vulnerabilities. However, 76% thought that a different scenario would identify different strengths and vulnerabilities. 94% of participants agreed or strongly agreed that the test was a valuable exercise. The semi-structured interviews consistently found that successful resilience testing requires neutral facilitation and carefully selected test participants. Lessons Resilience test results have been used in 2 out of 3 cases to develop health policy. Resilience testing needs to be initiated and supported by the ministry of health to ensure results are considered in the policy making process. Key messages • Health system resilience tests can identify health system strengths, weaknesses, and steps towards remedial policy action to improve resilience in different European health system contexts. • Successful health system resilience testing requires carefully selected facilitators and test participants as well as support from the Ministry of Health.

Hyper CLS-Data-Based Robotic Interface and Its Application to Intelligent Peg-in-Hole Task Robot Incorporating a CNN Model for Defect Detection

Various types of numerical control (NC) machine tools can be standardly operated and controlled based on NC data that can be easily generated using widespread CAD/CAM systems. On the other hand, the operation environments of industrial robots still depend on conventional teaching and playback systems provided by the makers, so it seems that they have not been standardized and unified like NC machine tools yet. Additionally, robotic functional extensions, e.g., the easy implementation of a machine learning model, such as a convolutional neural network (CNN), a visual feedback controller, cooperative control for multiple robots, and so on, has not been sufficiently realized yet. In this paper, a hyper cutter location source (HCLS)-data-based robotic interface is proposed to cope with the issues. Due to the HCLS-data-based robot interface, the robotic control sequence can be visually and unifiedly described as NC codes. In addition, a VGG19-based CNN model for defect detection, whose classification accuracy is over 99% and average time for forward calculation is 70 ms, can be systematically incorporated into a robotic control application that handles multiple robots. The effectiveness and validity of the proposed system are demonstrated through a cooperative pick and place task using three small-sized industrial robot MG400s and a peg-in-hole task while checking undesirable defects in workpieces with a CNN model without using any programmable logic controller (PLC). The specifications of the PC used for the experiments are CPU: Intel(R) Core(TM) i9-10850K CPU 3.60 GHz, GPU: NVIDIA GeForce RTX 3090, Main memory: 64 GB.

Learning differentiable logic programs for abstract visual reasoning

AbstractVisual reasoning is essential for building intelligent agents that understand the world and perform problem-solving beyond perception. Differentiable forward reasoning has been developed to integrate reasoning with gradient-based machine learning paradigms. However, due to the memory intensity, most existing approaches do not bring the best of the expressivity of first-order logic, excluding a crucial ability to solve abstract visual reasoning, where agents need to perform reasoning by using analogies on abstract concepts in different scenarios. To overcome this problem, we propose NEUro-symbolic Message-pAssiNg reasoNer (NEUMANN), which is a graph-based differentiable forward reasoner, passing messages in a memory-efficient manner and handling structured programs with functors. Moreover, we propose a computationally-efficient structure learning algorithm to perform explanatory program induction on complex visual scenes. To evaluate, in addition to conventional visual reasoning tasks, we propose a new task, visual reasoning behind-the-scenes, where agents need to learn abstract programs and then answer queries by imagining scenes that are not observed. We empirically demonstrate that NEUMANN solves visual reasoning tasks efficiently, outperforming neural, symbolic, and neuro-symbolic baselines.

Implementation of Digital Computing by Colloidal Crystal Engineering with DNA.

Toehold-mediated strand displacement (TMSD) provides a versatile toolbox for developing DNA digital computing systems. Although different logic circuits with diverse functions have achieved good performance in terms of complexity and scalability, most previous DNA logic circuits perform information processing only at the molecular level, and nonspecific signal leakages are often difficult to avoid. Here, we demonstrate the feasibility of constructing leakless digital computing systems in three-dimensionally ordered colloidal supercrystals. These systems possess a unique signal leakage resistance by integrating different TMSD-based logic gates with the catalytic assembly of DNA-functionalized gold colloids. A complete set of basic Boolean logic gates and different cascaded logic circuits is constructed on the basis of the catalytic assembly strategy enabled by a facilely designed catassembler, where the output signals are recognized by determining whether specific colloidal supercrystals are formed or not. In addition, a half adder is built through a combination of XOR and AND logic gates with two distinct crystal types as readouts. Finally, a leakless two-digit DNA keypad lock for information security protection is demonstrated. The combination of TMSD-based logic circuits with the universal nanoparticle catalytic assembly offers the possibility to develop highly complicated and leakage-free digital computing systems and promotes macroscopic colloidal superlattice materials with programmable logic functions.

A geometric model with stochastic error for abnormal motion detection of portal crane bucket grab

Abnormal swing angle detection of bucket grabs is crucial for efficient harbor operations. In this study, we develop a practically convenient swing angle detection method for crane operation, requiring only a single standard surveillance camera at the fly-jib head, without the need for sophisticated sensors or markers on the payload. Specifically, our algorithm takes the video images from the camera as input. Next, a fine-tuned ‘the fifth version of the You Only Look Once algorithm’ (YOLOv5) model is used to automatically detect the position of the bucket grab on the image plane. Subsequently, a novel geometric model is constructed, which takes the pixel position of the bucket grab, the steel rope length provided by the Programmable Logic Controller (PLC) system, and the optical lens information of the camera into consideration. The key parameters of this geometric model are statistically estimated by a novel iterative algorithm. Once the key parameters are estimated, the algorithm can automatically detect swing angles from video streams. Being analytically simple, the computation of our algorithm is fast, as it takes about 0.01 s to process one single image generated by the surveillance camera. Therefore, we are able to obtain an accurate and fast estimation of the swing angle of an operating crane in real-time applications. Simulation studies are conducted to validate the model and algorithm. Real video examples from Qingdao Seaport under various weather conditions are analyzed to demonstrate its practical performance.

Belief-Rule-Based System with Self-organizing and Multi-temporal Modeling for Sensor-based Human Activity Recognition.

Smart environment is an efficient and cost- effective way to afford intelligent supports for the elderly people. Human activity recognition (HAR) is a crucial aspect of the research field of smart environments, and it has attracted widespread attention lately. The goal of this study is to develop an effective sensor-based HAR model based on the belief-rule-based system (BRBS), which is one of representative rule-based expert systems. Specially, a new belief rule base (BRB) modeling approach is proposed by taking into account the self- organizing rule generation method and the multi-temporal rule representation scheme, in order to address the problem of combination explosion that existed in the traditional BRB modelling procedure and the time correlation found in continuous sensor data in chronological order. The new BRB modeling approach is so called self-organizing and multi-temporal BRB (SOMT-BRB) modeling procedure. A case study is further deducted to validate the effectiveness of the SOMT-BRB modeling procedure. By comparing with some conventional BRBSs and classical activity recognition models, the results show a significant improvement of the BRBS in terms of the number of belief rules, modelling efficiency, and activity recognition accuracy.

Otomatisasi Penyemprotan Polyester Menggunakan Kawasaki Cobot dengan Human Machine Interface (HMI) Berbasis Web

Brago Luchttechniek BV is a company specializing in the production of PIR (Polyisocyanurate) air ducts, facing challenges in maintaining accuracy and consistency in the polyester insulation spraying process on its products using Kawasaki collaborative robots (cobot) for automation. To address these challenges, this study designed a web-based Human Machine Interface (HMI) to automate the polyester insulation spraying process on PIR air ducts using the Kawasaki Cobot. A web-based HMI was chosen for its ease of development, flexibility, and integration capabilities with modern technology. This HMI uses the TCP/IP protocol to send real-time command signals to automation devices such as the Kawasaki Cobot and devices connected to the Programmable Logic Controller (PLC). Testing results demonstrated an improvement in the spraying process efficiency, reducing the time from 15–18 minutes conventionally to 5–7 minutes with the web-based HMI automation. Additionally, the web-based HMI successfully communicated with other devices connected to the robot, such as the conveyor belt and rotating table, via TCP/IP protocols, with each device having its own ID and IP address to facilitate linear message delivery, prevent collisions, and minimize errors. After eight successful automation tests on four different products, the system operated smoothly and systematically through the available HMI controls. The system was also evaluated by four respondents, with an overall score of 4.33 out of 5, indicating a high level of satisfaction.

DEVELOPMENT AND EVALUATION OF A MULTI-CHAMBER ENVIRONMENTAL CONTROL SYSTEM

Growth chambers are control devices used in plant research. However, their high costs limit their application to a single environment, which compromises their efficiency for the generation of experimental data. The objective of this research was to design, construct, and assess the performance of a multi-chamber environmental control system capable of regulating both air temperature (AT) and relative humidity (RH). The evaluation was carried out using Syngonium podophyllum Schott plants. Three constant target temperature values (18, 23, and 40 °C) were established, together with a target RH value of 60 %. AT and RH data were recorded every 4 min for three days. The system was built with 15 growth chambers and the following devices: output (AT, RH, and lighting control), input (sensors), and central control managed by two Arduino® Mega 2560 based microcontrollers, plus a programmable logic controller. Programming was done in open-source languages such as Arduino® and Python®. Results indicate that the absolute error (AE) and standard deviation (SD) in AT were higher when the target temperature was lower (AE = 0.59 °C and SD = 0.93 °C) compared to the other higher target temperatures (AE = 0.31 °C and SD = 0.43 °C at 23 °C, and AE = 0.25 °C and SD = 0.51 °C at 40 °C). On the other hand, the AE in RH increased as temperature increased, going from AE = 3.75 % and SD = 5.12 % at 18 °C, to AE = 9.23 % and SD = 10.77 % at 40 °C. The use of open-source technologies allowed the construction of a multi-chamber environmental control system capable of replicating controlled environments.

Rapid urease test according to the rules and without

The article discusses the problems of diagnosing Helicobacter pylori (H.pylori) using a rapid urease test (RUT). Features of H.pylori colonization and persistence are highlighted, which affect the results of RUT and determine the preferential use of biopsy specimens for the study, rather than gastric mucus aspirate, the localization of biopsy collection and their optimal number and the factors influencing the urease activity of H.pylori are indicated. Based on the described properties, the main causes of erroneous results and methods for preventing false negative and false positive results are given, the basic rules for working with RUT are formulated to obtain optimal results.

Crimes against Palestinian Civilians Under Zionist Occupation

Throughout its long history, humanity has coexisted with conflicts, power, influence, policies of violence and distress, bloodbaths, massacres, racial calamities and famines. Its present and future visions are still subject to horrors, hardships and tragedies. International humanitarian law has come to express these values in legal language that imposes the obligation on the States parties that make up these agreements. The basic rules and principles of this law have become a set of values accepted by the entire international community and to know that the civilian population is the first to be exposed in most cases to acts of aggression and violence. In different parts of the world, we are witnessing the process of ethnic cleansing, mass killings and atrocities committed against civilians and destroying their homes and property. This is what prompted the researcher to choose his research ( crimes committed against Palestinian civilians under the Zionist occupation) to answer many questions, including knowing who are civilians through the concept of crimes against civilians. The international trend of retaliation against civilians was rejected during the military operations. In the UN General Assembly resolution adopted in 1970, it is not permissible to retaliate against the civilian population, whether they are a group or individuals in armed conflicts. Civilians, in particular, within the framework of international humanitarian law, which is a newly established law that includes a set of international legal rules targeting situations of armed conflict and the protection of civilians suffering from the scourge of this conflict , and the subsequent military occupation. The rules of international humanitarian law are characterized by moral highness, as they codify the rules of morality that must prevail during armed conflict and military occupation

Integrating reinforcement learning and supervisory control theory for optimal directed control of discrete-event systems

Directed control is crucial for implementing controllers on programmable logic controllers. A directed controller is one that actively selects at most one controllable event (control command) to execute at any instant. This study investigates the numerical optimization problem of directed control for discrete-event systems, such as traffic systems and robotic systems. By integrating supervisory control theory and reinforcement learning, an algorithmic procedure is designed to synthesize an optimal directed controller, which minimizes the total cost associated with event execution while ensuring the safety and liveness of the controlled system. Two improved Q-learning algorithms incorporating dynamic parameter adaptation strategies are developed to enhance the global search ability of basic Q-learning. To demonstrate applicability and effectiveness, we apply the proposed method to control a guideway system and a multi-train traffic system, respectively. The experimental results indicate the superiority of the proposed method over the comparison methods.

A New Method Based on Belief Rule Base with Balanced Accuracy and Interpretability for Student Achievement Prediction

In the field of education, the accurate prediction of students’ future performance is essential for personalized instruction and efficient allocation of resources. Such predictions not only help education professionals develop targeted educational strategies but also identify students’ learning needs at an early stage so that timely interventions and support can be provided. To gain the trust of educational experts and ensure the practical application value of the prediction results, the prediction methods used must be highly interpretable. However, there are two problems with the current belief rule base (BRB) applied to student performance prediction. First, there is a current lack of effective strategies for enhancing the interpretability of the optimization process. Second, BRB models that overemphasize accuracy tend to exhibit characteristics of black-box models. To overcome these challenges, this paper proposes a new method based on BRB with balanced accuracy and interpretability (BRB-Bai) for student achievement prediction. First, an attribute selection method is proposed to filter out important features associated with student performance. Then, expert knowledge credibility is calculated, and four interpretability strategies are proposed to ensure the interpretability of the model and to achieve a balance between interpretability and accuracy on the basis of expert knowledge credibility. The effectiveness of the proposed model is demonstrated by conducting experiments on the student achievement dataset.

Six Basic Rules of Palliative Care and Their Buddhist Counterparts.

As healthcare workers in palliative care, every day brings its share of awfulness and beauty, suffering and connection, meaning and cynicism. Without a way to support ourselves, we cannot help our patients, let alone one another. But how do we cope? Despite the unpredictability inherent in our work, we can discern certain patterns that offer an approach for dealing with these stressors. These patterns can be summarized into six simple rules of palliative care-rules that are coterminous with the teachings of Buddhism. Recognizing the synergy between our role in palliative care and ancient observations about dealing with life's difficulties can help us with the stress of providing conscientious care within a system that relentlessly tries to stop us from doing so. Such considerations offer one of many paths we might choose to cope with this challenging work.

An efficient propositional system for Abductive Logic Programming

Abductive logic programming (ALP) extends logic programming with hypothetical reasoning by means of abducibles, an extension able to handle interesting problems, such as diagnosis, planning, and verification with formal methods. Implementations of this extension have been using Prolog meta-interpreters and Prolog programs with Constraint Handling Rules (CHR). While the latter adds a clean and efficient interface to the host system, it still suffers in performance for large programs. Here, the concern is to obtain a more performant implementation of the SCIFF system following a compiled approach. This paper, as a first step in this long term goal, sets out a propositional ALP system following SCIFF, eliminating the need for CHR and achieving better performance.

Cybersecurity in Prognostics and Health Management

PHM continues to show its value by improving operational efficiencies, increasing safety, reducing downtime, and decreasing cost of operations. PHM technologies are therefore not only being deployed as retrofit solutions but are being integrated into new systems as standard practice. Deployment covers areas such as medical equipment, nuclear power plants, aeronautics applications, oil and gas, mining, and many others. As the impact of PHM increases, it is imperative to also consider the potential vulnerabilities that are being exposed. Hackers have famously used Supervisory Control and Data Acquisition (SCADA) and Programmable Logic Controller (PLC) systems to sabotage industrial facilities. As such, it is important to understand the exposure to malfeasance to ensure that PHM does not end up being the enabling mechanism for unauthorized access to the system it is meant to keep in running order. It is also important to understand the measures that need to be taken to avoid or respond to an attack. These range from extensive penetration testing to conducting extensive counter-social engineering training, setting up a PHM-specific CERT plan and team in place. This paper discusses various threats that are emerging and that may have to be considered when designing a PHM solution. Additionally, the NIST cybersecurity framework is discussed in the context of PHM. Finally, this paper looks at the diagnostic capabilities of PHM systems to detect cyber security attacks and to contain these threats.

A public health/hospital partnership to improve Emergency Department transitions of care for vulnerable older adults.

Community-dwelling older adults are at high risk for unmet social service needs. We describe a novel partnership embedding county services case managers in the Emergency Department (ED) to connect older adults to community services alongside their medical care. Setting: A medium-sized urban ED with 55,000 patient visits a year. Case managers from the Franklin County, Ohio Office on Aging (OA) were embedded within the ED. The OA team worked with the ED social work team to identify community-dwelling older patients, perform an in-person intake assessment, and initiate needed community services (including home-delivered meals, emergency response systems, house repairs, and transportation). Program logic model and development are reported in detail. From June to December 2023, there were 7284 ED visits for adults ≥60 years old. Referrals to the OA case manager ranged from 1 to 13 per day. The OA case managers performed 252 full intake assessments on unique patients. The population was 51% men. Only 11% (n = 28) were currently connected to OA services, and of those already connected 29% (n = 8) needed increased services. Of the remaining unconnected patients (n = 224), 8% (n = 20) were not county residents and the OA team connected them with other county OAs. Half 53% (n = 120) were accepting of services and had services from the OA or other community health programs initiated during the ED visit. The OA team made three new Adult Protective Services referrals and one referral to the long-term care ombudsman. The program did not increase ED length of stay or hospital admission rates. Embedding county service enrollment within a community ED is a cost neutral intervention that reached a population without previous services. Future plans include expansion of the program and evaluation of the program's ability to detect elder mistreatment and self-neglect.

Islamic commercial law in action: a look into AAOIFI’s Sharīʿah standard on Waqf

Purpose The purpose of this paper is to analyse the Sharīʿah Basis of AAOIFI’s Sharīʿah Standard No. (60) – “Waqf” and gain an insight into the Islamic jurisprudential schools of thought depended on while drafting/issuing the standard. Design/methodology/approach This research uses a qualitative methodology and AAOIFI’s Sharīʿah Standard No. (60) – “Waqf” as a case study. This is referred to as a single/holistic case study design. Findings The findings of this study reveal that while AAOIFI’s Sharīʿah Standard No. (60) – “Waqf” did depend on the Maliki school of fiqh, it certainly referenced other jurisprudential schools of thought, such as Ḥanafī and Shāfiʿī schools of law. This raised a question of legal pluralism and how contemporary Sharīʿah jurisconsults are able to select rulings within particular Islamic jurisprudential schools to fit current needs and for the overall betterment of society. Research limitations/implications This research does not delve into the technical aspects of AAOIFI’s waqf standard (furūʿ al-fiqh), but rather, is limited to understanding the basis of rulings therein and the jurisprudential schools of thought heavily depended on. Originality/value Given that the (revised) AAOIFI waqf standard is relatively new, limited studies have been conducted particularly focusing on the basis of Sharīʿah rulings therein. This research contributes to knowledge by providing one of the starting points to deliberate on this matter.