Programming System Research Articles

Incidence of A.I. and TECD Assessment-Learning in University Students

Currently it is seen a diversity in the use of digital tools emerging every day, so A.I. appears as an ally in the academic formation on the assessment-leaning and the TECD (Technology of digital knowledge empowerment). The main objective is to determine the incidence that exists between artificial intelligence and TECD assessment-leaning in the empowerment of digital knowledge in university students. The method used in the research is of basic type, with positivist paradigm of quantitative approach, because it measures the casual relationship that exists between the dependent variables, with non-experimental correlational causal design using surveys for data collection in the students at a university. In the results, it is obtained that 33.3% of students manage to empower themselves with digital knowledge creating materials within specific works according to their professional training lines; in the intermediate level, the result is that 35.9% of students manage to have a regular domain on programming systems and model new computer systems creating a computational system, neural network training and deep learning. In conclusion, data collection and analysis are powerful tools in the continuous improvement of education.

Ecosystem services assessment of Beledanga oxbow lake in the Gangetic plains: pathways to sustainable conservation

Oxbow lakes, formed from abandoned river meanders, are unique wetlands that play a crucial role in supporting aquatic biodiversity and sustaining local communities through their ecosystem services. This study focuses on the Beledanga oxbow lake, highlighting its ecosystem services and the critical role of hydrological connectivity with the adjacent river for maintaining ecological functionality and sustainability. Using the Millennium Ecosystem Assessment framework, the study assessed provisioning, regulating, cultural, and supporting services through field surveys, stakeholder consultations, and secondary data analysis. Results show that the lake provides essential provisioning services, such as fisheries and irrigation, which support local food security and income generation. It also plays a role in regulating services like water management and carbon sequestration, while its biodiversity underpins vital ecological functions, including nutrient cycling and habitat provision. However, challenges such as seasonal water scarcity and macrophyte infestation threaten the lake's productivity and connectivity with the river. The study emphasizes the need for collaborative efforts involving local stakeholders to devise water management strategies and remove macrophytes, utilizing government initiatives like the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) for funding. Additionally, the establishment of robust monitoring systems for sustainable fisheries management and incentive programs for conservation efforts are recommended. The research highlights the importance of hydrological connectivity in sustaining the lake's ecosystem services and enhancing biodiversity. By integrating local knowledge and stakeholder engagement, this study provides valuable insights into the sustainable management of oxbow lakes and highlights the broader significance of river-oxbow lake connectivity in wetland conservation efforts. The findings also have implications for broader wetland conservation policies in similar ecosystems.

Authentication and authorization in microservice oriented application design.

In the past years microservice oriented design patterns obtained much popularity among the system architects and programmers. One of the obstacles in this design is authentication and authorization issues. This article presents one of the possible solutions for this problem. The essence of this methodology is a single point of authentication and many points of authorization spread across all the microservices.

Cross-validation of the Biofuels Beliefs Scale (BBS) on a European sample: a tool to measure the perception of the technological and contextual features of biofuels.

Studies on the acceptance of renewable and sustainable energy technologies have grown exponentially over the past few decades. While there are a large number of technology acceptance models, none of them includes belief-related variables. Developed within the EC H2020 XXXXXX project, this contribution focuses on the cross-validation, in a large sample (N = 1016), across eight European countries, of the Biofuels Beliefs Scale (BBS). The BBS is composed of 26 items, organized into six factors (i.e., Policy Making Legitimation, Emissions Sustainability, Global Environmental Sustainability, Technology Compatibility, Local Socio-Economic Sustainability, and Cost Savings). Factors are distinct, reliable, and each one composed of a psychometrically acceptable number of items. The validation procedure fulfilled the adequacy requirements regarding convergent, discriminant, and predictive validity. The BBS could be useful both for testing models on technology acceptance in future studies and for communication campaigns on biofuel-related issues in applied contexts (e.g., pre-/post-assessment, monitoring, etc.).

Intelligent Numerical Control Programming System Based on Knowledge Graph

With the wide application of computer-aided manufacturing (CAM) software, manufacturing enterprises have accumulated a wealth of numerical control (NC) programming data, providing valuable knowledge resources for new products’ development. Efficiently acquiring and reusing existing NC knowledge is essential for enhancing programming efficiency, improving product quality, and shortening manufacturing cycles. This study proposes an intelligent NC programming method based on knowledge graph. Firstly, the relevant knowledge in the NC programming domain is analyzed, and CAM knowledge elements are constructed to reduce the granularity of knowledge. Then, the ontology layer and data layer are constructed to achieve the development of the knowledge graph. Next, knowledge reasoning is performed on the knowledge graph through entity alignment and semantic rule-based reasoning. Furthermore, to address the issues of low reliability, limited applicability and need for frequent manual modifications in NC programming templates guided by the CAM knowledge graph, a CAM knowledge graph completion method based on neighborhood aggregation and semantic enhancement is proposed. Finally, an intelligent NC programming system based on knowledge graph is developed, and comparative experiments with mainstream algorithms on public datasets for few-shot knowledge graph completion are conducted, validating the effectiveness of the proposed method by experimenting with the key components of marine diesel engines.

Perceptions of Relationships with Kinship Caregivers Among Parents Following State-Detected Psychoactive Substance Use

SYNOPSIS Objective. This study sought to better understand parent relationships with kinship caregivers, with an eye toward parents’ perceptions of how kinship caregiver mindsets and behaviors surrounding substance use may have impacted parents. Design. Data came from transcripts of in-depth, semi-structured interviews with 16 parents in the United States, who had substantiated child welfare cases related to substance use. Transcripts were dual-coded, and analysis was guided by the reflexive thematic analysis framework. Results. Parents described two different experiences with kinship caregivers. For some families, parents reported fragmentation of their support system; parents described caregivers as undermining their parental authority, disrupting attachment bonds, and communicating with children in ways that perpetuated stigma around substance use. Other parents reported increased cohesion among family members; parents described caregivers as supportive and empowering, particularly as they navigated mandated “recovery.” Conclusions. Findings offer insights to help inform decision-making by both practitioners and policymakers by giving a voice to parents as they navigate the complex systems of child welfare services and drug court programming.

A QSimulator Quantum Programming System

In this paper the authors propose their own QSimulator quantum programming system, which is a software tool for deve­loping and debugging quantum algorithms in a high-level C++ language. The basic structure of the presented system is de­scribed, including quantum algorithms and quantum operations, available to the programmer. The components of the system and their fundamental basis are briefly outlined. This system emulates the behaviour of a quantum coprocessor in a standard computational basis and thus can be used for debugging and programming quantum algorithms in the language of quantum circuits. The system proposed by the authors also implements the Grover search algorithm and the Durr-Hoyer minimization algorithm based on it. Many tasks can be formulated in terms of the search task. In particular, the SAT problem. In this article, we also propose a variant of constructing an oracle in the form of a quantum circuit for the SAT problem, which can be integrated into the Grover algorithm implemented in the presented system. Automatic oracle generation for the SAT task will help save time in developing quantum algorithms related to this task when working with our system.

A Study of The Geographical Information System (GIS) based Energy Station Identification and Frequency Analysis Using ETA

The energy corporation entity recently pays maximum attention to the environmental and social safe guards associated with its operations. In developing countries mainly suffers to balance the gap between generation and demand for effective operation by the energy utilities. An inadequate transmission capacity, non-uniform location of energy stations may lead to worst consequences when the occurrences of fault and may end up with black out. To overcome the above situation and to improve the stability of such system, In this research proposes a Geographical Information System (GIS) based identification of location of the uncertainty power stations and also a technique called Islanding Scheme to protect the severely affected system by measuring its frequency parameters. The severely disturbed large interconnected power system is divided into number of subsystems with two or more generating buses feeding its nearby loads by disintegrating the tie lines. The sample 50 bus test case system taken from Tamilnadu Electricity Board (TNEB), INDIA to analyze and simulate the results using Electrical Transient Analyzer Program (ETAP) and Geographical Information System (GIS). In this paper, ETAP and GIS effective software tools used for identify the energy station in various locations and analyzing the frequency status of different buses for better operation in electric utilities and thus ensure better management, enhance the improved stability by mapping the data and outage management.

Optimizing Group Size using Percentile Based Group Acceptance Sampling Plans with Application

The present paper focuses on optimizing the sample size and the acceptance number which are commonly known as design parameters for the group acceptance sampling plan (GASP). Design parameters are analyzed under the assumption that the characteristic of interest for the product follows Another Generalized Transmuted-Exponential (AGTransmuted-Exponential) distribution. Using different values of the quality levels (median lifetime), the values of the Operating Characteristic function is determined. The proposed plan satisfies two types of risks based on producer's point of view and consumer’s point of view at varied stipulated quality levels. Optimization of the sample size, group size and acceptance numbers is obtained through Monte Carlo simulation, for which relevant R codes were developed. Specific R codes are appended for future usage by academia and practitioners from various fields of life. The simulated results of the study are exhibited in the form of tables and explained with relevant examples. Results of the proposed GASP are compared with plan parameters obtained using MLE estimates of AGTransmuted-Exponential distribution and also by design parameters obtained using mean as quality level. Results of the study exhibited that Median as a quality parameter resulted in the decrease of group size and acceptance number simultaneously at all quality levels. Easy to follow the methodology of the current paper will open new vistas for applying the proposed GASP to a family of transmuted probability distributions. For illustration purposes, a real data set for fatigue fracture stress is analyzed using MLE estimates of AGTransmuted-Exponential distribution to demonstrate the implementation of the proposed sampling plan.

Extending battery lifetime of electric-hydraulic hybrid wheel loader through system parameter optimization

Parallel electric hydraulic hybrid powertrain (PEHH) is a promising substitute for electric direct drive to extend battery lifetime. The convex programming (CP) based simultaneous optimization method has been adopted in electric hybrid system parameter optimization, but it is difficult to extend it for PEHH due to its non-convexity. In this paper, a combined exhaustive search – convex programming (ES-CP) system parameter optimization method is proposed to address the non-convexity of PEHH system parameter optimization problem. In this method, the PEHH system parameter optimization problem is modeled and reformulated in convex form and the convex part is decoupled from the optimization problem. The CP method is used for the convex part and the ES method is used for the remained non-convex part. The ES-CP method is applied to the PEHH powertrain to extend its battery lifetime. Optimization results show that the battery lifetime of the ES-CP optimized PEHH system is 6.8% longer than that of the non-optimized system. The computing time of ES-CP is 97% lower than Latin hypercube sampling and 90% lower than genetic algorithm methods.

A highly-configurable session designer for VR nursing training

Virtual Reality (VR) has proven to be a valuable tool for medical and nursing education. VR simulators are available at any time and from anywhere, and can be used with or without faculty supervision, which results in a significant optimization of time, space, and resources. In this paper we present a highly-configurable session designer for VR-based nursing education following the Standards for QUality Improvement Reporting Excellence: SQUIRE 2.0 and SQUIRE-EDU. Unlike existing platforms, we focus on letting educators quickly customize the training sessions in multiple aspects. This is achieved through the use of a visual editor, where educators can choose the environment and the elements in the session, and a node-based visual system for programming the behavior of the sessions. Thanks to end-user customization, educators can develop different variants of a session, adapt the sessions to new nursing protocols, incorporate new equipment or instruments, or accommodate custom environments. We have implemented the designer and tested it on various nursing training scenarios. The designer can be integrated into VR simulators to help educators save time in delivering highly customizable training.

Biofeedback system in remotely monitored exercise program in pregnant women with obesity: a pre-experimental research

Objectives: E-health programs may be an excellent strategy to overcome some barriers related to the non-practice of exercise among pregnant women with obesity. This study aims to present a monitored exercise program for pregnant women with obesity using the Sword Phoenix® biofeedback system; and to evaluate pregnant women's perception of the biofeedback system and the exercise program protocol. Methods: A pre-experimental research was done through a One-shot case study design. Twelve pregnant women with Body Mass Index ≥ 30 kg/m2, were invited to perform for two weeks a home-based structured exercise program protocol with a digital biofeedback system, during exercise the women used an accelerometer to measure the intensity of the exercises. At the end of the two-week exercise period, the pregnant women filled in a questionnaire to assess their perception of usability, satisfaction, safety and motivation regarding exercise program and biofeedback system. Results: The accelerometry results indicate that, during the exercise program protocol, the pregnant women spent 79.3% of the time in moderate activities. The adherence to the program had an average performance of 40%. The results of the questionnaire demonstrated that 100% (n=12) of the participants enjoyed the system and recognized it as a safe instrument with the potential to increase physical activity during pregnancy. Discussion: The biofeedback system can be a useful tool for implementing an exercise program for pregnant women with obesity. The proposed exercise program complies with international recommendations for physical exercise during pregnancy, ensuring safety. Additionally, it reduces the team intervention, increases participant motivation, and has the potential to reduce sedentary behavior in pregnant women with obesity.

Vitamin C intake, psychological distress and health outcomes in patients with heart failure

Abstract Background Patients with heart failure (HF) have lower dietary vitamin C intake than healthy adults, which may increase oxidative stress and worsen important patient-reported health outcomes (functional status and quality of life [QOL]). Patients with HF are more likely to have higher levels of psychological distress (anxiety and depressive symptoms). However, it is not known whether psychological distress is a link in the association of dietary vitamin C with health outcomes. Objective To examine the inter-relationships among dietary vitamin C intake, psychological distress (anxiety and depressive symptoms), and health outcomes (functional status and QOL) in patients with HF. Method We performed a secondary analysis of baseline data from 206 adults with HF (female: 33%, age: 61±12, NYHA class III/IV: 44%) with complete data on dietary vitamin C intake, anxiety, depressive symptoms, functional status, and QOL. Dietary vitamin C intake was assessed using a 4-day food diary and analyzed using the Nutrition Data System for Research software program. Low dietary vitamin C intake was defined as averaged daily intake below the estimated average requirement (EAR) from the Institute of Medicine of 75 mg/day for men and 60 mg/day for women. Anxiety was assessed using the Brief Symptom Inventory Anxiety subscale. Depressive symptoms were assessed using the Beck Depression Inventory II. Functional status was measured using the Duke Activity Status Index. Quality of life was measured using the Minnesota Living with Heart Failure Questionnaire. Linear regression was conducted using the PROCESS macro in SPSS. Results Eighty-four patients (41%) had low dietary vitamin C intake based on the EAR standard. In bivariate analyses, patients with low dietary vitamin C intake had higher levels of anxiety, depressive symptoms, poorer functional status, and worse QOL (all p <.005). In the mediation regression model, low dietary vitamin C intake was directly associated with higher levels of anxiety, but not with depressive symptoms. Depressive symptoms were directly associated with functional status and QOL. Dietary vitamin C intake was not directly associated with functional status or QOL. Low dietary vitamin C intake was indirectly associated with poor functional status through higher levels of anxiety and depressive symptoms (Figure 1). Similarly, low dietary vitamin C intake was indirectly associated with worse QOL (Figure 2). Conclusion Anxiety and depressive symptoms link the relationship between dietary vitamin C intake and functional status and between dietary vitamin C intake and QOL. Our findings highlight the importance of including interventions to enhance diet quality and psychological well-being as part of HF treatment to improve health outcomes.Vitamin C and functional statusVitamin C and quality of life

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.

Unleashing quantum algorithms with Qinterpreter: bridging the gap between theory and practice across leading quantum computing platforms.

Quantum computing is a rapidly emerging and promising field with the potential to transform various research domains including drug design, network technologies, and sustainable energy solutions. Due to the inherent complexity and divergence from classical computing, several major quantum computing libraries have been developed to implement quantum algorithms, namely IBM Qiskit, Amazon Braket, Cirq, PyQuil, and PennyLane. These libraries enable quantum simulations on classical computers and execution on corresponding quantum hardware, such as Qiskit programs on IBM quantum computers. Despite the variations among these platforms, the core concepts remain the same. One notable challenge is the absence of a Python-based quantum interpreter to connect these five frameworks, a gap that remains to be fully addressed. In response, our work introduces a tool called Qinterpreter, accessible through a user-friendly web interface, the Quantum Science Gateway QubitHub, which operates alongside Jupyter Notebooks. Built using the Python Object-Oriented Programming System, Qinterpreter unifies the five well-known quantum libraries into a single framework. Designed as an educational tool for students and researchers entering the quantum domain, Qinterpreter enables the straightforward development and execution of quantum circuits across such platforms. This work highlights the quantum programming versatility and accessibility of Qinterpreter and underscores our ultimate goal of pervading Quantum Computing through younger, less specialized, and diverse cultural and national communities.

Dynamic Programming-Based ANFIS Energy Management System for Fuel Cell Hybrid Electric Vehicles

Reducing reliance on fossil fuels has driven the development of innovative technologies in recent years due to the increasing levels of greenhouse gases in the atmosphere. Since the automotive industry is one of the main contributors of high CO2 emissions, the introduction of more sustainable solutions in this sector is fundamental. This paper presents a novel energy management system for fuel cell hybrid electric vehicles based on dynamic programming and adaptive neuro fuzzy inference system methodologies to optimize energy distribution between battery and fuel cell, therefore enhancing powertrain efficiency and reducing hydrogen consumption. Three different approaches have been considered for performance assessment through a simulation platform developed in MATLAB/Simulink 2023a. Further validation has been conducted via a rapid control prototyping device, showcasing significant improvements in hydrogen usage and operational efficiency across different drive cycles. Results manifest that the developed controllers successfully replicate the optimal control trajectory, providing a robust and computationally feasible solution for real-world applications. This research highlights the potential of combining advanced control strategies to meet performance and environmental demands of modern heavy-duty vehicles.

CoolerSpace: A Language for Physically Correct and Computationally Efficient Color Programming

Color programmers manipulate lights, materials, and the resulting colors from light-material interactions. Existing libraries for color programming provide only a thin layer of abstraction around matrix operations. Color programs are, thus, vulnerable to bugs arising from mathematically permissible but physically meaningless matrix computations. Correct implementations are difficult to write and optimize. We introduce CoolerSpace to facilitate physically correct and computationally efficient color programming. CoolerSpace raises the level of abstraction of color programming by allowing programmers to focus on describing the logic of color physics. Correctness and efficiency are handled by CoolerSpace. The type system in CoolerSpace assigns physical meaning and dimensions to user-defined objects. The typing rules permit only legal computations informed by color physics and perception. Along with type checking, CoolerSpace also generates performance-optimized programs using equality saturation. CoolerSpace is implemented as a Python library and compiles to ONNX, a common intermediate representation for tensor computations. CoolerSpace not only prevents common errors in color programming, but also does so without run-time overhead: even unoptimized CoolerSpace programs out-perform existing Python-based color programming systems by up to 5.7 times; our optimizations provide up to an additional 1.4 times speed-up.

Compilation of Shape Operators on Sparse Arrays

We show how to build a compiler for a sparse array language that supports shape operators such as reshaping or concatenating arrays, in addition to compute operators. Existing sparse array programming systems implement generic shape operators for only some sparse data structures, reduce shape operators on other data structures to those, and do not support fusion. Our system compiles sparse array expressions to code that efficiently iterates over reshaped views of irregular sparse data structures, without needing to materialize temporary storage for intermediates. Our evaluation shows that our approach generates sparse array code competitive with popular sparse array libraries: our generated shape operators achieve geometric mean speed-ups of 1.66×–15.3× when compared to hand-written kernels in scipy.sparse and 1.67×–651× when compared to generic implementations in pydata/sparse. For operators that require data structure conversions in these libraries, our generated code achieves geometric mean speed-ups of 7.29×–13.0× when compared to scipy.sparse and 21.3×–511× when compared to pydata/sparse. Finally, our evaluation demonstrates that fusing shape and compute operators improves the performance of several expressions by geometric mean speed-ups of 1.22×–2.23×.

Customizing Bayesian multivariate generalizability theory to mixed-format tests.

Mixed-format tests, which typically include dichotomous items and polytomously scored tasks, are employed to assess a wider range of knowledge and skills. Recent behavioral and educational studies have highlighted their practical importance and methodological developments, particularly within the context of multivariate generalizability theory. However, the diverse response types and complex designs of these tests pose significant analytical challenges when modeling data simultaneously. Current methods often struggle to yield reliable results, either due to the inappropriate treatment of different types of response data separately or the imposition of identical covariates across various response types. Moreover, there are few software packages or programs that offer customized solutions for modeling mixed-format tests, addressing these limitations. This tutorial provides a detailed example of using a Bayesian approach to model data collected from a mixed-format test, comprising multiple-choice questions and free-response tasks. The modeling was conducted using the Stan software within the R programming system, with Stan codes tailored to the structure of the test design, following the principles of multivariate generalizability theory. By further examining the effects of prior distributions in this example, this study demonstrates how the adaptability of Bayesian models to diverse test formats, coupled with their potential for nuanced analysis, can significantly advance the field of psychometric modeling.

RANCANG BANGUN PROTOTYPE SMART GREENHOUSE MENGGUNAKAN THINGSPEAK

The design of this smart greenhouse prototype is one of the implementations of artificial intelligence controlled by the Arduino uno microcontroller, which has actuators in the form of lights and pumps and is equipped with DHT22 sensors, Soil Moisture and Temperature Sensors. The method used in making this smart greenhouse prototype is the Prototype method. There are seven stages in the prototype method that guide the process of making robots, namely gathering requirements, building prototypes/prototypes, evaluating prototypes, programming systems, testing systems, evaluating systems, and using the system. The most important stage in making this smart greenhouse prototype is the stage where at this stage testing is carried out on the results of the design and programming of the robot that has been made to prove the robot prototype is in accordance with what is desired or not. The testing process is carried out in tandem with the assembly process and programming process. In the process of assembling and programming the robot prototype, it is done with system errors. Testing with system errors is carried out to get a robot prototype that works optimally.