Programming Framework Research Articles

Approaches to inclusive foreign language instruction in higher education

Introduction: The rapid development trend in innovative technological solutions significantly impacts societal activities, stimulating global digital transformation processes in the educational environment. Objectives: The article analyses innovative tools within the concept of inclusive technologies for teaching foreign languages in higher education in the digital age. Methods: Several general scientific research methods were used to achieve the research objectives, including abstraction, analysis, synthesis, induction, and deduction. Results: Attention is focused on exploring the potential of audio-lingual and audiovisual methods, didactic game solutions, case methods, reproductive methods, thematic studies, linguistic and sociocultural, and project methodologies. It has been proven that using digital educational platforms in educational programmes allows teaching foreign languages to be carried out at a high professional level, stimulating aspects of motivation and effectiveness. The study identifies that implementing innovative approaches in inclusive strategies for teaching foreign languages enables students to gain practical skills through active interaction with various non-standard methods and forms of work. Such methods include brainstorming, project work, role-playing, group work, and several others. It has been found that specific innovative approaches, such as random and cross-learning, learning through argumentation, or embodied learning, form a solid practical basis for the successful future application of acquired knowledge. Conclusions: The article proves that innovative methods of teaching foreign languages within the framework of inclusive educational technology programmes have the potential to create an effective learning environment, ensuring a significant improvement in the quality of higher education

Optimal energy management and scheduling of a microgrid considering hydrogen storage and PEMFC with uncertainties

Renewable energy sources have been widely installed and operated in power systems, particularly in microgrids in the form of distributed generation units. This issue requires efficient energy management tools which take into account the inherent uncertainties of such energy resources. Thus, this paper presents a stochastic framework aimed at scheduling the renewable energy-based and thermal units in a coordinated way. The generation units comprise fuel cell units with proton exchange membrane known as PEMFC-CHP producing heat and power, concurrently. Moreover, the uncertainties arising from wind and solar power as well as market prices are characterized by deploying scenario-based optimization. The mentioned framework considers storing hydrogen and the model is presented within a stochastic mixed-integer nonlinear programming (MINLP) framework. The resulting problem is simulated on a modified 33-bus distribution network and tackled using the modified marine predators algorithm (MMPA)algorithm. The obtained results indicate that the revenue increases by more than 5% compared to other optimization algorithms. Furthermore, taking into account CHP will increase the total profit of the system by more than 15%.

Integrated risk management and maintenance planning in Oil and Gas Supply Chain operations under market uncertainty

The Oil and Gas Supply Chain (OGSC) is a multifaceted network comprising diverse activities and echelons. Instability or interruptions can cause economic fluctuations, impacting industries, markets, and consumers. Maintenance activities, which pause production but extend facilities' life, are recommended during non-peak demand periods to avoid production losses and meet customer demand. To mitigate these effects, decisions on operations planning, maintenance scheduling, and maintenance team assignments should be optimized in a risk management framework. The proposed model adopts a mixed-integer linear programming (MILP) framework and is solved via a sequential approach that incorporates the relax-and-fix (RF) heuristic in order to find a solution that is close to optimal. Subsequently, the solution serves as an initial solution for the CPLEX solver, which employs a branch-and-cut algorithm to attain the exact optimal solution. The practicality of this model has been showcased through its application to the supply chain in Saudi Arabia. The model efficiently schedules maintenance activities evenly and consistently across the OGSC plants over the planning period to reduce lost sales by keeping plants operational during high-demand periods. Furthermore, a sensitivity analysis was conducted to investigate the influence of the decision-maker's risk attitude on the outcomes that were obtained.

Gene-level alignment of single-cell trajectories

Single-cell data analysis can infer dynamic changes in cell populations, for example across time, space or in response to perturbation, thus deriving pseudotime trajectories. Current approaches comparing trajectories often use dynamic programming but are limited by assumptions such as the existence of a definitive match. Here we describe Genes2Genes, a Bayesian information-theoretic dynamic programming framework for aligning single-cell trajectories. It is able to capture sequential matches and mismatches of individual genes between a reference and query trajectory, highlighting distinct clusters of alignment patterns. Across both real world and simulated datasets, it accurately inferred alignments and demonstrated its utility in disease cell-state trajectory analysis. In a proof-of-concept application, Genes2Genes revealed that T cells differentiated in vitro match an immature in vivo state while lacking expression of genes associated with TNF signaling. This demonstrates that precise trajectory alignment can pinpoint divergence from the in vivo system, thus guiding the optimization of in vitro culture conditions.

Performance study on a hybrid solid desiccant‐vapor compression air‐conditioning system for hot‐humid ambient conditions

AbstractThe paper focuses on the simulation and testing of a hybrid solid desiccant‐vapor compression air‐conditioning system under different hot‐humid climates. The simulation is carried out by a BLUEJ programming framework. Air at the lowest achievable temperatures from a solid desiccant cooling system is supplied to a standard vapor compression air‐conditioning system (VCAS). The cooling capacities of the hybrid system under three modes 1, 2, and 3, indicating different supply air conditions to the cabin, are reported in the paper. This paper expresses the trade‐off between having comfort conditions and reasonable comfort conditions inside the cooling space with energy efficiency as the inflection point. The more the study is moved towards the point of energy efficiency, the higher the reduction in the cooling capacities of the vapor compression air conditioner. This is demonstrated by the cooling capacity savings when the system is operated in three modes namely 1, 2, and 3. The substantial energy savings provided by Mode 1, Mode 2, and Mode 3 are 71%, 57%, and 37%, respectively. If the system is made to provide substantial cooling and focuses deeply on cooling capacity savings, then the system could save up to 77% in the cooling capacity of the vapor compression air‐conditioning system. If the system is made to operate solely with the milestone of comfort cooling for the occupants with any substantial savings in the cooling capacity of the vapor compression air‐conditioning system, then Mode 3 would deliver the milestone and give savings in the cooling capacities from 32% to 41% which is a significant savings. From the results of the performance study, it is inferred that the hybrid system provides significant energy savings compared to a standard air conditioner, especially in hot‐humid ambient conditions. The solid desiccant cooling system thus establishes itself as an effective pre‐cooler unit for a VCAS. The hybrid solid desiccant air‐conditioning in the paper is analyzed from the aspects of savings in cooling capacities of an existing vapor compression air‐conditioning system and comfort cooling. This is a newer operational approach, especially for using this hybrid solid desiccant cooling system for ambient humidity ratios greater than 15 kg−1 d.a.

Community-Based Participatory Research: Lessons and Challenges. Symposium Special Communication.

Community-based participatory research is an equitable and wholesome approach that aims to respectfully collaborate with the communities that it seeks to impact. It offers everyone a seat at the table when trying to create transformative clinical, behavioral, and health services change. Oral health scientists and program implementers can apply this framework for research and programming in communities where past approaches have not necessarily benefited the peoples or their communities in an equitable manner.

Asynchronous Memory Access Unit: Exploiting Massive Parallelism for Far Memory Access

The growing memory demands of modern applications have driven the adoption of far memory technologies in data centers to provide cost-effective, high-capacity memory solutions. However, far memory presents new performance challenges because its access latencies are significantly longer and more variable than local DRAM. For applications to achieve acceptable performance on far memory, a high degree of memory-level parallelism (MLP) is needed to tolerate the long access latency. While modern out-of-order processors are capable of exploiting a certain degree of MLP, they are constrained by resource limitations and hardware complexity. The key obstacle is the synchronous memory access semantics of traditional load/store instructions, which occupy critical hardware resources for a long time. The longer far memory latencies exacerbate this limitation. This article proposes a set of Asynchronous Memory Access Instructions (AMI) and its supporting function unit, Asynchronous Memory Access Unit (AMU), inside contemporary Out-of-Order Core. AMI separates memory request issuing from response handling to reduce resource occupation. Additionally, AMU architecture supports up to several hundreds of asynchronous memory requests through re-purposing a portion of L2 Cache as scratchpad memory (SPM) to provide sufficient temporal storage. Together with a coroutine-based programming framework, this scheme can achieve significantly higher MLP for hiding far memory latencies. Evaluation with a cycle-accurate simulation shows AMI achieves 2.42× speedup on average for memory-bound benchmarks with 1μs additional far memory latency. Over 130 outstanding requests are supported with 26.86× speedup for GUPS (random access) with 5 μs latency. These demonstrate how the techniques tackle far memory performance impacts through explicit MLP expression and latency adaptation.

Art therapy as a therapeutic resource integrated into mental health programmes: Components, effects and integration pathways

To understand the effectiveness of art therapy within the framework of multidisciplinary therapeutic programs, it is essential to adopt a holistic perspective that allows for the description of effect relationships. Under this premise, and in the context of the work carried out in two Mental Health Day Hospitals in the Community of Madrid, a study has been conducted with the primary objective of identifying and analysing which conditions and/or therapeutic factors make art therapy an effective therapeutic resource. Semi-structured interviews including questions about key factors, therapeutic contributions and suggested improvements were administered to a total of 10 professionals, 5 from each hospital. The responses were recorded, transcribed, and thematically analysed. From the analysis of the interviews, 5 categories were identified: Common Factors, Specific Factors, Benefits to Patients, Contributions to Therapists and Improvements. The study concludes that art therapy is a practical and helpful treatment, particularly for narrative regulation and elaboration. It requires an interdisciplinary approach to fit into each patient’s treatment plan. Clinical debriefings with multidisciplinary clinicians have been effective and essential.

Comparative Analysis of Machine Learning Models for Enhanced Software Fault Detection: Performance and Accuracy Evaluation

Software defect prediction expectation is a critical portion of program designing that tries to discover and anticipate bugs or imperfections in program frameworks some time recently they happen. The objective is to come up with strategies and models that offer assistance program advancement groups choose which testing assignments are most imperative and how to best utilize their assets. A few machine learning (ML) strategies are utilized to do this. To create forecasts, these frameworks utilize a parcel of distinctive information, like code measures, past imperfection information, and data approximately the coder. The reason of this study is to use different machine learning strategies to make a determining demonstrate for finding computer program bugs. The proposed demonstrate is utilized in tests on the KC2 dataset from NASA's Guarantee library as portion of the study. It was attempted and compared how well diverse machine learning strategies worked. This consider utilized the KC2 dataset and found that the Decision Tree strategy got 73.28% accuracy, Naïve Bayes got 85.96%, K-Nearest Neighbour (KNN) got 82.57%, Support Vector Machine (SVM) got 86.74%, and Random Forest got 84.20%. The study about appeared that these different models can precisely anticipate program bugs when the Guarantee dataset KC2 is utilized. These comes about appear that machine learning models can offer assistance anticipate bugs superior, which can offer assistance groups speed up their tests and make way better utilize of their assets. The study appears that employing a run of machine learning models can enormously move forward the precision of foreseeing computer program abandons. This gives us valuable data almost regions of program frameworks that are more likely to have bugs, which helps keep software quality high.

Large-scale molecular dynamics simulation and aggregate behavior research on asphalt

In this comprehensive study, a molecular dynamics (MD) model consisting of approximately 150,000 atoms was developed within the Strategic Highway Research Program (SHRP) framework to investigate the behavior of AAA asphalt, focusing on asphaltene aggregate dynamics behavior over a simulation time span of 0.282 microseconds. The findings reveal that the model achieves a stable state in terms of both energy and microstructure without significant changes or the formation of densely clustered molecules, despite the extensive simulation period. Notably, the patterns of asphaltene aggregation were found to significantly influence the MD system energy, with variations ranging from loosely organized structures due to dispersed asphaltenes to more structured, energy-efficient clusters formed by encapsulated asphaltene pentamers. The size of asphaltene aggregates emerged as a pivotal factor, affecting their encapsulation and leading to distinct formation patterns. Furthermore, the research highlighted that increasing shear rates prompt the depolymerization of asphaltene aggregates, shifting from large-scale structures to smaller ones and adapting dynamically to the flow conditions by aligning parallel to the shear direction. This study underscores the critical impact of asphaltene aggregate behavior and shear rates on the structural integrity and distribution within the asphalt model, offering profound insights into the MD of asphalt materials.

Differentiable and hardware-accelerated waveforms for gravitational wave data analysis

We propose the use of automatic differentiation through the programming framework for accelerating a variety of analysis tasks throughout gravitational wave (GW) science. Firstly, we demonstrate that complete waveforms which cover the inspiral, merger, and ringdown of binary black holes (i.e., IMRPhenomD) can be written in and demonstrate that the serial evaluation speed of the waveform (and its derivative) is similar to the implementation in . Moreover, allows for graphics processing unit–accelerated waveform calls which can be over an order of magnitude faster than serial evaluation on a CPU. We then focus on three applications where efficient and differentiable waveforms are essential. Firstly, we demonstrate how gradient descent can be used to optimize the ∼200 coefficients that are used to calibrate the waveform model. In particular, we demonstrate that the typical with numerical relativity waveforms can be improved by more than 50%. Secondly, we show that Fisher forecasting calculations can be sped up by ∼3–5× (on a CPU) with no loss in accuracy. This increased speed makes Fisher forecasting for a population of events substantially simpler. Finally, we show that gradient-based samplers like Hamiltonian Monte Carlo lead to significantly reduced autocorrelation values when compared to traditional Monte Carlo methods. Since differentiable waveforms have substantial advantages for a variety of tasks throughout GW science, we propose that waveform developers use to build new waveforms moving forward. Our waveform code, , can be found on GitHub website and will continue to be updated with new waveforms as they are implemented. Published by the American Physical Society 2024

Longitudinal analysis of teacher self-efficacy evolution during a STEAM professional development program: a qualitative case study

Despite the widespread advocacy for the integration of arts and humanities (A&H) into science, technology, engineering, and mathematics (STEM) education on an international scale, teachers face numerous obstacles in practically integrating A&H into STEM teaching (IAT). To tackle the challenges, a comprehensive five-stage framework for teacher professional development programs focussed on IAT has been developed. Through the use of a qualitative case study approach, this study outlines the shifts in a participant teacher’s self-efficacy following their exposure to each stage of the framework. The data obtained from interviews and reflective analyses were analyzed using a seven-stage inductive method. The findings have substantiated the significant impact of a teacher professional development program based on the framework on teacher self-efficacy, evident in both individual performance and student outcomes observed over eighteen months. The evolution of teacher self-efficacy in IAT should be regarded as an open and multi-level system, characterized by interactions with teacher knowledge, skills and other entrenched beliefs. Building on our research findings, an enhanced model of teacher professional learning is proposed. The revised model illustrates that professional learning for STEAM teachers should be conceived as a continuous and sustainable process, characterized by the dynamic interaction among teaching performance, teacher knowledge, and teacher beliefs. The updated model further confirms the inseparable link between teacher learning and student learning within STEAM education. This study contributes to the existing body of literature on teacher self-efficacy, teacher professional learning models and the design of IAT teacher professional development programs.

Multi-instrumental analysis of ozone vertical profiles and total columns in South America: comparison between subtropical and equatorial latitudes

Abstract. The behavior of ozone gas (O3) in the atmosphere varies according to the region of the globe. Its formation occurs mainly in the tropical stratosphere through the photodissociation of molecular oxygen with the aid of the incidence of ultraviolet solar radiation. Still, the highest concentrations of O3 content are found in high-latitude regions (poles) due to the Brewer–Dobson circulation, a large-scale circulation that takes place from the tropics to the pole in the winter hemisphere. This work presents a multi-instrumental analysis at two Brazilian sites, a subtropical one (Santa Maria – 29.72° S, 53.41° W) and an equatorial one (Natal – 5.4° S, 35.4° W), to investigate ozone distributions in terms of vertical profiles (2002–2020) and total abundance in terms of total columns of ozone (1979–2020). The study is based on the use of ground-based and satellite observations. Ozone profiles over Natal, from the ground up to the mesosphere, are obtained by radiosonde experiments (0–30 km) in the framework of the SHADOZ program and by satellite measurements from the SABER instrument (15–60 km). This enabled the construction of a continuous time series for ozone, including monthly values and climatological trends. There is a good agreement between the two measurements in the common observation layer, mainly for altitudes above 20 km. Below 20 km, SABER ozone profiles showed high variability and overestimated ozone mixing ratios by over 50 %. Dynamic and photochemical effects can interfere with O3 formation and distribution along higher latitudes through the Brewer–Dobson circulation. The measurements of the total ozone columns used are in good agreement with each other (TOMS/OMI × Dobson for Natal and TOMS/OMI × Brewer for Santa Maria) in time and space, in line with previous studies for these latitudes. Wavelet analysis was used over 42 years. The investigation revealed a significant annual cycle in both data series for both sites. The study highlighted that the quasi-biennial oscillation (QBO) plays a significant role in the variability of stratospheric ozone at the two study sites – Natal and Santa Maria. The QBO's contribution was found to be stronger at the Equator (Natal) than at the subtropics (Santa Maria). Additionally, the study showed that the 11-year solar cycle also has a significant impact on ozone variability at both locations. Given the study latitudes, the ozone variations observed at the two sites showed different patterns and amounts. Only a limited number of studies have been conducted on stratospheric ozone in South America, particularly in the region between the Equator and the subtropics. The primary aim of this work is to investigate the behavior of stratospheric ozone at various altitudes and latitudes using ground-based and satellite measurements in terms of vertical profiles and total columns of ozone.

Effectiveness of demographic policy measures in addressing population decline in contemporary Russia

Demographic policy is one of the key tasks of almost any state at the present time. It correlates with the solution of pressing problems in the economic and social spheres, directly depends on the state of healthcare, education, migration policy and other factors and directly affects the socio-economic development of both individual regions and the country as a whole. Many Russian and foreign researchers believe that demographic indicators very accurately reflect the socio-economic and political situation of the state. The relevance of the study is due to the fact that for the progressive socio-economic development of any country, positive demographic dynamics are necessary. The main sign of the negative demographic situation that has developed in modern Russia and a number of countries, primarily European, is the growing scale of depopulation (population extinction). The purpose of this work was to analyze the existing demographic policy of Russia and compare demographic trends in Russia and other countries. The work uses methods of statistical data analysis, comparison of statistical indicators of fertility, mortality, natural population decline, migration, marriage rates in Russia and the Republic of Srpska, methods of retrospective analysis, research of the institutional environment created by the action of state and national programs “Demography”, “Providing accessible and comfortable housing and public services for citizens of the Russian Federation”, “Strategy of socio-economic development for the period until 2024”, Presidential decrees, etc. Research has shown that despite measures taken to overcome the demographic crisis, Russia’s population continues to decline. According to the Federal State Statistics Service of the Russian Federation (Rosstat), as of 1 January 2023, 146.45 million people lived in Russia. By 1 January 2046, according to a Rosstat forecast published in October 2023 the country’s population will decrease to 138.77 million people. To solve demographic problems in the Russian Federation, a national project “Demography” was developed and approved. The government has allocated more than 3 trillion rubles for its implementation. However, it is not possible to completely overcome the negative trend. The authors proposed a number of economic and ideological measures within the framework of agglomeration, migration, and family support policies that can be used within the framework of socio-economic development strategies and national programs aimed at overcoming the demographic crisis.

An integrated transcription factor framework for Treg identity and diversity.

Vertebrate cell identity depends on the combined activity of scores of transcription factors (TF). While TFs have often been studied in isolation, a systematic perspective on their integration has been missing. Focusing on FoxP3+ regulatory T cells (Tregs), key guardians of immune tolerance, we combined single-cell chromatin accessibility, machine learning, and high-density genetic variation, to resolve a validated framework of diverse Treg chromatin programs, each shaped by multi-TF inputs. This framework identified previously unrecognized Treg controllers (Smarcc1) and illuminated the mechanism of action of FoxP3, which amplified a pre-existing Treg identity, diversely activating or repressing distinct programs, dependent on different regulatory partners. Treg subpopulations in the colon relied variably on FoxP3, Helios+ Tregs being completely dependent, but RORγ+ Tregs largely independent. These differences were rooted in intrinsic biases decoded by the integrated framework. Moving beyond master regulators, this work unravels how overlapping TF activities coalesce into Treg identity and diversity.

A two-stage stochastic programming framework for blood product inventory management with ABO substitution and lateral transshipment

This work proposes a two-stage stochastic optimization model for managing the inventory of a two-echelon blood supply chain comprised of a blood center and a network of hospitals and transfusion points. The proposed model identifies optimal distribution quantities across the supply chain, considering demand stochasticity and product perishability. Furthermore, the model utilizes redistribution policies, such as lateral transshipment and product substitution based on ABO compatibility, as corrective actions to minimize outdates (i.e., expired product) and shortages across the network. Using a computational experiment that includes a network with one regional blood center and twenty hospitals, we show that inventory management policies that include both ABO substitution and transshipment can significantly decrease the expected total network costs. Similarly, we see corresponding reductions in the expected number of outdated units and emergency requests to external networks for additional supply to satisfy unmet demand.

Developing peace leadership: lessons from the peace practice alliance

ABSTRACT In 2020, Euphrates Institute piloted the Peace Practice Alliance (PPA), a virtual six-month program that brings together an international cohort of peacebuilders to learn peace leadership theories and develop related skills and practices. The program framework is based on integral peace leadership, which focuses on four interrelated areas of Innerwork, Knowledge, Community, and Environment. As part of an emergent field, limited research has documented the application of integral peace leadership in peacebuilder training and development programs. This paper, therefore, examines the pilot PPA program to understand the ways in which an online, global peace leadership training program can support the development of community peace leaders. Findings from interviews with 14 of the program’s first cohort of 18 peace leaders revealed that the PPA supported their development through the content of the curriculum, the creation of a strong learning community, and the dual focus on theory and practice. This paper concludes with suggestions for further improvement, including additional spaces for participant interaction and experiential learning and the creation of further training and development opportunities.

130 Promoting health enhancing physical activity through social prescribing in Wales: A delivery and recommendations framework for nature-based wellbeing support programmes

130 Promoting health enhancing physical activity through social prescribing in Wales: A delivery and recommendations framework for nature-based wellbeing support programmes

Integrated Optimal Energy Management of Multi-Microgrid Network Considering Energy Performance Index: Global Chance-Constrained Programming Framework

Distributed generation (DG) sources play a special role in the operation of active energy networks. The microgrid (MG) is known as a suitable substrate for the development and installation of DGs. However, the future of energy distribution networks will consist of more interconnected and complex MGs, called multi-microgrid (MMG) networks. Therefore, energy management in such an energy system is a major challenge for distribution network operators. This paper presents a new energy management method for the MMG network in the presence of battery storage, renewable sources, and demand response (DR) programs. To show the performance of each connected MG’s inefficient utilization of its available generation capacity, an index called unused power capacity (UPC) is defined, which indicates the availability and individual performance of each MG. The uncertainties associated with load and the power output of wind and solar sources are handled by employing the chance-constrained programming (CCP) optimization framework in the MMG energy management model. The proposed CCP ensures the safe operation of the system at the desired confidence level by involving various uncertainties in the problem while optimizing operating costs under Mixed-Integer Linear Programming (MILP). The proposed energy management model is assessed on a sample network concerning DC power flow limitations. The procured power of each MG and power exchanges at the distribution network level are investigated and discussed.

Real Time Collaborative Code Editor

Abstract: The main goal of our project is to provide an online platform especially for group coding interviews. This platform provides a comprehensive environment for seamless communication between interviewers and candidates, with the goal of addressing the challenges faced during remote technical assessments. The creation ofa feature-rich web platform is the goal of our project. Thisincludes an adaptable rich text editor that enables coding in real time, a mechanism for instantaneous code evaluation via execution and compilation, integrated video conferencing for efficient communication, and the abilityto set up interview rooms to hold several interviews at once. We're designing a user intuitive interface using technology and strong programming frameworks. The main objectives of the platform are to ensure real time code execution and a seamless interview process. This is achieved by incorporating code compilation engines implementing editing tools and utilizing WebRTC for video conferencing. By creating a user-friendly and productive environment that benefits both interviewers and candidates, our platform seeks to expedite the remote technical interview process. This means reducing logistical obstacles, making thorough coding evaluations possible, and creating a positive interview environment.