Practical Constraints Research Articles

A techno-economic framework for optimizing multi-area power dispatch in microgrids with tie-line constraints

Fuel-fired power plants are the primary electricity sources in Saudi Arabia. This investigation aims to reduce the overall costs and emissions of electricity production from thermal units operated by the Shuqaiq Water and Electricity Company. It also considers practical thermoelectric constraints, such as power losses, ramp-rate constraints, and environmental optimization functions, to align with the sustainability goals of Saudi Vision 2030. Optimizing electric energy delivery is crucial within an energy control center. The focus of this work is to develop an advanced computational technique that reduces reliance on fossil fuels and minimizes the production cost of electricity, while also considering environmental emissions as an optimization function. An examination was conducted through two distinct practical case studies with power demand of 850 MW—one involving renewable sources and the other without—to assess the effectiveness of the hybrid crow search and JAYA optimization algorithm in optimizing performance. This system includes a multi-area setup with limitations on the tie-line power flow constraints. Additionally, the study also involved RETScreen modelling to assess the practical financial, emission, and benchmark analysis of a proposed integrated solar plant. This analyses offers a thorough assessment of the facility’s performance and reliability in practical situations. This study serves as a motivating factor for energy operators, stakeholders, governmental organizations, and grid administrators to invest resources in environmentally-friendly initiatives. The simulation results showed that the proposed approach successfully reduced the electricity cost by 2.29% and 3.70% in hybrid energy networks while effectively handling the system constraints. In addition, the analysis revealed that the optimization algorithm effectively decreased toxic emissions from power plants while also supporting the sustainability objectives of United Nations SDG-7 and Saudi Vision 2030 through the successful integration of renewable sources.

Retention-aware zero-shifting technique for Tiki-Taka algorithm-based analog deep learning accelerator.

We present the fabrication of 4 K-scale electrochemical random-access memory (ECRAM) cross-point arrays for analog neural network training accelerator and an electrical characteristic of an 8 × 8 ECRAM array with a 100% yield, showing excellent switching characteristics, low cycle-to-cycle, and device-to-device variations. Leveraging the advances of the ECRAM array, we showcase its efficacy in neural network training using the Tiki-Taka version 2 algorithm (TTv2) tailored for non-ideal analog memory devices. Through an experimental study using ECRAM devices, we investigate the influence of retention characteristics on the training performance of TTv2, revealing that the relative location of the retention convergence point critically determines the available weight range and, consequently, affects the training accuracy. We propose a retention-aware zero-shifting technique designed to optimize neural network training performance, particularly in scenarios involving cross-point devices with limited retention times. This technique ensures robust and efficient analog neural network training despite the practical constraints posed by analog cross-point devices.

Barriers to Practicing Values: Challenges Encountered by Values Education Students in Applying Values Education to Their Everyday Lives

The study focuses on the challenges faced by Values Education students in applying their learnings to daily life, emphasizing the importance of values education in character and moral development. The key questions explore the barriers in integrating values education into daily life, factors affecting the application of values education concepts, and coping mechanisms used by students. A phenomenological research design was employed, focusing on semi-structured interviews with tertiary-level students majoring in Values Education at Leyte Normal University. A convenience sampling was used with a sample size of 10 students. Finding shows that students face challenges such as socio-economic pressures, peer influence, and personal biases that hinder the application of values education in their everyday lives. Key challenges identified include internal conflicts, societal norms, peer expectations, and practical constraints that hinder the consistent application of values. Specific values such as courage, commitment, respect, empathy, patience, resilience, optimism, and determination were found to be particularly difficult to integrate into daily routines. The study suggests the need for supportive educational environments, experiential learning opportunities, to better equip students to overcome barriers and effectively apply the values learned in school in various life contexts.

Introducing Green, Eco-Friendly Practices and Circular Economy Principles in Vocational Education Through a Novel Analysis-Synthesis Method: Design, Implementation and Evaluation

Purpose: A novel, Project- and Collaborative Learning-based educational method is proposed, implemented and evaluated in this paper. The aim is to exploit hands-on laboratory modules in Vocational Education in order to experientially introduce students to green, eco-friendly practices and the principles of sustainability and circular economy. Besides their apparent individual and social benefits, such knowledge and skills are also expected to raise qualifications and employability of Vocational Education graduates. Methods: The proposed method is tested through a quasi-experimental methodology, via an educational intervention with a class of Vocational Lyceum students, in the field of Electrical and Electronics Engineering. The learning content focuses on the reclaiming and reuse of operational components from damaged electrical/electronic equipment at end-of-life stage. Through repeated Analysis and Synthesis phases, students learn to extract, measure, classify and reuse operational components either to repair similar equipment or to design and construct novel devices. Results: Evaluation is carried out via closed and open type activities as well as by observation sheets of the teacher. Learning outcomes are evaluated through knowledge post-tests of the closed type while social/emotional outcomes are evaluated through questionnaires. Evaluation results indicate that the proposed method does produce cognitive and social/emotional skills gains for the students. The development of metacognitive skills and the stimulation of imagination and innovative thinking in the students is also observed by the teacher, but not formally evaluated due to practical constraints. Conclusions: The proposed method is implemented and pilot-tested with positive results both as to the cognitive and as to the social/emotional domain – yet, these results are of an indicative value, due to the limited scale of the educational intervention. Future research is necessary in order to evaluate the proposed method in extent and possibly compare results across education grades or engineering fields, as the method is generic enough to be easily adaptable for different ages/grades and engineering/technical fields of study.

Landholder perceptions and attitudes towards the rewilding of private land: an analysis from Surrey, UK

ABSTRACT Rewilding is a conservation concept and practice which has gained traction over the past two decades, and is often perceived as a powerful tool to reverse anthropogenic ecological degradation. In the UK, Environmental Land Management Schemes (ELMS) have introduced the idea of ‘public money for public goods’, making some rewilding initiatives more viable for landholders. Many conservation organisations work with landholders to promote rewilding as part of local conservation strategies, but there remains a lack of understanding of landholder perceptions and attitudes towards rewilding. This research used semi-structured interviews based on the gaps identified in the literature to explore these aspects in a sample population of 8 landholders in Surrey, UK. Thematic analysis interpreted the importance of emergent patterns and implications in relation to existing literature. This research found that landholders associate many different meanings towards rewilding. These perceptions fall along two spectra, ranging from passive to active forms of rewilding and with different levels of impact on human activities. Landholder valuations of rewilding are profoundly influenced by their perceptions of its meaning, with more favourable attitudes expressed towards ‘low-impact’, active forms of rewilding. Concern was expressed about the need to balance rewilding goals with food security. This emphasises that understanding local views is essential to improve consideration of practical constraints, whilst helping to reduce polarisation and mistrust about rewilding. Conservation organisations should facilitate collaboration among landholders to kickstart the implementation of acceptable and context-specific forms of rewilding, playing a key role in achieving local and national nature recovery targets.

Localization of Coordinated Cyber-Physical Attacks in Power Grids Using Moving Target Defense and Machine Learning

Coordinated cyber-physical attacks (CCPAs) are dangerously stealthy and have considerable destructive effects against power grids. The problem of stealthy CCPA (SCCPA) localization, specifically identifying disconnected lines in attack, is a nonlinear multi-classification problem. To the best of our knowledge, only one paper has studied the problem; nevertheless, the total number of classifications is not appropriate. In the paper, we propose several methods to solve the problem of SCCPA localization. Firstly, considering the practical constraints and abiding by one of our previous studies, we elaborately determine the total number of classifications and design an approach for generating training and testing datasets. Secondly, we develop two algorithms to solve multiple classifications via the support vector machine (SVM) and random forest (RF), respectively. Similarly, we also present a one-dimensional convolutional neural network (1D-CNN) architecture. Finally, extensive simulations are carried out for IEEE 14-bus, 30-bus, and 118-bus power system, respectively, and we verify the effectiveness of our approaches in solving the problem of SCCPA localization.

A simple accurate method for concentration-QTc analysis in preclinical animal models

IntroductionIn preclinical cardiovascular safety pharmacology studies, statistical analysis of the rate corrected QT interval (QTc) is the focus for predicting QTc interval changes in the clinic. Modeling of a concentration/QTc relationship, common clinically, is limited due to minimal pharmacokinetic (PK) data in nonclinical testing. It is possible, however, to relate the average drug plasma concentration from sparse PK samples over specific times to the mean corrected QTc. We hypothesize that averaging drug plasma concentration and the QTc-rate relationship over time provides a simple, accurate concentration-QTc relationship bridging statistical and concentration/QTc modeling. MethodsCardiovascular telemetry studies were conducted in non-human primates (NHP; n = 48) and canines (n = 8). Pharmacokinetic samples were collected on separate study days in both species. Average plasma concentrations for specific intervals (CAverage0-X) were calculated for moxifloxacin in canines and NHP using times corresponding to super-intervals for the QTc data statistical analysis. The QTc effect was calculated for each super-interval using a linear regression correction incorporating QT and HR data from the whole super-interval. The concentration QTc effects were then modeled. ResultsIn NHP, a 10.9 ± 0.06 ms (mean ± 95% CI) change in QTc was detected at approximately 1.5× the moxifloxacin plasma concentration that causes a 10 ms QTc change in humans, based on a 0-24 h super-interval. When simulating a drug without QT effects, mock, no effect on QTc was detected at up to 3× the clinical concentration. Similarly, in canines, a 16.6 ± 0.1 ms change was detected at 1.7× critical clinical moxifloxacin concentration, and a 0.04 ± 0.1 ms change was seen for mock. ConclusionsWhile simultaneous PK and QTc data points are preferred, practical constraints and the need for QTc averaging did not prevent concentration-QTc analyses. Utilizing a 0-24 h super-interval method illustrates a simple and effective method to address cardiovascular questions when preclinical drug exposures exceed clinical concentrations.

We must discuss research environments.

A major challenge facing the biomedical community is creating and sustaining high-quality research environments. A literature search identified five common themes underlying biomedical research environments comprising collaboration, data access, user-led innovation, data provenance and a deep commitment to public and scientific benefit. Club theory is used to develop a model describing social structures that underpin these themes. It is argued that collaboration underlies impactful science and that collaboration is hindered by high transaction costs. This, combined with poorly defined property rights surrounding publicly funded data, limits the ability of data markets to operate efficiently. Although the science community is best placed to provide solutions for these issues, incentivization by funding agencies to increase the benefits of collaboration and reduce uncoordinated activity will be an accelerator. Given the complexity of emerging datasets and the collaborations needed to exploit them, trust-by-design solutions are suggested. The underlying motivational 'glue' that holds this activity together is the aesthetic and ethical value base underlying good science. The model has implications for data-driven science more generally. As biomedical science in the Global South develops, there is an opportunity to address foundational structural issues prospectively rather than inherit unwanted constraints of current practice.

Sustainability insights on learning-based approaches in precision agriculture in internet-of-things

Precision agriculture (PA) is meant to automate the complete agricultural processes with the sole target of enhanced crop yield with reduced cost of operation. However, deployment of PA in internet of things (IoT) based architecture demands solutions towards addressing various challenges where most are related to proper and precise predictive management of agricultural data. In this perspective, it is noted that learning-based approaches have made some contributory success towards addressing different variants of issues in PA; however, such methods suffer from certain loopholes, primarily related to the non-inclusion of practical constraints of IoT infrastructure in PA and lack of emphasis towards bridging the trade-off between higher accuracy and computational burden that is eventually associated with this. This paper contributes towards highlighting the strengths and weaknesses of recent learning approaches and contributes towards novel findings.

Saudi physiotherapists' attitudes, knowledge, behaviors, and barriers towards evidence-based practice: A cross-sectional study

Background and objective: At present, there is limited knowledge about the determinants of evidence-based practice (EBP) among Saudi physiotherapists. So, this study investigated the attitudes, knowledge, behaviors and perceived barriers of Saudi physiotherapists regarding EBP. Methods: A sample of 323 physiotherapists completed an EBP questionnaire, which was designed to cover six domains of EBP; Attitudes and beliefs, interest, attention to literature, information availability, educational background, and its barriers. Results: The participants concurred that EBP is essential (91%), literature helps practice (75%). EBP enhances patient care (80%), reimbursement rates (82%), and aids in decision-making (75%). They discovered that EBP does not consider patient preferences (37%) or the constraints of the clinical practice (71%). They were interested in enhancing their abilities (80%). They needed to use evidence more frequently in daily work (74%). Seventy-six percent having access to professional publications, and 80% have online access to EBP guidelines. Seventy-one percent disagree that their workplace encouraged the EBP usage. Fifty-one percent had not learned the EBP's fundamentals. Sixty percent had not attended training sessions on finding research, 75% were not confident in their capacity to evaluate professional literature, and locate pertinent research to address clinical concerns (62%). Conclusion: The Saudi physiotherapists have a generally positive attitude toward EBP and are interested in increasing its usage in their practices. However, their attention to literature was relatively low. They need for training sessions for using data base. The lack of time was the most important barrier to EBP.

In Pursuit of Innocence: A Study of Race and Ethnicity Differences in Time-to-Exoneration

The purpose of this study is to examine the impact of race and ethnicity on time-to-exoneration through the lens of focal concerns theory. Focal concerns theory has been used to demonstrate that criminal justice actors are influenced by legal and extralegal factors in decision making and rely on stereotypes to assess blameworthiness, protection of the community, and in navigating practical constraints and consequences. Utilizing data obtained from the National Registry of Exonerations (N =507) survival analysis was performed. The findings indicate that Black exonerees experienced a longer time-to-exoneration than did White exonerees and that Hispanic exonerees experienced the shortest time-to-exoneration of all. The findings offer support for focal concerns theory in the demonstration that racial and ethnic differences are present in time-to-exoneration resulting in disparities which disadvantage minorities. Further support for focal concerns theory is found in that the legal components of a case are shown to be associated with racial and ethnic differences in time-to-exoneration.

Role of Community Empowerment in the Sustainable Cultural Heritage Management: A Review

The study's objective is to examine the role of cultural heritage in socially sustainable development and, through the descriptive analysis, provide a comprehensive understating concerning cultural heritage management, social empowerment, and sustainable development. The study highlights the importance of long-term planning, community engagement, and strengthening partnerships in developing sustainable cultural heritage management. Quality integration with economic, social-cultural, and environmental values is essential for further sustainable cultural heritage conservation. The descriptive analysis utilized in the study provides the theoretical understanding of community empowerment in the cultural heritage and its positive effects, such as social cohesion, well-being, and conflict elimination. These contributions represent the prerequisite of socially sustainable development. The documentary evidence analysis demonstrates the essence of community engagement in cultural heritage planning. Municipality document analysis reveals constraints of cultural heritage practice, particularly community participation organization in cultural heritage management. Distinct authors highlight the core contributors to successful community involvement in sustainable cultural tourism development: Knowing sustainable development principles, cultural heritage potential, and possible positive and negative implications of tourism. Practical cooperation between governmental, non-governmental, and educational organizations generates the prerequisite for effective cultural heritage management. The national strategy document for cultural heritage highlights the importance of social inclusivity in heritage planning and management. However, community participation in the process needs improvement, particularly in the context of Akhmeta municipality.

Twin-system recurrent reinforcement learning for optimizing portfolio strategy

Portfolio management is important for sequential investment decisions in response to fluctuating financial markets. As portfolio management can be formulated as a sequential decision-making problem, it has been addressed using reinforcement learning in recent years. However, reinforcement learning methods face challenges in addressing portfolio management problems considering practical constraints. To overcome the limitations, this study proposes a twin-system approach, establishing a tractable twin that mirrors the original problem but with more manageable constraints and system dynamics. Once an optimized portfolio strategy is achieved within the tractable twin, the proposed mapping function translates it back to the original problem, ensuring the retention of optimized performance. Unlike the previous study, the proposed recurrent reinforcement learning method optimizes the portfolio strategy for a single agent managing all candidate assets. This method allows for comprehensive investment decisions by incorporating the features of candidate assets, leading to a more globally optimized portfolio strategy. Experimental studies demonstrate that the proposed method consistently outperforms benchmark strategies on the US sector and foreign exchange portfolios.

Biocompatible and Implantable Hydrogel Optical Waveguide with Lens‐Microneedles for Enhancing Light Delivery in Photodynamic Therapy

The finite penetration depth of light in biological tissues is a practical constraint in light‐induced therapies, such as antimicrobial light therapy, photothermal therapy, and photodynamic cancer therapy. Herein, a biocompatible and implantable device, termed hydrogel planar waveguide with lens‐microneedles, for light delivery in deep tissue is demonstrated. The prototype device, integrated planar waveguide and lens‐microneedles, is fabricated by press‐molding polyethylene glycol diacrylate polymers. The optical beams through the lens‐microneedles are focused at a specific point to realize the optimal intensity profile in the tissue. The adequate treatment depth and region for the hydrogel planar waveguide with five lens‐microneedles are extended to 24 mm and 3.1 cm2. The photoswitchable chemotherapeutic against colorectal cancer cells is switched by using different hydrogel waveguides. The performances of hydrogel‐waveguide‐enabled photoswitching are characterized by the dose responses from the optical microscope, crystal violet staining, and MTT assays. The anticancer drug activated by the hydrogel planar waveguide with five lens‐microneedles is shown to be twice as effective as the other fibers and waveguides in causing cancer cell death. The proposed biodegradable waveguide can be utilized for long‐term light delivery and does not require to be removed as it is gradually resorbed by the tissue. The results point to a new paradigm for widespread use in photomedicine.

Optimizing LED array irradiance uniformity with a particle swarm optimization-based scheme: application in a parallel photoreactor

This study proposes a particle swarm optimization (PSO)-based method for enhancing the irradiance uniformity of light emitting diode (LED) arrays in parallel photoreactors. We construct a spatial irradiance model, validating its accuracy with near-field optical test data and optical simulation results. An evaluation function for irradiance uniformity guides the iterative optimization of the LED arrangement, taking into consideration practical constraints such as chip size, heat dissipation, and circuit board wiring. The optimized LED array demonstrated a significant improvement in irradiance uniformity, increasing from 75.27% to 95.61%. This showcases the feasibility and effectiveness of our PSO-based method for practical applications in optimizing LED arrays.

Phase Noise in Real‐World Twin‐Field Quantum Key Distribution

AbstractThe impact of noise sources in real‐world implementations of twin‐field quantum key distribution (TF‐QKD) protocols is investigated, focusing on phase noise from photon sources and connecting fibers. This work emphasizes the role of laser quality, network topology, fiber length, arm balance, and detector performance in determining key rates. Remarkably, it reveals that the leading TF‐QKD protocols are similarly affected by phase noise despite different mechanisms. This study demonstrates duty cycle improvements of over a factor of two through narrow‐linewidth lasers and phase‐control techniques, highlighting the potential synergy with high‐precision time and frequency distribution services. Ultrastable lasers, evolving toward integration and miniaturization, offer promising solutions for agile TF‐QKD implementations on existing networks. Properly addressing phase noise and practical constraints allows for consistent key rate predictions, protocol selection, and layout design, crucial for establishing secure long‐haul links for the quantum communication infrastructures under development in several countries.

Opening up openings: Zooming in on improvisation in the Theater of Home

This article presents a qualitative analysis of the opening section of an online improvisation session. The session, which was organized by the Glasgow Improvisers Orchestra, included an international group of musicians. It took place during the global COVID-19 pandemic where the participants were experiencing lockdown conditions. Phenomenological reflexive analysis and video elicitation techniques were utilized to develop a number of key themes related to the multimodal improvisation strategies identified as emergent in the session. The results highlight how technical, physical, and psychological constraints of online practice can facilitate new creative insights and approaches to improvisation. Particular emphasis is placed upon how an improvisation begins and the role of distributed and collaborative creativity within the overall process. The importance of the domestic environment, what we term The Theater of Home, is central to these new ideas, as is how particular scenarios/items function as psychological and creative boundary objects. The spontaneous multimodal integration of text, visual, and audio material within the domestic and virtual environment can be seen to support a new type of creative collaboration and one that draws out features of social improvisation.

Economic modelling and optimisation of SPRT-based quality control schemes for individual observations

In many industrial processes, it may not be practical to obtain sample sizes larger than one due to various reasons. As a result, practitioners must rely on control charts based on individual observations for statistical process monitoring and control. The sequential probability ratio test (SPRT) chart is an appropriate solution for such situations. Existing literature predominantly delves into the statistical aspects of designing or optimising an SPRT chart, neglecting the economic and associated design aspects. However, past experience suggests that statistical design may not always be optimal from an economic perspective. This paper aims to bridge this research gap by proposing a two-stage Markov chain approach to develop an economic model for the optimal design of the SPRT chart with individual observations. Following the principles of economic-statistical designs, optimal parameters are determined by solving a mixed-integer non-linear programming problem that minimises the long-run cost per item while ensuring compliance with practical constraints. Through an extensive study, we find that the economic performance of the SPRT chart surpasses that of competing individual control charts in many cases. This indicates that the economic-statistical design of the SPRT chart can yield significant economic benefits while maintaining desirable statistical properties. Moreover, enhancements have been introduced to refine the optimal design of the SPRT chart, retaining excellent overall performance across a wide range of shifts. Finally, we provide two industrial examples to illustrate the effectiveness of the proposed SPRT chart.

Double jeopardy for families of prisoners in Kashmir: A focus on prison visiting experiences

This analysis delves into the challenges faced by women prisoners’ families in Kashmir during the prison visitation process. Drawing on 51 interviews with incarcerated women and their families, this article elucidates the difficulties in establishing and maintaining communication with the prisoners. Despite the pivotal role families play in the visitation process and rehabilitation of prisoners, there is a notable absence of consideration of the challenges from the familial perspective in extant discourse. In the context of Kashmir, the visitation process is intricately complicated, as logistical and political challenges intertwine to add layers of complexity to the procedure. Further, there exists negligible research that considers the perspective of families visiting their loved ones in prison. The article explores the double jeopardy families face due to financial strain, long travel distances, strict prison procedures, lockdowns due to the pandemic and political instability. The analysis illustrates the gendered complexities involved in sustaining connections with women prisoners amid practical and emotional constraints.

Maize leaf disease image enhancement algorithm using TFEGAN

Maize leaf diseases have a profound impact on maize crop yield. However, due to practical constraints, gathering a substantial number of maize disease images is time-consuming and labor-intensive. Furthermore, low-light conditions and noise interference in maize leaf disease images present significant challenges for image generation. Therefore, this paper proposes TFEGAN, a novel algorithm designed to generate maize leaf disease images. TFEGAN leverages a Two-Way Extraction Module (TWEM) to enhance the perceptual domain of the model and effectively reuse maize leaf disease features. The algorithm employs two well-trained Generative Adversarial Networks (GANs) to enhance low-light and noisy maize leaf disease images. The low-light enhancement component utilizes a double discriminator and self-regularization to balance global and local enhancement while constraining the distance between the input image and the denoising component. Moreover, the denoising part employs a strong coordinated attention mechanism and an environmental encoder to learn noise regions and their surrounding structures, thus generating better local images and enabling more accurate evaluation by the discriminant network. The generators of both networks are encapsulated by two discriminators and combined with a bi-directional feature extraction structure to generate normal maize leaf disease images. Experimental evaluations demonstrate the effectiveness of TFEGAN in improving the quality of maize leaf disease images under low-light and noisy conditions, showcasing its potential for practical applications. Overall, this paper presents a promising solution to the challenges of low-light and noise interference in maize leaf disease images, utilizing bidirectional feature extraction and GANs.