Processing Demands Research Articles

Machine learning methods as auxiliary tool for effective mathematics teaching

AbstractSeeing mathematics teaching as a very demanding and responsible process while having in mind the importance of mathematical knowledge for students of technical faculties, this paper aims to present heuristics for student classification according to their predicted mathematical success. Over the last few decades, the process of informatization of universities has resulted in new challenges universities are faced with. Due to the widespread use of educational databases, which opens new possibilities for educational data mining and analyses, machine learning algorithms have become a very popular tool for predicting students' academic performance. The decision tree algorithm is used in this paper for the classification and prediction of students' mathematical performance and it is trained on the data collected from the educational information system. The experimental results show that the model accuracy is 72% with an error rate of 0.28. The implementation of the Decision Tree Model to predict whether a student will pass, fail or be conditional in mathematical courses is important for both teachers and students, as well as for universities. Students' performance is one of the major keys in evaluating the quality of the teaching process, but also for evaluating the overall success of the university itself. As mathematics is considered a basic and important discipline, it is clear why predicting students' mathematical achievement is crucial for all levels of university organization.

Printed polymer platform empowering machine-assisted chemical synthesis in stacked droplets

Efficiently exploring organic molecules through multi-step processes demands a transition from conventional laboratory synthesis to automated systems. Existing platforms for machine-assistant synthetic workflows compatible with multiple liquid-phases require substantial engineering investments for setup, thereby hindering quick customization and throughput increasement. Here we present a droplet-based chip that facilitates the self-organization of various liquid phases into stacked layers for conducting chemical transformations. The chip’s precision polymer printing capability, enabled by digital micromirror device (DMD)-maskless photolithography and dual post-chemical modifications, allows it to create customized, sub-10 µm featured patterns to confine diverse liquids, regardless of density, within each droplet. The robustness and open design of surface-templated liquid layers actualize machine-assistant droplet manipulation, synchronous reaction triggering, local oscillation, and real-time monitoring of individual layers into a reality. We propose that, with further integration of machine operation line and self-learning, this droplet-based platform holds the potential to become a valuable addition to the toolkit of chemistry process, operating autonomously and with high-throughput.

Unlocking Potential: Assessing Motivation, Learning Strategies, and Cognitive Load in Flipped Learning during Online Teaching in Higher Education

Flipped learning has become the focus in many educational contexts especially with the integration of technological tools in learning space for the last decade. In flipped learning, learners perform surface learning at home, whereas deep learning such as problem-solving, critical thinking exercises etc. is covered in the classroom which potentially increases motivation and satisfaction of learners. Cognitive load, the assumed load of the working memory caused by the processing of tasks, has also attracted attention in online learning settings which pose different processing demands. This study took place in the extraordinary settings of education during the Covid-19 pandemic which required online education for all education levels. A fourth-year course of an ELT program was delivered through a flipped learning approach during online education. Learners’ motivation, use of learning strategies, satisfaction and cognitive load levels were calculated at the end of the intervention and their perceptions regarding the experience were investigated through an open-ended questionnaire. The results showed that their motivation and satisfaction levels were quite high as well as their strategy uses whereas cognitive load levels were comparatively low. Inferential statistics showed that relationships exist between and among the motivation, learning strategies and cognitive load at various levels. Motivation emerged to be a significant predictor of course satisfaction whereas time spent for the flipped lectures predicted course achievement. Learners’ perceptions of flipped learning process were generally positive while they also articulated some drawbacks of it like instructional tools-related and individual learner-related problems.

Assessment of the economic viability, environmental, and social impacts of green hydrogen production: an Algerian case study

The impacts of climate change are real and in many parts of the world testify to its harsh reality, including rampant extreme weather events, droughts, heat, wildfires, and flooding which have recorded in places which have not experienced them in recent memory. In the quest to avert such events, there is a growing awareness and demand for sustainable processes and operations. Today, sustainability encompasses a balance between ecological footprint and human development index, taking into consideration economics, the green environment, safety, quality, ethics, diversity and inclusion (D&I), and communities. This article presents some steps that have been taken by Algeria to balance energetic autonomy and sustainable development, and a case study on green hydrogen production employing membrane processes. Algeria’s objective to join the global fight against climate change is to develop its green hydrogen base. Given its resources, including available solar and wind power, seawater desalination plants, building capacity, and its favorable location, it is developing its green hydrogen economy to supply hydrogen, especially to Europe. This presents an opportunity for other developing nations, especially in Africa, to gain from this experience.

Optimization of milling parameters based on GA-BP neural network

Abstract Since the selection of machining parameters usually relies on the experience of workers or traditional calculation formulas, the traditional prediction method has the disadvantages of a complicated arithmetic process, large prediction deviation, and high consumption cost, making it challenging to meet the increasing demand of production and processing. Therefore, this paper proposes a machining quality prediction model based on the GA-BP neural network. Through experiments, it verifies the data-fitting ability of the prediction model and then takes the prediction model as the optimization objective, culminating in a multi-objective optimization model for process parameters based on the NSGA-II algorithm. Experiments demonstrate that the cutting force and surface roughness obtained by the optimization model are 3.6% and 10.0% lower than those obtained by the empirical parameters, respectively, leading to reductions of 3.6% and 10.6%, which verified the optimization effect of the model.

Inspecting Hard Facing Doesn’t Have to Be Hard: PCI of Chrome Carbide Overlay

Mining equipment needs to be tough to withstand the harsh demands of the mining process. During operation, many mining extraction and processing components are subject to abrasion, often compounded by corrosion. To shield surfaces from wear, they can be protected with a welded layer called hard facing. Chrome carbide overlay (CCO) is a go-to hard-facing solution because of its wear resistance and affordability compared with other overlay options such as tungsten. Regardless of the durability of mining assets, proactive inspection and maintenance of parts that endure the most wear and tear is essential to extend the life of equipment. Ultrasonic testing (UT) is a favored method for this, as it offers a portable nondestructive testing (NDT) option for inspecting CCO and hard facing. Recent advancements in UT technology have produced a technique that has the potential to solve some challenges associated with this inspection application.

Scaling Up Perovskite Solar Cell Fabrication: Antisolvent‐Controlled Crystallization of Printed Perovskite Semiconductor

Scaling up perovskite solar cells stands as one of the frontiers in advancing this rapidly growing technology. Yet, controlling perovskite thin‐film crystallization during and post‐printing differs significantly from lab‐scale processes that have yielded record device efficiencies. This study investigates antisolvent treatment for slot‐die‐coated perovskite solar cells using in situ optical spectroscopy and comparing among multiple antisolvents. The antisolvent bath used in slot‐die coating affects the perovskite crystallization and film quality differently when comparing to the established spin‐coating antisolvent treatment process. A novel dynamic antisolvent method, employing either vortex or laminar flow, is developed. It outperforms steady‐bath techniques in generating high‐quality, haze‐free films. Optimization studies identify critical treatment times. Implementing this novel antisolvent treatment leads to a peak average power conversion efficiency of 15.62% and the highest device efficiency of 18.57%, an excellent performance for slot‐die‐coated MAPbI3 devices printed and tested under ambient conditions. The method is validated for an alternative perovskite composition, FA0.9Cs0.1PbI3, and printing technique, blade coating. This research highlights the importance of in situ analysis for enhancing perovskite film quality and introduces scalable approaches for controlling large‐area film crystallization kinetics, driven by the demand for efficient and scalable manufacturing processes in the field of perovskite solar cells.

Fabricating hydrothermally robust ceramic membranes from amorphous yttrium-doped SiO2-ZrO2 composites

SiO2-ZrO2 ceramic composites are necessary for highly demanding separation processes. We demonstrated the efficacy of using yttrium doping to improve the hydrothermal stability of SiO2-ZrO2 composite-derived membranes. In long-term hydrothermal stability tests involving steam-N2 mixture permeation between 500–800 °C, N2 permeance for an undoped SiO2-ZrO2 membrane fabricated on an α-Al2O3 substrate decreased from 5.0 × 10−6 to 2.5 × 10−6 mol m−2 s−1 Pa−1 after 60 h between 500–700 °C and to 3.2 × 10−8 mol m−2 s−1 Pa−1 after 32 h at 800 °C. The yttrium-doped SiO2-ZrO2 membrane achieved a stable N2 permeance of 6.5 × 10−6 mol m−2 s−1 Pa−1 after 60 h between 500–700 °C, which decreased to 8.0 × 10−7 mol m−2 s−1 Pa−1 after 32 h at 800 °C. Microstructural analyses revealed that integrating yttrium into a SiO2-ZrO2 matrix suppressed segregation and aggregation rates of ZrO2 nanocrystals to form more stable microstructures.

Ditching the Queue: Optimizing Coprocessor Utilization with Out-of-Order CPUs on Compact Systems on Chip

The growing demand for high-performance and energy-efficient processing in edge-oriented Systems-on-Chip is driving the adoption of dedicated integrated circuits that accelerate computationally intensive workloads. To minimize area and performance overhead, low-power, general-purpose CPUs are often tightly coupled with domain-specific coprocessors implementing custom instructions, thereby delivering higher throughput and reduced memory traffic. However, commonly used in-order CPUs are not optimized for instruction-level parallelism, leading to stalls in the instruction stream while waiting for long-latency coprocessor operations and under-utilization of the coprocessor while executing other instructions. This work investigates the benefits of replacing simple in-order cores with a more complex out-of-order architecture to dynamically schedule instructions for the main core and coprocessor, optimizing resource utilization and reducing execution time. To ensure generality, an in-depth analysis was carried out by offloading instructions to a custom dummy coprocessor capable of emulating iterative and pipelined operations with arbitrary latency. Various workloads simulating real-world applications were executed on two variants of an open-source microcontroller equipped with a recent out-of-order core and the state-of-the-art CV32E40X in-order core, respectively. Results from Register Transfer Level simulations show that the former configuration executes up to 60% more instructions per cycle, with a modest 12% system area overhead on a 65 nm CMOS technology node.

Mental health and humanitarian crisis: Moral stress in trauma therapy.

This articleoffers a narrative analysis of the contributing factors of moral distress (MD) and moral injury (MI) among mental health clinicians working amidst humanitarian crises. It discusses the impact of moral stress on therapeutic relationships in mental health trauma. The articleoriginated from the author's experience developing a peer-to-peer support program at a nongovernmental organization (NGO) and conducting peer-to-peer support for mental health clinicians and healthcare providers in Ukraine and Turkey. A significant amount of literature has documented the detrimental effects of MD and MI on mental health, job sustainability, and resilience of healthcare providers and first responders. The negative effects of MD and MI are particularly relevant in trauma counseling, where clinicians must draw upon the use of self to develop therapeutic relationships with their clients. This process demands a high level of moral reasoning and self-awareness, which can be severely tested under the morally challenging conditions of a humanitarian crisis. There is an imperative need to deepen our understanding and to swiftly address the factors that precipitate MD and MI in mental health clinicians working in crisis zones. By doing so, we aim to bolster their resilience and the enduring nature of their commitment to help and save others. This, in turn, will not only contribute to saving more lives but also enable those who are affected by trauma to flourish in the aftermath of their experiences.

Governance and obstacles in the reception and social and labour integration of asylum seekers in Spain

Changes in migratory flows have transformed Spain from a country of emigration to one of the main destinations for international migration. And it has also become one of the most important European countries in the reception of asylum seekers and refugees in the last decade. With an unprepared reception system, the State has faced this challenge by decentralising socio-labour integration responsibilities to third sector entities. This article aims to analyse the institutional approach to the management and integration of asylum seekers, studying changes in the management of the reception and integration system for this group. Through documentary analysis and descriptive statistics, we observe a greater streamlining of certain processes, particularly after the arrival of asylum seekers from Ukraine in 2022. However, structural deficiencies remain, hindering the processes of matching labour supply and demand.

Qutrit representation of quantum images: new quantum ternary circuit design

Quantum computation is growing in significance and proving to be a powerful tool in meeting the high real-time computational demands of classical digital image processing. However, extensive research has been done on quantum image processing, mainly rooted in binary quantum systems. In this paper, we propose a new quantum ternary image circuit based on the analysis of the existing qutrit representation of quantum images. The proposed design utilizes ternary shift gates and ternary Muthukrishnan–Stroud gates, with the belief that this circuit can be used for ternary quantum image processing. This study makes a significant improvement compared to the existing counterpart in terms of quantum cost, the number of constant inputs, and garbage outputs, which are all essential parameters in quantum circuit design.

All-Optical Control of Ultrafast Switching between the Hybridized Plasmonic Fields of Au Nanorod Dimer in fs-nm Scale with Dispersed Femtosecond Laser.

With the increasing demand for ultrafast communication and information processing in future optical chips, arbitrary manipulation of electromagnetic fields in the femtosecond-nanometer spatiotemporal scale has attracted great attention in integrated optics. Manipulation of the nanoscale light field in the real femtosecond temporal domain is challenging work. Here, we have demonstrated all-optical control of ultrafast switching between the hybridized plasmonic fields of a Au nanorod dimer in the fs-nm scale using a dispersed femtosecond laser and revealed the transformation process with ultrahigh spatiotemporal resolved technology via the combination of a pump-probe technique and photoemission electron microscopy (PEEM). The results show that we can actively and coherently control the transformation sequence and time (with the shortest temporal interval of around 15 fs) of the two hybridized modes in the Au nanorod dimer by tuning the dispersion of the laser pulse. The nanoscale light manipulation achieved by all-optical control may contribute to the design of high-speed miniaturized signal-processing systems.

Memory Retention and Cultural Resonance: Exploring the Impact of Ephemeral Digital Narratives

The accelerating progress of the digital landscape has led to the rise of consumption of content that is short-lived and fast-paced. In this context, social media platforms such as Instagram and Snapchat have become predominant for their effective use of temporary digital content that lasts for a brief period (usually 24 hours) before disappearing. Ephemerality has become the zeitgeist of the 21st century. In a world where immediacy and instant gratification are highly valued, the pattern of consumption of digital media is also aligned to such quick snippets of information rather than watching a long video. This paradigm shift is redefining how people perceive time and memory in the digital age. This preliminary study analyses the effect of such ephemeral digital narratives on the memory retention of consumers and also explores the implications of such content on cultural and collective memory. A sample of 100 participants ranging from the age of 18-35 years, were exposed to curated ephemeral content from Instagram and Snapchat for seven days. Participants were exposed to different media types, presentation styles, and cognitive demands. Daily surveys examined their memory retention using various question formats. The study examined ten hypotheses using Analysis of Variance (ANOVA) to determine how media type (video, image, both), presentation style (visual, verbal/textual, both), cognitive processing demands (shallow, deep processing), and question format affect recall effectiveness.

New Opportunities for Organic Synthesis with Superheated Flow Chemistry.

ConspectusFlow chemistry has brought a fresh breeze with great promises for chemical manufacturing, yet critical deterrents persist. To remain economically viable at production scales, flow processes demand quick reactions, which are actually not that common. Superheated flow technology stands out as a promising alternative poised to confront modern chemistry challenges. While continuous micro- and mesofluidic reactors offer uniform heating and rapid cooling across different scales, operating above solvent boiling points (i.e., operating under superheated conditions) significantly enhances reaction rates. Despite the energy costs associated with high temperatures, superheated flow chemistry aligns with sustainability goals by improving productivity (process intensification), offering solvent flexibility, and enhancing safety.However, navigating the unconventional chemical space of superheated flow chemistry can be cumbersome, particularly for neophytes. Expanding the temperature/pressure process window beyond the conventional boiling point under the atmospheric pressure limit vastly increases the optimization space. When associated with conventional trial-and-error approaches, this can become exceedingly wasteful, resource-intensive, and discouraging. Over the years, flow chemists have developed various tools to mitigate these challenges, with an increased reliance on statistical models, artificial intelligence, and experimental (kinetics, preliminary test reactions under microwave irradiation) or theoretical (quantum mechanics) a priori knowledge. Yet, the rationale for using superheated conditions has been slow to emerge, despite the growing emphasis on predictive methodologies.To fill this gap, this Account provides a concise yet comprehensive overview of superheated flow chemistry. Key concepts are illustrated with examples from our laboratory's research, as well as other relevant examples from the literature. These examples have been thoroughly studied to answer the main questions Why? At what cost? How? For what? The answers we provide will encourage educated and widespread adoption. The discussion begins with a demonstration of the various advantages arising from superheated flow chemistry. Different reactor alternatives suitable for high temperatures and pressures are then presented. Next, a clear workflow toward strategic adoption of superheated conditions is resorted either using Design of Experiments (DoE), microwave test chemistry, kinetics data, or Quantum Mechanics (QM). We provide rationalization for chemistries that are well suited for superheated conditions (e.g., additions to carbonyl functions, aromatic substitutions, as well as C-Y [Y = N, O, S, C, Br, Cl] heterolytic cleavages). Lastly, we bring the reader to a rational decision analysis toward superheated flow conditions. We believe this Account will become a reference guide for exploring extended chemical spaces, accelerating organic synthesis, and advancing molecular sciences.

Summary characteristics for multivariate function‐valued spatial point process attributes

SummaryPrompted by modern technologies in data acquisition, the statistical analysis of spatially distributed function‐valued quantities has attracted a lot of attention in recent years. In particular, combinations of functional variables and spatial point processes yield a highly challenging instance of such modern spatial data applications. Indeed, the analysis of spatial random point configurations, where the point attributes themselves are functions rather than scalar‐valued quantities, is just in its infancy, and extensions to function‐valued quantities still remain limited. In this view, we extend current existing first‐ and second‐order summary characteristics for real‐valued point attributes to the case where, in addition to every spatial point location, a set of distinct function‐valued quantities are available. Providing a flexible treatment of more complex point process scenarios, we build a framework to consider points with multivariate function‐valued marks, and develop sets of different cross‐function (cross‐type and also multi‐function cross‐type) versions of summary characteristics that allow for the analysis of highly demanding modern spatial point process scenarios. We consider estimators of the theoretical tools and analyse their behaviour through a simulation study and two real data applications.

Academic Profile of Teachers Affected by Poor Performances in Philosophy in the Final Year of Secondary School in Yaounde and Mbalmayo, Cameroon

Objective: The aim of this article is to examine the extent to which the teacher's academic profile can help to improve the performances of students in the final year of literary classes in philosophy. Methodology: This article is part of the field of Education Sciences, specifically in Discipline Didactics. The study is of an experimental type, centred on the reality of the educational facts that surround the practice of teaching philosophy. With the help of measuring instruments such as the questionnaire, test papers and observation guide, this approach made it possible to understand the determinants of performances in philosophy mentioned above. Results: The academic profile of the teacher constitutes a fundamental characteristic in the teaching practice of a discipline. Thus, in organised and structured teaching systems, only those who have the aptitudes to teach can really be admitted to teaching. This presupposes initial training in the subject coupled with appropriate professional training. In this way, we can partly understand the public authorities' demand for a selection process to recruit and train philosophy teachers. Discussion: The teaching of philosophy is based on a requirement of qualification and academic profile for the teacher, although it is more often referred to as a matter of vocation. In any case, a teacher with a vocation, unless he or she is gifted, is first and foremost concerned with training, with a view to practicing the profession of his or her heart with passion. This training predisposes them not only to be masters of their subject, but also to be able to overcome the socio-professional hazards characteristic of the educational environment. Conclusion: Philosophy is the subject in which students in the final year of secondary school perform worst. Consequently, this subject is increasingly feared and considered by learners to be their ‘bête noire’. Consequently, students' performances in philosophy can be improved thanks to ........

Emerging Advances around Nanofluidic Transport and Mass Separation under Confinement in Atomically Thin Nanoporous Graphene.

Membrane separation stands as an environmentally friendly, high permeance and selectivity, low energy demand process that deserves scientific investigation and industrialization. To address intensive demand, seeking appropriate membrane materials to surpass trade-off between permeability and selectivity and improve stability is on the schedule. 2Dmaterials offer transformational opportunities and a revolutionary platform for researching membrane separation process. Especially, the atomically thin graphene with controllable porosity and structure, as well as unique properties, is widely considered as a candidate for membrane materials aiming to provide extreme stability, exponentially large selectivity combined with high permeability. Currently, it has shown promising opportunities to develop separation membranes to tackle bottlenecks of traditional membranes, and it has been of great interest for tremendously versatile applications such as separation, energy harvesting, and sensing. In this review, starting from transport mechanisms of separation, the material selection bank is narrowed down to nanoporous graphene. The study presents an enlightening overview of very recent developments in the preparation of atomically thin nanoporous graphene and correlates surface properties of such 2D nanoporous materials to their performance in critical separation applications. Finally, challenges related to modulation and manufacturing as well as potential avenues for performance improvements are also pointed out.

Sequential Patching Lattice for Image Classification and Enquiry: Streamlining Digital Pathology Image Processing

Digital pathology and the integration of artificial intelligence (AI) models have revolutionized histopathology, opening new opportunities. With the increasing availability of Whole Slide Images (WSIs), there’s a growing demand for efficient retrieval, processing, and analysis of relevant images from vast biomedical archives. However, processing WSIs presents challenges due to their large size and content complexity. Full computer digestion of WSIs is impractical, and processing all patches individually is prohibitively expensive. In this paper, we propose an unsupervised patching algorithm, Sequential Patching Lattice for Image Classification and Enquiry (SPLICE). This novel approach condenses a histopathology WSI into a compact set of representative patches, forming a “collage” of WSI while minimizing redundancy. SPLICE prioritizes patch quality and uniqueness by sequentially analyzing a WSI and selecting nonredundant representative features. We evaluated SPLICE for search and match applications, demonstrating improved accuracy, reduced computation time, and storage requirements compared to existing state-of-the-art methods. As an unsupervised method, SPLICE effectively reduces storage requirements for representing tissue images by 50%. This reduction enables numerous algorithms in computational pathology to operate much more efficiently, paving the way for accelerated adoption of digital pathology.

Effect of micro and nanobubble size and concentration on membrane fouling: Strategies for energy-saving and ultrafiltration efficiency enhancement

The impact of micro and nanobubbles (MNBs) on preventing membrane fouling in various filtration techniques has been effectively demonstrated. However, there remains a gap in understanding the influence of their size and concentration on membrane fouling. Moreover, the substantial energy demand for MNBs-aided filtration processes hinders the wide adoption of this technology on a global scale. This study controlled MNB size and concentration by optimizing the release pressure of air-saturated water and introducing intermittent operation schemes. At 0.35 MPa, the MNBs averaged 91 μm and 117.9 nm with concentrations of 3.27E+3 and 6.85E+6 particles mL−1. Increasing the pressure to 0.60 MPa reduced the average size to 31 μm and 119.9 nm. Concentrations to 6.43E+5 and 9.40E+6 particles mL−1. Bubbles released at 0.60 MPa reduced TMP from −0.0109 MPa attained with 0.35 MPa bubbles to −0.0052 MPa making a 70 % decrease. Comparing continuous and optimal intermittent release patterns (30 min) revealed marginal differences: normalized transmembrane pressure (4 %), cleaning efficiency (9 %), and rejection efficiency (2.4 %). Our approach enhances membrane filtration efficiency, maintains performance, reduces energy consumption, and advances sustainable filtration technologies encouraging global adoption.