Relevant Applications Research Articles

Sustainable urban digital innovation: A socio-technical competency-based approach to evaluation

This study explores the leadership competencies required in practice by city planners and managers in smart city projects focusing on environmental urban sustainability. Although the literature notes that urban technologies and their capabilities can help address sustainability challenges in cities, there is a lack of studies exploring the competency requirements necessary to foster leadership capacity. This paper identifies leadership competencies within four real-world case studies in the urban built environment, guided by a socio-technical competency framework (DC2-CF). The selected case studies represent a diverse set of city planning purposes, geographic regions, various levels of spatial scale, and socio-technical elements of digital innovation. In these case studies, city managers exhibit specific competencies to develop digital innovation projects that uphold and advance urban sustainability. The study demonstrates the relevance and practical application of DC2-CF as a valuable tool to identify competency needs for local public, private, and community stakeholders throughout diverse stages of the urban digital innovation process. The findings suggest the complex relationship between competencies and project delivery, stressing variations in how they are utilised across various projects. Drawing from these key results, this paper provides practical recommendations for city professionals, guiding them in leading climate-friendly and sustainable urban digital innovation.

Tiers of Engagement: Achieved Learning From Business Simulations Reflected in Economics Students’ Experiences

This article investigates the impact of a business simulator in an online economics course, focusing on the relationship between student engagement and reflections on learning. Utilizing a mixed-methods approach, our study analyzes responses from 328 students who used the "Hubro Business Simulator" in a flexible online bachelor's program in economics and administration. Students provided reflections on the simulator as a learning activity, application of theoretical concepts and practical relevance. Qualitative analysis of 1640 responses further explored the depth of students' reflections, categorized into different tiers of engagement based on their responses. This analysis builds on Self-Regulated Learning (SRL) and motivational theory. The study sought to determine whether students reporting higher levels of engagement exhibited deeper metacognitive reflections compared to their less engaged peers. Findings support a link between the level of engagement and the depth of reflection. Students with higher engagement levels were more likely to reflect on specific ways in which the simulator had supported their learning and understanding of theoretical concepts. This paper discusses the limitations and implications of these findings for enhancing game-based learning interventions, emphasizing the need to foster higher engagement levels to improve educational outcomes in online learning environments. The study contributes to the literature on game-based learning by providing a large empirical study on how business simulators can foster higher-order thinking and deeper learning reflections among economics students. It also highlights the role of engagement in using business simulators as learning tools, offering insights that could improve the learning design and implementation in similar online and flexible learning program contexts.

Exploring The Protease Diversity of Psychrophilic Yeast, Glaciozyma antarctica Through Genome Mining Analysis

Proteases are one of the most significant classes of enzymes, holding immense physiological relevance and extensive industrial applications. The genome of Glaciozyma antarctica was fully sequenced, showing 7,857 open reading frames that offer an intriguing opportunity to investigate its proteolytic repertoire. This study aims to unveil the protease landscape of G. antarctica, a psychrophilic yeast that produces cold-active enzymes that offer remarkable benefits, particularly in the food and pharmaceutical industries. In this work, we performed a comprehensive analysis to identify the diverse families of proteases encoded within the G. antarctica genome and compare them with proteases from other mesophilic and thermophilic fungi in the MEROPS database. The sequence similarity searches resulted in the identification of 195 open reading frames predicted to encode for proteases in G. antarctica with a high number of intracellular proteases. These findings suggest an evolved system for protein quality control and turnover, essential for cell viability and adaptation to environmental stressors. The MEROPS classification analysis showed an abundance of metalloproteases, constituting 38% of the total protease genes, a proportion surpassing that found in other yeast and fungal genomes studied. This reflects the vital role of metalloproteases in the cold adaptation of microbes in the Antarctic region. This unique profile not only sheds light on the adaptive mechanisms of psychrophilic organisms but also presents a rich reservoir of potential cold-active proteases for various applications. The findings of this study provide a foundation for targeted enzyme discovery and engineering, unlocking new frontiers in industrial biotechnology and extremophile biology.

Progress on the development of Class A GPCR-biased ligands.

Class A G protein-coupled receptors (GPCRs) continue to garner interest for their essential roles in cell signalling and their importance as drug targets. Although numerous drugs in the clinic target these receptors, over 60% GPCRs remain unexploited. Moreover, the adverse effects triggered by the available unbiased GPCR modulators, limit their use and therapeutic value. In this context, the elucidation of biased signalling has opened up new pharmacological avenues holding promise for safer therapeutics. Functionally selective ligands favour receptor conformations facilitating the recruitment of specific effectors and the modulation of the associated pathways. This review surveys the current drug discovery landscape of GPCR-biased modulators with a focus on recent advances. Understanding the biological effects of this preferential coupling is at different stages depending on the Class A GPCR family. Therefore, with a focus on individual GPCR families, we present a compilation of the functionally selective modulators reported over the past few years. In doing so, we dissect their therapeutic relevance, molecular determinants and potential clinical applications.

Pentingnya Pertimbangan Pastoral dalam Pelayanan Pastor Paroki: Pendekatan Tanggung Jawab Menurut Bernard Haring

Pastoral discernment is an important process in accompanying people, which enables the parish priest to provide appropriate moral guidance in appropriate situations. Bernard Haring emphasized objective moral principles and objective moral principles and religious teachings as the basis for appropriate pastoral discernment. This article explores this concept in more depth and provides concrete examples of how Haring's approach can be applied in pastoral life. Haring's approach can be applied in everyday pastoral life. In addition, the article highlights the importance of integrating pastoral wisdom with the parish priest's responsibility to embrace and accompany people in complex situations. complex situations. By looking at the relevance and practical application of these concepts, this article provides valuable insights on how the ministry of parish priests can be more effective and meaningful in accompanying parishioners in terms of moral especially family moral and spiritual.

Relevance and Potential Applications of C2-Carboxylated 1,3-Azoles.

Carbon dioxide (CO2) is an economically viable and abundant carbon source that can be incorporated into compounds such as 1,3-azoles relevant to the pharmaceutical, cosmetics, and pesticide industries. Of the 2.4 million commercially available C2-unsubstituted 1,3-azole compounds, less than 1 % are currently purchasable as their C2-carboxylated derivatives, highlighting the substantial gap in compound availability. This availability gap leaves ample opportunities for exploring the synthetic accessibility and use of carboxylated azoles in bioactive compounds. In this study, we analyze and quantify the relevance of C2-carboxylated 1,3-azoles in small-molecule research. An analysis of molecular databases such as ZINC, ChEMBL, COSMOS, and DrugBank identified relevant C2-carboxylated 1,3-azoles as anticoagulant and aroma-giving compounds. Moreover, a pharmacophore analysis highlights promising pharmaceutical potential associated with C2-carboxylated 1,3-azoles, revealing the ATP-sensitive inward rectifier potassium channel 1 (KATP) and Kinesin-like protein KIF18A as targets that can potentially be addressed with C2-carboxylated 1,3-azoles. Moreover, we identified several bioisosteres of C2-carboxylated 1,3-azoles. In conclusion, further exploration of the chemical space of C2-carboxylated 1,3-azoles is encouraged to harness their full potential in drug discovery and related fields.

Steady spectra of supreme resolution and lowest noise in high-order optimized derivative fast Fourier transform for ovarian NMR spectroscopy

The optimized derivative fast Fourier transform (dFFT) simultaneously increases resolution and reduces noise in spectra reconstructed from encoded time signals. The pertinent applications have recently been published for time signals encoded with and without water suppression by in vitro and in vivo magnetic resonance spectroscopy (MRS). Even with the employed lower derivative orders, genuine resonances were narrowed, their intensities enhanced and the background baselines flattened. This unequivocally separated many overlapped peaks that are the thorniest problem in data analysis by signal processing. However, it has been common knowledge that higher-order derivative spectra quickly deteriorate with the increased derivative order. The optimized dFFT can challenge such findings. An unprecedented resilience of this processor to derivative-induced distortions is presently demonstrated for high derivative orders (up to 20). The salient illustrations are given for the water residual, lactate quartet and lactate doublet alongside their close surroundings. These applications of diagnostic relevance for patients with cancer are reported for time signals encoded with water suppression by in vitro proton MRS of human ovary.

Metode Pendampingan Iman Anak melalui Kegiatan Serikat Kepausan Anak Misioner di Stasi Santo Yosef Kampung Baru

This research aims to determine the forms of activities for mentoring children's faith, implementation of mentoring methods, supporting and inhibiting factors for mentoring children's faith through SEKAMI activities at Stasi St. Yosef Kampung Baru. A descriptive qualitative approach, with interactive analysis was used to analyze data collected through observation, interviews, with 13 informants consisting of 4 companions and 9 SEKAMI members. The results of this research show that the forms of mentoring activities are communicative, creative, communal and learning activities. Based on the mentoring methods, namely the explaining, asking, doing and assigning methods, it is known that the most dominant method used by the mentors is the doing method because this method is considered to be able to help children see the relevance and practical application of the mentoring material in everyday life. The explanation method shows an attempt to provide a direct explanation regarding the material to be studied. The questioning method is used to encourage participants to participate in learning by formulating relevant questions. Meanwhile, the assignment method shows an effort to involve children in tasks that can test their understanding and skills. The combination of these four methods is a holistic approach to mentoring. Through a variety of different methods, it can provide an interesting, enjoyable and effective faith mentoring experience.

Scalable solutions for implementing satellite-based sensor networks

The importance of sensor networks in the field of IoT cannot be overstated, especially with the growing demand for IoT solutions. The new Internet protocols such as IPv6 and 5G mobile provide high-capacity communication networks, demonstrating the relevance and potential applications of IoT in the future. However, with new demands come new challenges. This paper aims to explore the challenges of Satellite-based Sensor Networks, a new topic with various applications. This article explores the challenges and constraints of implementing a scalable satellite-based sensor network. We examined the hardware and software limitations of the Iridium satellite SBD protocol and proposed tailored solutions using design flow algorithms to tackle flexibility, power consumption, bandwidth, and scalability issues. Satellite-based sensor networks face challenges in data transmission and sensor lifespan. In this study, we introduce event-driven and compression algorithms to address these challenges. By implementing clustering, we achieved a significant increase in the number of nodes within each cluster. Our proposed methods were validated using simulated datasets derived from temperature and location data. A unique data transmission method, based on event count, was introduced. Notably, the Differential Huffman compression algorithm resulted in an 81 % reduction in data transmission compared to traditional methods. This demonstrates about 62 % improvement in efficiency over existing techniques. Our approach not only paves the way for future advancements in this domain but also highlights the untapped potential of satellite-based sensor networks.

SED-RCNN-BE: A SE-Dual channel RCNN network optimized binocular estimation model for automatic size estimation of free swimming fish in aquaculture

Size of fish body is one of the most important morphological characteristics for cultured fish and plays a key role to enable effective management in aquaculture. Automatic, accurate and real-time monitoring of fish body size is therefore essential for improving aquaculture production. Computer vision facilitated the non-contact estimation of fish size and reduce labor consumption. While, current computer vision estimation techniques are still restricted in multi-scale size and multi-pose-variant of free-swimming fish, which limit the relevance application to aquaculture. Hence, we propose a novel method to facilitate automatic and accurate size estimation of free-swimming fish for growth monitoring of fish in aquaculture. This method comprised of an improved keypoint detection module based on Keypoint RCNN (SE-D-KP-RCNN) network and size estimation module based on CREStereo matching and spatial plane curve fitting. In the detection module, a squeeze and excitation Dual channel pooling layer (SE-D) is designed for the backbone network to improve the keypoint detection accuracy for free-swimming fish. In the estimation module, the global parallax map of binocular image is produced by CREStereo matching to obtain the 3D information of detection keypoint and build curve fitting of multi-pose variant fish body for height and length estimation. We implement and test our proposed method in different sizes and poses fish and the comprehensive experimental results show that the average accuracy of size estimation can reached 93.18% in the real aquaculture scene. This is important to help breeders to obtain underwater fish size information accurately and improve the efficiency for aquaculture management.

Hierarchical Neighbors Embedding.

Manifold learning now plays an important role in machine learning and many relevant applications. In spite of the superior performance of manifold learning techniques in dealing with nonlinear data distribution, their performance would drop when facing the problem of data sparsity. It is hard to obtain satisfactory embeddings when sparsely sampled high-dimensional data are mapped into the observation space. To address this issue, in this article, we propose hierarchical neighbors embedding (HNE), which enhances the local connections through hierarchical combination of neighbors. And three different HNE-based implementations are derived by further analyzing the topological connection and reconstruction performance. The experimental results on both the synthetic and real-world datasets illustrate that our HNE-based methods could obtain more faithful embeddings with better topological and geometrical properties. From the view of embedding quality, HNE develops the outstanding advantages in dealing with data of general distributions. Furthermore, comparing with other state-of-the-art manifold learning methods, HNE shows its superiority in dealing with sparsely sampled data and weak-connected manifolds.

Physically consistent modelling of surface tension forces in the Volume-of-Fluid method for three or more phases

Multiphase numerical simulations have become a widely sought methodology for modelling capillary flows due to their scientific relevance and multiple industrial applications. Much progress has been achieved using different approaches, and the volume of fluid is one of the most popular methods widely used for modelling two or more phases due to its simplicity, accuracy and robustness. However, when prescribing the forces emerging from three or more fluid-fluid interfaces, the force balance is not guaranteed and can lead to spurious self-propulsion. Here, a new approach to account for the surface tension forces for multiphase flows with a correct force balance is proposed. The newly proposed method is successfully validated for a wide range of tests, including contact angles for the fluid-fluid and fluid-solid triple line. Additionally, complete spreading phenomena of fluid on fluid and fluid on solid have been found to emerge naturally from the newly proposed surface tension force model. Finally, simulation results are compared against experiments of lubricant-impregnated surfaces to demonstrate the practical applicability of the newly proposed method.

Research on Teaching of Reading Based on Krashen s Input Hypothesis in Senior High Schools

It is well known that reading teaching plays a vital role in English teaching in high schools, because it can not only improve students cultural literacy, but also promote their development of other language skills. According to the requirements of New Curriculum Standard of the Ministry of Education, students should have read more than 200, 000 English words before graduating from high schools. However, in traditional teaching mode, the teacher s teaching and the student s learning are not closely connected, so students reading ability can hardly be improved. Based on this phenomenon, the paper combines Input Hypothesis with English reading teaching in senior high schools, which aims to raise students reading interest and improve their reading ability, so that English reading teaching can be promoted at the same time. In this paper, the current problems in high school English reading are found out. To address the problems, the author gives some suggestions according to Krashen s Input Hypothesis, including teaching of various reading strategies and application of comprehensibility , relevance and interest , not grammatically sequenced and sufficient quantity principle. It is hoped that the advice can provide reference and guidance for English reading teaching in senior high schools.

Stability, Chemical Bonding, and Electron Lone Pair Localization in AsN at High Pressure by Density Functional Theory Calculations.

The covalent bonding framework of crystalline single-bonded cubic AsN, recently synthesized under high pressure and high temperature conditions in a laser-heated diamond anvil cell, is here studied by means of density functional theory calculations and compared to single crystal X-ray diffraction data. The precise localization of the nonbonding electron lone pairs and the determination of their distances and orientations are related to the presence of characteristic structural motifs and space regions of the unit cell dominated by repulsive electronic interactions, with the relative orientation of the electron lone pairs playing a key role in minimizing the energy of the structure. We find that the vibrational modes associated with the expression of the lone pairs are strongly localized, an observation that may have implications for the thermal conductivity of the compound. The results indicate the thermodynamic stability of the experimentally observed structure of AsN above ∼17 GPa, provide a detailed insight into the nature of the chemical bonding network underlying the formation of this compound, and open new perspectives to the design and high pressure synthesis of new pnictogen-based advanced materials for potential applications of energetic and technological relevance.

Electric Propulsion in Space: Technology and Geopolitics

ABSTRACT During the 20th Century and within the first decade of the 21st, electric propulsion (EP) has played an important role in space activities, and its importance has not ceased to increase significantly in the last 15 years. The potential benefits of using EP on a bigger scale could be immense, reducing the cost of future space missions. The main hypothesis of this paper is that EP will play an increasingly important role in the system of international space relations in the coming decades with deep scientific, commercial, and geopolitical implications. To prove this, we examine the historical evolution, relevance, and future applications of such a propulsion system. Although there is extensive technical literature on EP, only a few works analyze electric space propulsion from an interdisciplinary perspective. On the contrary, much of the available information only provides an atomized, unipolar, and fragmented approach to the phenomenon. To fill this gap, this study presents an integrated analysis of the evolution, relevance, and trends of EP technologies, as well as their geopolitical implications in the space domain. The results obtained constitute a crucial contribution to the analysis of space propulsion from an interdisciplinary – socio-technological – perspective.

Low-temperature gas sensing mechanism in β-Ga2O3 nanostructures revealed by near-ambient pressure XPS

Ga2O3 is a promising gas-sensing material that can operate over a wide temperature range. However, despite its relevance for practical applications, there is a lack of understanding regarding its sensing mechanism at temperatures below 800 °C. To reveal the sensing mechanism, GaOOH nanorods were grown on β-Ga2O3 seed layers by chemical bath deposition, then converted to β-Ga2O3 by annealing in the air at 500 °C, and the surface properties were investigated by near-ambient pressure X-ray photoelectron spectroscopy (NAP-XPS) in the presence of oxidizing/reducing gases. Time-stable shifts in the XPS spectra were used to estimate the relative changes in conductivity in compliance with the ionosorption model. Our results indicate that the sensing mechanism at lower temperatures is governed by redox reactions, leading to an increase/decrease in the conductivity for reducing/oxidizing gases. Furthermore, we provide a detailed description of the ethanol-sensing mechanism at different temperatures.

A Sparse SAR Imaging Method for Low-Oversampled Staggered Mode via Compound Regularization

High-resolution wide-swath (HRWS) imaging is the research focus of the modern spaceborne synthetic-aperture radar (SAR) imaging field, with significant relevance and vast application potential. Staggered SAR, as an innovative imaging system, mitigates blind areas across the entire swath by periodically altering the radar pulse repetition interval (PRI), thereby extending the swath width to multiples of that achievable by conventional systems. However, the staggered mode introduces inherent challenges, such as nonuniform azimuth sampling and echo data loss, leading to azimuth ambiguities and substantially impacting image quality. This paper proposes a sparse SAR imaging method for the low-oversampled staggered mode via compound regularization. The proposed method not only effectively suppresses azimuth ambiguities arising from nonuniform sampling without necessitating the restoration of missing echo data, but also incorporates total variation (TV) regularization into the sparse reconstruction model. This enhances the accurate reconstruction of distributed targets within the scene. The efficacy of the proposed method is substantiated through simulations and real data experiments from spaceborne missions.

Rapid Volumetric Bioprinting of Decellularized Extracellular Matrix Bioinks.

Decellularized extracellular matrix (dECM)-based hydrogels are widely applied to additive biomanufacturing strategies for relevant applications. The extracellular matrix components and growth factors of dECM play crucial roles in cell adhesion, growth, and differentiation. However, the generally poor mechanical properties and printability have remained as major limitations for dECM-based materials. In this study, heart-derived dECM (h-dECM) and meniscus-derived dECM (Ms-dECM) bioinks in their pristine, unmodified state supplemented with the photoinitiator system of tris(2,2-bipyridyl) dichlororuthenium(II) hexahydrate and sodium persulfate, demonstrate cytocompatibility with volumetric bioprinting processes. This recently developed bioprinting modality illuminates a dynamically evolving light pattern into a rotating volume of the bioink, and thus decouples the requirement of mechanical strengths of bioprinted hydrogel constructs with printability, allowing for the fabrication of sophisticated shapes and architectures with low-concentration dECM materials that set within tens of seconds. As exemplary applications, cardiac tissues are volumetrically bioprinted using the cardiomyocyte-laden h-dECM bioink showing favorable cell proliferation, expansion, spreading, biomarker expressions, and synchronized contractions; whereas the volumetrically bioprinted Ms-dECM meniscus structures embedded with human mesenchymal stem cells present appropriate chondrogenic differentiation outcomes. This study supplies expanded bioink libraries for volumetric bioprinting and broadens utilities of dECM toward tissue engineering and regenerative medicine.

Advancing sustainable water treatment strategies: harnessing magnetite-based photocatalysts and techno-economic analysis for enhanced wastewater management in the context of SDGs.

Herein, we explore the holistic integration of magnetite-based photocatalysts and techno-economic analysis (TEA) as a sustainable approach in wastewater treatment aligned with the Sustainable Development Goals (SDGs). While considerable attention has been devoted to photocatalytic dye degradation, the nexus between these processes and techno-economic considerations remains relatively unexplored. The review comprehensively examines the fundamental characteristics of magnetite-based photocatalysts, encompassing synthesis methods, composition, and unique properties. It investigates their efficacy in photocatalytic degradation, addressing homogeneous and heterogeneous aspects while discussing strategies to optimize photodegradation efficiency, including curbing electron-hole recombination and mitigating scavenging effects and interference by ions and humic acid. Moreover, the management aspects of magnetite-based photocatalysts are examined, focusing on their reusability and regeneration post-dye removal, along with the potential for reusing treated wastewater in relevant industrial applications. From a techno-economic perspective, the study evaluates the financial feasibility of deploying magnetite-based photocatalysts in wastewater treatment, correlating reduced pollution and the marketing of treated water with social, economic, and environmental objectives. By advocating the integration of magnetite-based photocatalysts and TEA, this paper contributes insights into scalable and profitable sustainable wastewater treatment practices. It underscores the alignment of these practices with SDGs, emphasizing a comprehensive and holistic approach to managing wastewater in ways that meet environmental, economic, and societal objectives.

A mathematical study of the adrenocorticotropic hormone as a regulator of human gene expression in adrenal glands

Abstract In this research, we have introduced compartments for asymptomatic and symptomatic individuals, along with reduced susceptibility, as key factors defining our investigation. The study is carried out in diverse scenarios, considering them as crucial for the essential generation number of the model, set at 3.18( r 0 > 1 {r}_{0}\gt 1 ). The persistent reproduction differential method was used to explore the impact of continuous adrenocorticotropic hormone (ACTH) administration on the global gene expression in primary cultures of both fetal and adult adrenal cells. The study also investigates ACTH’s genetic effects on both adult and fetal human adrenal cells. The conclusion of this study is demonstrated through relevant and correct medical applications.