Space Reduction Research Articles

Assessment of Green Space Dynamics Under Urban Expansion of Senegalese Cities: The Case of Dakar

Senegalese cities have experienced rapid urbanisation, leading to profound landscape changes. Dakar, one of Senegalese’s fastest-growing cities, is experiencing rapid urban expansion, significantly reducing green spaces. These green spaces, essential for urban sustainability and resilience, have become increasingly scarce, affecting the city’s environment and the quality of life for its residents. This study aims to assess the spatiotemporal changes in Dakar’s green spaces from 1990 to 2022. Using satellite imagery, this study produces land use maps to quantify green space coverage over the years. The results show a gradual decline in green spaces in Dakar between 1990 and 2022. In 1990, green spaces covered an estimated 13.36% of Dakar’s area, which decreased significantly to 9.54% by 2022. In contrast, other land uses, such as built-up areas, increased significantly over this period, rising from 19.23% in 1990 to 39.34% in 2022. Moreover, built-up areas are not the sole contributor to the reduction of green spaces in Dakar. The study revealed that, between 1990 and 2022, 5.49% of green spaces were converted into bare soil due to excessive tree cutting. This pattern highlights the growing challenge of green space availability as built-up areas expand rapidly, particularly when growth is unplanned. This study underscores the importance of sustainable urban planning that integrates the protection and conservation of Dakar’s vegetation to preserve vital ecosystem services.

Protein Binding Site Representation in Latent Space.

Interpretability and reliability of deep learning models are important for computer-based drug discovery. Aiming to understand feature perception by such a model, we investigatea graph neural network for affinity prediction of protein-ligand complexes. We assess a latent representation of ligand binding sites and investigate underlying geometric structure in this latent space and its relation to protein function. We introduce an automated computational pipeline for dimensionality reduction, clustering, hypothesis testing, and visualization of latent space. The results indicate that the learned protein latent space is inherently structured and not randomly distributed. Several of the identified protein binding site clusters in latent space correspond to functional protein families. Ligand size was found to be a determinant of cluster geometry. The computational pipeline proved applicable to latent space analysis and interpretation and can be adapted to work for different datasets and deep learning models.

Spectrum of the Maxwell Equations for a Flat Interface Between Homogeneous Dispersive Media

The paper determines and classifies the spectrum of a non-self-adjoint operator pencil generated by the time-harmonic Maxwell problem with a nonlinear dependence on the frequency for the case of two homogeneous materials joined at a planar interface. We study spatially one-dimensional and two-dimensional reductions in the whole space R and R2. The dependence on the spectral parameter, i.e. the frequency, is in the dielectric function and we make no assumptions on its form. These function values determine the spectral sets. In order to allow also for non-conservative media, the dielectric function is allowed to be complex, yielding a non-self-adjoint problem. The whole spectrum consists of eigenvalues and the essential spectrum, but the various standard types of essential spectra do not coincide in all cases. The main tool for determining the essential spectra are Weyl sequences.

Household car-ownership in a world of constant change: The continued influence of traditional variables and the rising influence of emerging mobility scenarios

AbstractEmerging modes (e.g., cargo bikes), mobility solutions (e.g., shared mobility services) and policy measures (e.g., the reduction of public parking spaces) are envisioned to reduce private car-ownership. However, the development of disaggregate car-ownership models dealing with them, which can be integrated with transport simulation systems, is still missing. Therefore, this study aims to fill this gap through the development of household car-ownership models, focusing on the cities of Madrid, Regensburg and Leuven. These cities correspond to distinct urban contexts in Europe. Models based on a representative individual as well as based only on household variables are explored, to check whether the latter shows congruence with the former and can achieve good summary statistics with lower data requirements. Furthermore, such models with only household-level variables are also found to be useful in ascertaining the impact of small-scale shared services. The estimation results show the influence of socio-demographic characteristics, mobility patterns, mobility tool ownership, transport supply and urban characteristics. Discussions are included on behavioural and policy insights. For example, cargo bike ownership and the presence of shared services support in car-ownership reduction. Furthermore, public parking spaces have a significant impact on single car-ownership in Madrid, but not on multiple car-ownership. Besides, the contrasting effects found for the mobility rates in Madrid and Regensburg convey the importance of urban design contexts. The contributions from this study enable to estimate the household car-ownership with the consideration of emerging mobility scenarios, and to devise policies to reduce private car-ownership and promote sustainable urban mobility.

First-principles investigation of oxygen repulsion behavior on the Ir–Hf alloy surface

Enhancing the oxidation resistance of hot-end components under extreme conditions is a key focus in aerospace equipment research. We used first-principles simulations to investigate a series of IrxHf100−x (at. %) coatings. By employing density functional theory and the local density approximation method, we studied the surface oxygen repulsion energy and estimated the optimal composition ratio of IrxHf100−x for the strongest oxidation resistance. The results indicate that as the Hf content increases, the oxygen repulsion energy first rises and then falls, reaching a maximum value of 3.04 eV at x = 10 at. %, with active oxygen adsorption occurring when X exceeds 45 at.%. To understand the mechanism of the oxygen repulsion process, we analyzed the electronic characteristics between Ir, Hf, and O. O preferentially hybridizes its atomic orbitals with Ir, and the improvement in oxidation resistance of the Ir–Hf coating is attributed to the reduction in Ir atomic spacing due to Hf doping, which reduces O–Ir orbital hybridization.

Formal error bounds for the state space reduction of Markov chains

Formal error bounds for the state space reduction of Markov chains

A machine learning approach for solution space reduction in aircraft disruption recovery

A machine learning approach for solution space reduction in aircraft disruption recovery

Outcome evaluation for the treatment of low flow venous and lymphatic malformations

PurposeTo propose a standardized method of subjectively and objectively evaluating outcomes of sclerotherapy in treating low flow vascular malformations.Materials and methodsSixty-six patients with low flow vascular malformations (venous, lymphatic, or combined) were treated with percutaneous sclerotherapy using bleomycin, doxycycline, or sodium tetradecyl sulphate. Each lesion required between 2–5 sessions of sclerotherapy with 8-week intervals in between.The success of sclerotherapy was evaluated subjectively and objectively. The subjective response was based on the degree of patient satisfaction, by recording improvement of their symptoms and quality of life. The objective response was based on the changes in lesion characteristics after treatment, by recording changes in size, sonographic features, number of cystic spaces, and development of phleboliths.Results91% of our patients were satisfied with the treatment and reported improvement of symptoms and quality of life. Radiologically, 62% (41/66) of the patients had a reduction in lesion size, 77% (51/66) had a change in echogenicity, 84% (51/61) had a reduction in cystic spaces, and 68% (30/44) developed phleboliths. Of the patients reporting significant improvement, 94% displayed reduction in cystic spaces, 89% displayed change in the echogenicity and 71% showed changes in the size of the lesions, representing a linear correlation.ConclusionEvaluating the outcomes of percutaneous sclerotherapy for treating vascular malformations is a recognized challenge. Creating a questionnaire with defined parameters to apply before and after treatment allows objective measurement of outcomes. This will enable improved treatment pathways and treatment choice for patients, informed consent, and enable outcome comparison with other centers.

Quantifying the impact of construction defects on square RC columns

Reinforced concrete (RC) columns are integral to structural integrity, and their performance is highly dependent on precise construction practices. This study investigates the influence of construction defects on the behavior of RC columns, focusing on key parameters such as misplaced longitudinal (vertical) reinforcing bars, honeycombed concrete, variations in stirrup spacing, casting eccentricity, and the effectiveness of square welded wire mesh for confinement. An experimental program was conducted with thirteen square RC column specimens designed to simulate potential construction defects. The findings demonstrate a clear relationship between defect types and column performance. Misplacement of vertical reinforcing bars by up to 50 % resulted in a 17 % reduction in ultimate load capacity. Honeycombed concrete, due to inadequate compaction, was identified as the most harmful defect, leading to a 46 % decrease in ultimate load and a 34 % reduction in toughness. Conversely, reducing stirrup spacing, while maintaining reinforcement quantity, enhanced column performance; a 33 % reduction in spacing resulted in comparable ultimate load but a 36 % increase in toughness. Increased casting eccentricity negatively impacted performance, with a 30 mm eccentricity causing a 46 % reduction in ultimate load capacity. The addition of square welded wire mesh over stirrups significantly improved performance, yielding a 13 % increase in ultimate load, while the use of welded wire mesh alone was ineffective, resulting in nearly a 50 % decrease in load capacity. These results underscore the importance of proper construction practices for the safety and durability of concrete structures. Accurate reinforcement placement, thorough concrete compaction, and precise column alignment are crucial. The research also suggests that using square welded wire mesh alongside stirrups can mitigate some construction defects’ adverse effects. Future studies should explore a broader range of column designs and loading conditions, as well as the effectiveness of various repair and strengthening techniques for columns with existing defects.

Dynamic indentation and microstructural analysis of artery under pre-strain in different directions

Strain from mechanical overload is a critical factor in vascular wall rupture associated with vascular diseases. Therefore, investigating the mechanical properties of blood vessels under strain is essential. Previous studies have predominantly focused on strain-free tests, and lacking microstructural analysis under load conditions. In this study, a self-made indentation device was used to combine with a pre-strain apparatus. Dynamic indentation tests were conducted on porcine arteries under pre-strain. Hematoxylin & Eosin staining and Masson staining were employed to observe microstructural changes in the arteries under strain. The results indicate that under pre-strain, the shear modulus of the arteries is positively correlated with the strain ratio. Radial strain increase the arterial relaxation ratio, while axial strain decrease it. Pre-strain enhance the arterial storage and loss modulus, which peaks at resonance frequencies of approximately 0.04 Hz and 1 Hz. Under pre-strain, elastic fibers experience a decrease in axial diameter and a reduction in spacing. The distribution of collagen is abnormal under radial strain. In the design of artery stents, a rationally designed stent structure should be considered so that the localized strain of the vessel wall is more decomposed into the axial direction.

BWM-MARCOS: A new hybrid MCDM approach for mineral potential modelling

Here we describe the application of a novel hybrid multi-criteria decision-making (MCDM) approach termed “Best-Worst-Method-Measurement of Alternatives and Ranking according to COmpromise Solution” (BWM-MARCOS) to mineral potential modelling (MPM). The newly proposed BWM-MARCOS technique combines two mathematical frameworks in which the BWM approach is utilised to weight decision criteria (i.e., predictor maps) whilst the MARCOS approach is applied to rank alternatives. A BWM-MARCOS model of orogenic gold mineral systems in the well-endowed Granites-Tanami Orogen (GTO) in Australia's Northern Territory was generated utilising the same study area and set of competent spatial proxies as previously developed and described by Roshanravan et al. (2020), facilitating the benchmarking of the new results against those obtained from previous models. We found the BWM-MARCOS approach to MPM performed better than any of the knowledge-driven (i.e., fuzzy inference system), continuous (i.e., data-driven index overlay, geometric average and fuzzy gamma) and data-driven (i.e., feed-forward deep neural network and ‘original’ random forest) mineral prospectivity models previously developed for the GTO by Roshanravan et al. (2020, 2023b). To further constrain the BWM-MARCOS outputs, the modeled gold potential zones were delimited utilising a concentration-area fractal approach. The areas covered by the prioritised 1st order (top priority) and 2nd order (high priority) targets represent significant reductions of the search space (i.e., >303 times or two orders of magnitude, and >93 times or one order of magnitude, respectively). Any order of magnitude, or greater, reduction of the search space, as achieved in this study, can be considered a hallmark of a well-performing, practically useful targeting approach.

Accelerated bender’s decomposition algorithm and hybrid heuristics for multi-period planning of maternal healthcare facilities in India

This work addresses the challenge of improving availability and accessibility of maternal healthcare in India by presenting a multi-period planning problem of hierarchical and successively inclusive healthcare facilities. The problem is formulated as a mixed-integer linear programming model to minimize the overall cost, including the cost of establishing and upgrading the facilities, the cost of allocating/referring the mothers-to-be to the respective facilities and the penalty cost of demotivating the overburdening of the facilities in each time period. To solve the model effectively and efficiently, a Bender’s Decomposition Algorithm (BDA) with several acceleration strategies such as valid inequalities, disaggregated Benders cuts, rolling horizon heuristic and parallelism is developed. A Bender’s type heuristic is also tested by solving the master problem heuristically. Additionally, a Fix-and-Optimize (F&O) heuristic hybridized with Simulated Annealing (SA) enhanced by various search space reduction techniques is developed to obtain good quality solutions in a reasonable time for large instances. It is evident from the results of the computational experiments that the accelerated BDA and Bender type heuristic outperforms Gurobi. The hybrid F&O and SA is observed to be the most computationally efficient approach. A representative scenario in the Indian setting presents further evidence of the model’s applicability.

Landscape urbanism in the context of the historical environment

Currently, most people live in cities, which leads to an increase in building density, a reduction in public spaces, and a violation of the ecological balance. Therefore, one of the main tasks facing architects and designers is to maintain ecological balance, which, in turn, contributes to the creation of more comfortable living conditions. This article examines the need for more effective integration of landscape urbanism techniques in the context of the historical urban environment. It is also proposed to take into account the methodology of urban space design based on the study of the environment, natural characteristics and interactions between them, economic, cultural and social aspects.

Tutorial on Molecular Latent Space Simulators (LSSs): Spatially and Temporally Continuous Data-Driven Surrogate Dynamical Models of Molecular Systems.

The inherently serial nature and requirement for short integration time steps in the numerical integration of molecular dynamics (MD) calculations place strong limitations on the accessible simulation time scales and statistical uncertainties in sampling slowly relaxing dynamical modes and rare events. Molecular latent space simulators (LSSs) are a data-driven approach to learning a surrogate dynamical model of the molecular system from modest MD training trajectories that can generate synthetic trajectories at a fraction of the computational cost. The training data may comprise single long trajectories or multiple short, discontinuous trajectories collected over, for example, distributed computing resources. Provided the training data provide sufficient sampling of the relevant thermodynamic states and dynamical transitions to robustly learn the underlying microscopic propagator, an LSS furnishes a global model of the dynamics capable of producing temporally and spatially continuous molecular trajectories. Trained LSS models have produced simulation trajectories at up to 6 orders of magnitude lower cost than standard MD to enable dense sampling of molecular phase space and large reduction of the statistical errors in structural, thermodynamic, and kinetic observables. The LSS employs three deep learning architectures to solve three independent learning problems over the training data: (i) an encoding of the high-dimensional MD into a low-dimensional slow latent space using state-free reversible VAMPnets (SRVs), (ii) a propagator of the microscopic dynamics within the low-dimensional latent space using mixture density networks (MDNs), and (iii) a generative decoding of the low-dimensional latent coordinates back to the original high-dimensional molecular configuration space using conditional Wasserstein generative adversarial networks (cWGANs) or denoising diffusion probability models (DDPMs). In this software tutorial, we introduce the mathematical and numerical background and theory of LSS and present example applications of a user-friendly Python package software implementation to alanine dipeptide and a 28-residue beta-beta-alpha (BBA) protein within simple Google Colab notebooks.

High-speed voltage level-up shifter with wide range conversion for system-on-chip applications

Abstract The purpose of this investigation is to demonstrate a level shifter circuit that enhances voltage switching capacity while simultaneously reducing latency by employing a cascaded current mirror, input, and output inverters. We improved our design using multi-threshold Complementary metal oxide semiconductor (MTCMOS) technology by adding stackable transistors, super-cutoff mechanisms, and cascaded techniques. The level shifting between voltage ranges of 0.3 V and 2.0 V is accomplished by the proposed level shifter. Our design has been fine-tuned to meet the needs of system-on-chip applications by meticulously optimizing performance characteristics like energy usage, latency, and space utilization. The proposed level shifter has an area of 6.936 µm2 and a power consumption of 17 nW, with a delay of 90.64 ps. The proposed work has been done on the Cadence Virtuoso Tool using 45 nm technology. Our suggested level shifter is more effective than earlier methods in terms of latency and conversion range. In addition to delivering considerable reductions in space and power consumption, the suggested design promises a 50-fold increase in operating speed.

Utilizing Binary Phase Segregation and Extrusion to Enhance the Electronic Properties of Graphene Nanocomposites.

Our goal in this study is to incorporate graphene nanoplatelets (GNPs) in a polymer blend of poly(lactic acid) (PLA) and isotactic polypropylene (iPP) to facilitate the dispersion of GNPs and use the morphology of phase segregation to create a pathway for concentrating GNPs to achieve percolation with lower GNP concentration. Investigating the interfacial properties between PLA/GNPs and iPP/GNPs, we noticed that iPP has a lower contact angle on GNPs compared to PLA on GNPs. This showed a great potential that the GNP are easily confined in iPP rather than in PLA domains or at the PLA/PP interfaces. Utilizing this property, as the PLA content increases, the iPP content decreases, resulting in a reduction of the available space for GNPs. This decrease in space leads to a higher chance for GNPs to accumulate together. Furthermore, through careful control of the concentration in the immiscible blend of PLA/PP, it is possible to achieve a bicontinuous structure. This structure facilitates the creation of a pathway for the fillers, allowing for the use of minimal GNPs while achieving optimal electrical conductivity properties. The PLA/PP/GNPs can be oriented by the shear forces coming from the nozzle that produce a higher performance.

Sustainable Development of Production–Living–Ecological Spaces: Insights from a 30-Year Remote Sensing Analysis

The rapid pace of urbanization and industrialization has reshaped land use patterns globally, particularly within the interconnected domains of ‘production, living, and ecological spaces’ (PLES). Understanding the spatiotemporal evolution of these spaces is crucial for guiding sustainable development. Although a number of previous studies have explored aspects of their dynamics and driving factors, further investigation is needed to fully understand their long-term spatiotemporal evolution and the broader influences of socio-economic and environmental forces. This study aims to fill that important gap by leveraging advanced remote sensing techniques to analyze PLES transformations over a 30-year period. Using Henan Province, China, as a testbed, this study applies high-resolution Landsat data, land use transition matrices, dynamic degree analysis, Principal Component Analysis (PCA), and multiple linear regressions to uncover trends and underlying drivers. The results reveal a substantial reduction in production spaces by 3394.62 km² steady growth in living spaces by 4459.41 km² and complex, non-linear changes in ecological spaces, which decreased by 1067.43 km². Key driving forces, such as economic growth, urbanization, and fiscal policies are identified and discussed. These insights provide a robust framework for sustainable land use planning, with broader implications for rapidly urbanizing regions worldwide.

Redundancy resolution of a variable base frame of a 3-DoF cable-driven serial chain by using an adaptive neuro-fuzzy controller

This paper presents a novel approach using adaptive neuro-fuzzy techniques to design controllers for planar cable-driven serial chain robots with variable configurations. The approach consists of two key components: (1) deriving dynamic models for cable-driven serial chain robots which are independent of their structure, and (2) adaptively determining the optimal cable connection points. Traditional methods face challenges in obtaining accurate dynamic equations for cable-driven serial chain robots with high degrees-of-freedom, hence neural networks are employed to estimate the model. In order to handle the variability in cable connection points, adaptive fuzzy methods are utilized. The proposed adaptive neuro-fuzzy controller algorithm introduces two new indices, namely cost-of-redundancy and degree-of-redundancy, to effectively address redundancy concerns. Additionally, the algorithm efficiently reduces the search space for finding the optimal configuration. Simulation results for a planar 3 degrees-of-freedom cable-driven serial chain robot using this algorithm showcase a noteworthy 42% reduction in cost-of-redundancy and an impressive 53.125% reduction in search space.

Joint feature selection and classification of low-resolution satellite images using the SAT-6 dataset

The modern industries of today demand the classification of satellite images, and to use the information obtained from it for their advantage and growth. The extracted information also plays a crucial role in national security and the mapping of geographical locations. The conventional methods often fail to handle the complexities of this process. so, an effective method is required with high accuracy and stability. In this paper, a new methodology named RankEnsembleFS is proposed that addresses the crucial issues of stability and feature aggregation in the context of the SAT-6 dataset. RankEnsembleFS makes use of a two-step process that consists of ranking the features and then selecting the optimal feature subset from the top-ranked features. RankEnsembleFS achieved comparable accuracy results to state-of-the-art models for the SAT-6 dataset while significantly reducing the feature space. This reduction in feature space is important because it reduces computational complexity and enhances the interpretability of the model. Moreover, the proposed method demonstrated good stability in handling changes in data characteristics, which is critical for reliable performance over time and surpasses existing ML ensemble methods in terms of stability, threshold setting, and feature aggregation. In summary, this paper provides compelling evidence that this RankEnsembleFS methodology presents excellent performance and overcomes key issues in feature selection and image classification for the SAT-6 dataset.

Risk factors for postoperative ileus after corrective spinal surgery: association with reduction in the retrocrural space area.

The objective of this study was to determine whether a reduction in the retrocrural space (RCS) area is a risk factor for postoperative ileus (POI) in patients with adult spinal deformity (ASD) treated with spinal corrective surgery. In total, 100 patients (mean age 67.5 ± 8.3 years, 9 males and 91 females) with ASD treated with spinal corrective surgery were included in this study. Spinal parameters, including thoracolumbar kyphosis (TLK), and RCS area were measured pre- and postoperatively. The change (Δ) in spinal parameters was calculated. The percent change between pre- and postoperative RCS areas was calculated as ΔRCS. Patients were identified as having POI if they exhibited both gastrointestinal symptoms and radiographic findings. Each parameter was compared between patients with and without POI. Multivariable logistic regression analysis was performed with development of POI as the dependent variable. The incidence of POI was 11.0%. The RCS area was significantly smaller in the POI group than in the non-POI group (p < 0.001). Multivariable logistic regression analysis revealed that ΔTLK and ΔRCS were risk factors for POI (p = 0.029 and p = 0.033, respectively). A reduction in the RCS area is a risk factor for the development of POI after corrective spinal surgery in patients with ASD. Overcorrection of the thoracolumbar junction should be avoided to prevent POI.