Optimal Distribution Research Articles

Evaluating the spatial accessibility and spatial layout optimization of HIV/AIDS healthcare services in Shandong Province, China

Improving access to HIV/AIDS healthcare services is of great concern to government and policymakers striving to strengthen overall public health. How to reasonably allocate HIV/AIDS healthcare resources and maximize the equality of access to healthcare services across subdistrict areas has become an urgent problem to be solved. However, there is limited research on this topic in China. It is necessary to evaluate spatial accessibility to improve the accessibility and equity of HIV/AIDS healthcare services. In this study, the improved multi-modal two-step floating catchment area (2SFCA) and inverted 2SFCA (i2SFCA) methods are used to measure the spatial accessibility of HIV/AIDS healthcare services and the crowdedness of the healthcare sites in Shandong Province, China. Then, the theoretical supply and the optimal spatial distribution of resources are calculated and visualized by minimizing the accessibility gaps between demand locations. This study showed that the spatial accessibility of HIV/AIDS service resources in Shandong Province was concentrated and unevenly distributed, and the accessibility scores in the marginal areas of prefecture-level cities were significantly lower than those in other areas. Regions with a large number of doctors had significantly higher levels of spatial accessibility. The ART accessibility scores in the southwest of Shandong Province were higher than those in other regions. As the travel friction coefficient increased, the accessibility scores formed an approximately circular cluster distribution centered on the healthcare sites in geographical distribution. More ART drugs needed to be supplied in marginal areas and more doctors were needed to work on HIV/AIDS in urban areas to address the spatial distribution imbalance of HIV/AIDS healthcare services. This study profoundly analyzed the spatial accessibility of HIV/AIDS healthcare services and provided essential references for decision-makers. In addition, it gives a significant exploration for achieving the goal of equal access to HIV/AIDS healthcare services in the future.

Location strategy for logistics distribution centers utilizing improved whale optimization algorithm

Abstract An enhanced whale optimization algorithm is introduced to address the challenges associated with the logistics distribution center location problem, aiming to overcome the traditional heuristic algorithm’s susceptibility to local optima and enhance the overall efficiency of the logistics system. This improvement involves the integration of stochastic sinusoidal inertia weights and a variational strategy to augment the global search capability and convergence accuracy of the conventional algorithm. The results of experimental simulations indicate that the upgraded whale algorithm exhibits superior computational performance compared to other heuristic algorithms. Furthermore, it demonstrates the ability to effectively compute optimal distribution center locations, thereby enhancing the delivery efficiency of the logistics distribution system.

High-Speed Fluorescence Imaging Corroborates Biological Data on the Influence of Different Nozzle Types on Cell Spray Viability and Formation

High-Speed Fluorescence Imaging Corroborates Biological Data on the Influence of Different Nozzle Types on Cell Spray Viability and Formation

Simulation of decision-making processes regarding the formation of an invest-ment portfolio using IT

Simulation of decision-making processes regarding the formation of an invest-ment portfolio using IT

Effective timing of hyaluronate gel injection in image-guided adaptive brachytherapy for uterine cervical cancer: a proposal of the 'adjusted dose score'.

Hyaluronate gel injection (HGI) in the rectovaginal septum and vesicovaginal septum is effective in the setting of high-dose-rate image-guided adaptive brachytherapy (IGABT) for cervical cancer. We aimed to retrospectively investigate optimal conditions for HGI to achieve optimal dose distribution with a minimum number of HGI. We classified 50 IGABT plans of 13 patients with cervical cancer who received IGABT both with and without HGI in the rectovaginal septum and vesicovaginal septum into the following two groups: plan with (number of plans = 32) and plan without (number of plans = 18) HGI. The irradiation dose parameters of high-risk clinical target volume (CTVHR) and organs at risk per fraction were compared between these groups. We also developed the adjusted dose score (ADS), reflecting the overall irradiation dose status for four organs at risk and CTVHR in one IGABT plan and investigated its utility in determining the application of HGI. HGI reduced the maximum dose to the most exposed 2.0cm3 (D2.0 cm3) of the bladder while increasing the minimum dose covering 90% of CTVHR and the percentage of CTVHR receiving 100% of the prescription dose in one IGABT plan without causing any associated complications. An ADS of ≥2.60 was the optimum cut-off value to decide whether to perform HGI. In conclusion, HGI is a useful procedure for improving target dose distribution while reducing D2.0 cm3 in the bladder in a single IGABT plan. The ADS can serve as a useful indicator for the implementation of HGI.

Optimal distribution grid allocation of reactive power with a focus on the particle swarm optimization technique and voltage stability

A structured approach to managing reactive power is imperative within the context of power systems. Among the restructuring initiatives in the electrical sector, power systems have undergone delineation into three principal categories: generation, transmission, and distribution entities, each of which is overseen by an independent system operator. Notably, active power emerges as the predominant commodity transacted within the electrical market, with the autonomous grid operator assuming the responsibility of ensuring conducive conditions for the execution of energy contracts across the transmission infrastructure. Ancillary services, comprising essential frameworks for energy generation and delivery to end-users, encompass reactive power services pivotal in the regulation of bus voltage. Of particular significance among the array of ancillary services requisite in a competitive market milieu is the provision of adequate reactive power to uphold grid safety and voltage stability. A salient impediment to the realization of energy contracts lies in the inadequacy of reactive power within the grid, which poses potential risks to its operational safety and voltage equilibrium. The optimal allocation of the reactive power load is predicated upon presumptions of consistent outcomes within the active power market. Under this conceptual framework, generators are afforded continual compensation for the provision of reactive power indispensable for sustaining their active energy production endeavors.

Intelligent control algorithm for dynamic positioning control system

Introduction: The dynamic positioning system resists the environmental forces such as wind, wave and current acting on the ship through the thruster, so that the ship can remain in the position required by the sea level as much as possible, and the operation is very convenient. But its current dynamic positioning ability can not meet people's needs.Methods: A Kalman filter based on untracked optimization was designed for dynamic positioning control system. Then the intelligent control algorithm is designed for the dynamic positioning top-level controller and thrust optimal distribution controller, which occupy an important position in the system, namely the adaptive weight variation particle swarm optimization algorithm and thrust optimal distribution algorithm.Results and Discussion: The average position error of three degrees of freedom after filter processing is 1.53 m. Compared with other mainstream controllers, the mean root error of controllers based on adaptive weight variation particle swarm optimization in environment A and B is 2.295 and 1.8 m, respectively. In environment C, the controller based on thrust optimization allocation algorithm can get the optimal solution when the full rotary thruster reaches the 7 s and the channel thruster reaches the 4 s. The thrust exclusion zone is crossed at 46 s in environment D. In the dynamic positioning capability curve of the system, the experimental hull can balance the different environmental loads at all angles. In summary, the intelligent control algorithm proposed in this paper can effectively improve the positioning ability of the dynamic positioning control system and meet the needs of people for ship navigation today.

Reverse engineering morphogenesis through Bayesian optimization of physics-based models

Morphogenetic programs coordinate cell signaling and mechanical interactions to shape organs. In systems and synthetic biology, a key challenge is determining optimal cellular interactions for predicting organ shape, size, and function. Physics-based models defining the subcellular force distribution facilitate this, but it is challenging to calibrate parameters in these models from data. To solve this inverse problem, we created a Bayesian optimization framework to determine the optimal cellular force distribution such that the predicted organ shapes match the experimentally observed organ shapes. This integrative framework employs Gaussian Process Regression, a non-parametric kernel-based probabilistic machine learning modeling paradigm, to learn the mapping functions relating to the morphogenetic programs that maintain the final organ shape. We calibrated and tested the method on Drosophila wing imaginal discs to study mechanisms that regulate epithelial processes ranging from development to cancer. The parameter estimation framework successfully infers the underlying changes in core parameters needed to match simulation data with imaging data of wing discs perturbed with collagenase. The computational pipeline identifies distinct parameter sets mimicking wild-type shapes. It enables a global sensitivity analysis to support the regulation of actomyosin contractility and basal ECM stiffness to generate and maintain the curved shape of the wing imaginal disc. The optimization framework, combined with experimental imaging, identified that Piezo, a mechanosensitive ion channel, impacts fold formation by regulating the apical-basal balance of actomyosin contractility and elasticity of ECM. This workflow is extensible toward reverse-engineering morphogenesis across organ systems and for real-time control of complex multicellular systems.

Кількісне оцінювання функціональної ефективності мережевих структур за аналізом чутливості оптимізаційної моделі до зовнішніх впливів

The real network organizational structure is constantly under the influence of external influences, which can cause unexpected consequences due to the cascading spread of relevant disturbances directed at critically important parameters. Parametric sensitivity analysis is the latest computational procedure, the results of which provide the researcher with an idea and quantitative assessment of the appropriate response of the structure in the form of an analytical platform as a composition and interaction of scenario analysis, cascade process development and optimization modeling technology. The main task is timely adaptation to expected changes in business analytics, oriented to optimal resources distribution. The generalized minimum cost problem is a typical example of widespread problems of optimal distribution and use of limited, in particular, energy resources, of any nature and purpose, using the scientific management and analysis of decisions, in its network model attention is paid to the potentials of nodes. The maximum flow problem is an example of the active and effective application of methods and models of flows optimization, where the processes and objects used a network organization, the energy systems and complexes investigated, where arc parameters are critical in its model. In the article, for these classic optimization problems, mathematical and spreadsheet models were built on concrete examples. Direct and dual mathematical programming problems were solved, the following stages were carried out - the organization of a computer experiment and the construction of an analytical platform for evaluating the behavior of the network structure under the influence of external influences and disturbances as their consequences. To take into account the specific conditions of real objects with a network structure at the stage of modification, the model supplemented with appropriate restrictions and correction of input data sets. The obtained results should be useful for planning and management personnel and top-management for discussion and decision-making regarding, for example, the optimal placement of energy equipment, the weighted distribution of energy flows in the "source-sink" system or the appointment of executors for work, automated project management using project networks, etc. Keywords: optimization modeling, maximum network flow, minimum cost flows, network organizational structures, decision making, spreadsheet modeling and analytics.

Ferroelectric nematic phase in the system of perfectly aligned cylindrically symmetric rods

ABSTRACT The recent experimental discovery of ferroelectric and splay nematic phases has sparked interest in comprehending the crucial molecular features necessary to stabilise these innovative structures. This study advances the ongoing discourse by investigating the significance of both molecular elongation and the distribution of molecular dipoles along the main molecular axis. Using Density Functional Theory, we have established that a molecular shape characterised by cylindrical symmetry and the presence of strong parallel dipoles along the symmetry axis can lead to the self-assembly of a ferroelectric nematic, which is more stable than the conventional uniaxial nematic phase. Additionally, for simple D ∞ h -symmetric shapes and two dipoles per molecule we provide criteria for achieving an optimal dipole distribution along the molecular axis.

IoT ENBALED SMART AQUARIUM FOR MONITORING THROUGH SENSORS

The development of an IoT-enabled smart aquarium system offering real-time monitoring and automated control of crucial environmental parameters. The core of the system lies in the ESP8266 microcontroller, facilitating seamless internet connectivity and data processing. Water quality assessment is achieved through a turbidity sensor, detecting suspended particles indicative of potential pollution. Environmental conditions are monitored using a DHT11 sensor, providing data on both temperature and humidity – essential factors for maintaining a healthy habitat for specific fish species. For automated lighting control mimicking natural day/night cycles and preventing excessive algae growth, a Light-Dependent Resistor (LDR) is implemented. The LDR adjusts aquarium lighting based on ambient light levels. Finally, a relay module controls the operation of a water pump, ensuring optimal water circulation and distribution of nutrients throughout the aquarium. While an I2C LCD connected to an Arduino Nano provides local data visualization within the aquarium setup, the ESP8266 transmits sensor readings to a user-friendly platform accessible remotely through a mobile application or web interface. This allows for convenient real-time monitoring of water quality, temperature, humidity, and lighting conditions. The system can be further programmed to generate alerts when critical parameters deviate from the ideal range, enabling proactive user intervention to maintain a healthy environment. This project demonstrates the potential of IoT technology to create a more automated, efficient, and user-friendly system for maintaining a thriving aquatic ecosystem. KEYWORDS: IoT (Internet of Things), Smart Aquarium, ESP8266, Turbidity Sensor, DHT11 Sensor, LDR(Light-Dependent Resistor), Relay Module, Water Pump, I2C LCD, Arduino Nano, Real-Time Monitoring, Automated Control, Remote Access, Mobile Application, Web Interface, Water Quality, Temperature, Humidity, Light Control, Aquatic Environment.

Effect of vegetation restoration type and topography on soil water storage and infiltration capacity in the Loess Plateau, China

Vegetation restoration effectively mitigates soil erosion and improves soil properties, but in drylands, it can lead to soil water scarcity and disrupt ecological balance. Until now, limited research has examined the combined effects of vegetation restoration type and topography on soil water and infiltration in the Loess Plateau of China. Here, we investigated soil water content (SWC), soil water storage (SWS), soil infiltration rates and related soil properties in typical forestland, shrubland, and grassland across five slope positions on steep slopes, and compared them with cropland. The results indicated that vegetation restoration significantly decreased SWS compared to cropland. Across the entire 0–500 cm depth, reductions were observed at 30.9 %, 33.2 %, and 20.8 % for forestland, shrubland, and grassland, respectively, with forestland and shrubland demonstrating a more pronounced decrease, particularly in deeper soil layers. Shrubland had the lowest SWS on the upper, middle, and top slopes, while forestland showed the lowest SWS on the lower and foot slopes. In terms of infiltration, vegetation restoration generally increased steady infiltration rate but resulted in reduced initial and mean infiltration rates (except for shrubland). Shrubland had the highest infiltration rates, followed by forestland, while grassland had the lowest rates. Soil infiltration rates decreased gradually along the slope, indicating that the impact of vegetation restoration on soil infiltration improvement weakened with lower slope positions. Vegetation restoration type had a stronger direct and total effects on infiltration than slope position. Yet, the indirect influence of slope position through clay content and SWC outweighed that of vegetation restoration type. Our findings highlight the comprehensive assessment of the eco-hydrological effects of vegetation restoration is essential for identifying suitable species and designing optimal spatial distribution, ultimately maximizing the ecological benefits of the restoration. These insights can offer valuable guidance for ecological restoration and land management in fragile dryland ecosystems.

Quadratic hyper-surface kernel-free large margin distribution machine-based regression and its least-square form

ε-Support vector regression (ε-SVR) is a powerful machine learning approach that focuses on minimizing the margin, which represents the tolerance range between predicted and actual values. However, recent theoretical studies have highlighted that simply minimizing structural risk does not necessarily result in well margin distribution. Instead, it has been shown that the distribution of margins plays a more crucial role in achieving better generalization performance. Furthermore, the kernel-free technique offers a significant advantage as it effectively reduces the overall running time and simplifies the parameter selection process compared to the kernel trick. Based on existing kernel-free regression methods, we present two efficient and robust approaches named quadratic hyper-surface kernel-free large margin distribution machine-based regression (QLDMR) and quadratic hyper-surface kernel-free least squares large margin distribution machine-based regression (QLSLDMR). The QLDMR optimizes the margin distribution by considering both ε-insensitive loss and quadratic loss function similar to the large-margin distribution machine-based regression (LDMR). QLSLDMR aims to reduce the cost of the computing process of QLDMR, which transforms inequality constraints into an equality constraint inspired by least squares support vector machines (LSSVR). Both models combined the spirit of optimal margin distribution with kernel-free technique and after simplification are convex so that they can be solved by some classical methods. Experimental results demonstrate the superiority of the optimal margin distribution combined with the kernel-free technique in robustness, generalization, and efficiency.

Dose averaged linear energy transfer optimization for large sacral chordomas in carbon ion therapy.

Carbon ion beams are well accepted as densely ionizing radiation with a high linear energy transfer (LET). However, the current clinical practice does not fully exploit the highest possible dose-averaged LET (LETd) and, consequently, the biological potential in the target. This aspect becomes worse in larger tumors for which inferior clinical outcomes and corresponding lower LETd wasreported. The vicinity to critical organs in general and the inferior overall survival reported for larger sacral chordomas treated with carbon ion radiotherapy (CIRT), makes the treatment of such tumors challenging. In this work it was aimed to increase the LETd in large volume tumors while maintaining the relative biological effectiveness (RBE)-weighted dose, utilizing the LETd optimization functions of a commercial treatment planning system (TPS). Ten reference sequential boost carbon ion treatment plans, designed to mimic clinical plans for large sacral chordoma tumors, were generated. High dose clinical target volumes (CTV-HD) larger than were considered as large targets. The total RBE-weighted median dose prescription with the local effect model (LEM) was in 16 fractions (nine to low dose and seven to high dose planning target volume). No LETd optimization was performed in the reference plans, while LETd optimized plans used the minimum LETd (Lmin) optimization function in RayStation2023B. Three different Lmin values were investigated and specified for the seven boost fractions: , and . To compare the LETd optimized against reference plans, LETd and RBE-weighted dose based goals similar to and less strict than clinical ones were specified for the target. The goals for the organs at risk (OAR) remained unchanged. Robustness evaluation was studied for eight scenarios ( range uncertainty and setup uncertainty along the main three axes). The optimization method with resulted in an optimal LETd distribution with an average increase of (and ) in the CTV-HD by ( ) (and ( )), without significant difference in the RBE-weighted dose. By allowing over- and under-dosage in the target, the (and ) can be increased by ( ) (and ( )), using the optimization parameters . The pass rate for the OAR goals in the LETd optimized plans was in the same level as the reference plans. LETd optimization lead to less robust plans compared to referenceplans. Compared to conventionally optimized treatment plans, the LETd in the target was increased while maintaining the RBE-weighted dose using TPS LETd optimization functionalities. Regularly assessing RBE-weighted dose robustness and acquiring more in-room images remain crucial and inevitable aspects duringtreatment.

Probabilistic unitary synthesis with optimal accuracy

The purpose of unitary synthesis is to find a gate sequence that optimally approximates a target unitary transformation. A new synthesis approach, called probabilistic synthesis, has been introduced, and its superiority has been demonstrated over traditional deterministic approaches with respect to approximation error and gate length. However, the optimality of current probabilistic synthesis algorithms is unknown. We obtain the tight lower bound on the approximation error obtained by the optimal probabilistic synthesis, which guarantees the sub-optimality of current algorithms. We also show its tight upper bound, which improves and unifies current upper bounds depending on the class of target unitaries. These two bounds reveal the fundamental relationship of approximation error between probabilistic approximation and deterministic approximation of unitary transformations. From a computational point of view, we show that the optimal probability distribution can be computed by the semidefinite program (SDP) we construct. We also construct an efficient probabilistic synthesis algorithm for single-qubit unitaries, rigorously estimate its time complexity, and show that it reduces the approximation error quadratically compared with deterministic algorithms.

Isogeometric topology optimization of structures using the overweight approach

In this paper, a 2D isogeometric formulation of the material distribution for structural topology optimization considering minimum weight and local stress constraints using the overweight approach is proposed. The aim of this isogeometric formulation is to provide solutions with high spatial definition using a lower number of design variables in comparison with the formulations previously developed to define the material layout. Despite of this, an important number of local stress constraints has to be considered in the solution of the problem. For this purpose, an Overweight Constraint is used to consider all of them. The structural analysis is performed by means of the Isogeometric Analysis (IGA) and the distribution of material is modeled by means of quadratic B-splines. Moreover, the optimization is addressed by means of the Sequential Linear Programming algorithm (SLP) that is driven by the information provided by a full first-order sensitivity analysis extension of the IGA formulation. Finally, the proposed formulation is tested by means of some benchmark problems, and the results show that the isogeometric formulation provides solutions with high spatial definition. A comparison with a Finite Element Method (FEM) topology optimization formulation is included.

La promoted Ni0-Niδ+ synergistic interaction for rapid and deep hydrogenation of liquid organic hydrogen carriers

The development of cost-effective, efficient and stable catalysts for liquid organic hydrogen carriers (LOHC) is crucial for large-scale hydrogen storage. Herein, we introduced La species into Ni/Al2O3 via co-precipitation method, yielding La-Ni/Al2O3 catalysts. Such catalysts demonstrated exceptional performance and durability in hydrogenating various LOHC candidates. Their hydrogenation capabilities surpassed most state-of-the-art and commercial catalysts, attributing to the homogenous formation of ultrafine nanoparticles induced by La species, prevention of producing unfavorable components, optimal microstructural distributions and balanced dispersion of acid sites. DFT calculations indicated that Ni-NiO(002) interface played crucial roles in modulating the electron deficiency of surface Ni atoms. The synergistic effect of Ni0-Niδ+ shifts the d-band center of Ni closer to the Fermi level, enhancing the catalyst’s adsorption capacity of intermediates and achieving higher activity toward activation of benzene and pyrrole ring, therefore leading to rapid and deep LOHC hydrogenation. The resultant catalysts exhibited great potentials for industrial applications.

The Importance of Ground Control Points In A Photogrammetric Workflow

Abstract This study investigates the optimal distribution and pattern of ground control points (GCPs) in aerial photogrammetric projects. Aerial triangulation (AT), also known as bundle adjustment, is the fundamental step in refining 3D reconstruction models and camera positions, thereby minimizing reprojection errors. The study utilizes data from a national project in Romania, employing high-resolution aerial images acquisition using photogrammetric sensors. The project has rigorous requirements of ground control points (GCP) placement and field measurements using GNSS and geometric leveling techniques. The study employs various scenarios, manipulating the number and distribution of GCPs, to assess their influence on planimetric and altimetric accuracy. Results indicate that the configuration and number of GCPs significantly affect the accuracy of photogrammetric products, such as dense image point clouds, digital surface models, and orthophotos. Moreover, the study underscores the importance of precise GCP determination methods, especially in regions lacking a precise gravimetric geoid model. In scenarios with inadequate GCP coverage the outcomes have inferior quality, emphasizing the critical role of GCPs in ensuring the quality of photogrammetric products. Overall, the research gives a clear view on the best placement patterns of GCPs and their influence on AT process evaluation performed in check points (CHKs).

Optimization models for photosynthetic bioenergy generation in building façades

Façade design optimization has a significant impact on promoting resilient solar harvesting methods in urban areas. This study introduces a new framework toward the optimal inclusion of microalgae photobioreactors in different façade geometries by developing a detailed chemical kinetics model aided by optimization techniques. Although algal bioreactive façades in recent literature are restricted to flat façades, the present study reveals that bioenergy generation can be enhanced by up to 29.6 % through optimized folded façades. Hooke-Jeeves algorithm demonstrates remarkable computational efficiency in optimizing such façades, achieving the optimal outcome with only 88 function evaluations. However, the Hooke-Jeeves algorithm has limitations when applied to free-form façades by displaying noticeable instability and reporting an optimal value approximately 40 % lower than Genetic Algorithms. Hooke-Jeeves algorithm has the possibility to conclude misleading outcomes as optimal points. This is mitigated by applying multiple initial points and verifying the outcomes with graphical optimization. In terms of energy performance, free-form façades have higher irradiation of up to 210 W/m2 compared to 180 W/m2 for flat façades. Optimal distribution of renewable modules on free-form façades increases energy generation by 14.3 %. South-facing façades produce the highest bioenergy at 2.4 W/m2, while west and east orientations generate the lowest at 0.8 W/m2.

LRFE-CL: A self-supervised fusion network for infrared and visible image via low redundancy feature extraction and contrastive learning

Aiming to amalgamate the distinct yet complementary attributes inherent to infrared and visible spectra, Infrared and Visible Image Fusion (IVIF) endeavors to preserve an optimal intensity distribution while curtailing the superfluous data within the resultant image. Nonetheless, it is an incontrovertible reality that the majority of extant methods are prone to engendering an undesirable redundancy of features, in addition to a marked degradation throughout the fusion process. To address the issue, we proposed a self-supervised fusion network for infrared and visible image via low redundancy feature extraction and contrastive learning, termed as LRFE-CL. Specifically, the Low Redundancy Feature Extraction Network (SSFN-LRAE) has been developed to effectively extract low-redundancy features from infrared and visible images using the self-supervised strategy and the Channel Shift Attention Module (CSAM). Subsequently, the Contrastive Fusion Decoder (CFDN) is designed to fuse multi-layer features extracted from the SSFN-LRAE. To prevent feature degradation, a novel regularized contrastive loss mechanism targeting multi-layer features has been formulated to optimize our CFDN, ensuring the fusion of comprehensive texture details and brightness information. Our comprehensive experimental results unequivocally demonstrate the exceptional performance of our method, both in terms of visual effects and fusion performance.