Specific Constraints Research Articles

A systemic analysis of container terminal layouts

The paper provides a systemic analysis of the layout characteristics of a geodatabase comprised of a large sample of 331 global container terminals. Despite the propensity towards terminal standardization that can be expected from containerization, container terminals demonstrate a substantial diversity in measurable attributes such as perimeter, terminal surface, yard surface, and berth length. This is mainly attributed to site characteristics constraining terminal design and operations with clusters arranged along terminal rectangularity level. A further observation concerns the propensity of container terminals to be oriented longitudinally. Geographical factors remain important, including whether the terminal design is a reconversion of an older facility, an adaptation to specific site constraints, or a new land reclamation.

Multi-Unmanned Aerial Vehicle Path Planning Based on Improved Nutcracker Optimization Algorithm

For the multi-UAV path planning problem, environmental modeling and an improved swarm intelligence-based optimization algorithm are discussed in this paper. Firstly, to align with reality, specific constraints of UAVs in motions, attitudes and altitudes, real-world threats such as radars and no-fly zones, and inter-UAV collisions are considered. Thus, multi-UAV path planning is transformed into a multi-objective constrained optimization problem. Accordingly, an improved nutcracker optimization algorithm is proposed to solve this problem. Through initializing with logistic chaotic mapping and the lens imaging inverse learning strategy, a more fit elite initialization population is produced to increase the efficiency of path planning. Furthermore, by adjusting adaptive parameters and integrating an improved sine-cosine search strategy, a balance between global exploration capability and local exploitation capability during path planning is achieved. Experimental results show that the improved nutcracker optimization algorithm surpasses other algorithms with respect to both convergence speed and convergence value, making it an effective method for multi-UAV path planning.

Going beyond the surface: the nexus between decent work, faculty engagement and intrinsic religiosity in Pakistan’s higher education institutions

PurposeThis study examines the impact of five dimensions of decent work on the faculty engagement in Pakistan’s higher education institutions. Furthermore, it examines the moderating influence of intrinsic religiosity on the associations above.Design/methodology/approachThis research employed a cross-sectional approach to collect data from 542 faculty members working with higher education institutions through electronic and in-person questionnaire administration.FindingsThe findings indicate that safe interpersonal working conditions, opportunities for free time and rest, adequate compensation, and the availability of healthcare services significantly positively impact the level of work engagement among faculty members. Additionally, the research revealed that intrinsic religiosity reinforces the previously established significant associations.Research limitations/implicationsThe research acknowledges specific constraints that could impact the applicability of its findings, including the utilization of a cross-sectional methodology, the dependence on self-reported information, and the possibility of sample biases. Subsequent investigations may rectify these constraints to provide a more all-encompassing comprehension of the subject matter.Practical implicationsThe findings possess practical significance for the management of HEIs in cultivating an ideal working atmosphere for faculty members. Ensuring adequate compensation, secure working environments and healthcare accessibility is underscored to enhance faculty engagement. Furthermore, acknowledging the significance of intrinsic religiosity can improve faculty engagement.Originality/valueThis research contributes to the current body of literature by investigating the complex relationship among decent work, faculty engagement, and intrinsic religiosity in the specific context of higher education institutions in Pakistan.

Adaptafood: an intelligent system to adapt recipes to specialised diets and healthy lifestyles

This paper presents AdaptaFood, a system to adapt recipes to specific dietary constraints. This is a common societal issue due to various dietary needs arising from medical conditions, allergies, or nutritional preferences. AdaptaFood provides recipe adaptations from two inputs: a recipe image (a fine-tuned image-captioning model allows us to extract the ingredients) or a recipe object (we extract the ingredients from the recipe features). For the adaptation, we propose to use an attention-based language sentence model based on BERT to learn the semantics of the ingredients and, therefore, discover the hidden relations among them. Specifically, we use them to perform two tasks: (1) align the food items from several sources to expand recipe information; (2) use the semantic features embedded in the representation vector to detect potential food substitutes for the ingredients. The results show that the model successfully learns domain-specific knowledge after re-training it to the food computing domain. Combining this acquired knowledge with the adopted strategy for sentence representation and food replacement enables the generation of high-quality recipe versions and dealing with the heterogeneity of different-origin food data.

Optimizing nature-based solutions for urban flood risk mitigation: A multi-objective genetic algorithm approach in Gdańsk, Poland.

Nature-based Solutions (NbS) have emerged as a sustainable approach to managing flood risks by enhancing natural water retention and reducing surface runoff in urban areas. As climate change and rapid urbanization exacerbate flood hazards, optimizing the spatial deployment of NbS is crucial for improving urban resilience and mitigating flood impacts. This study presents a comprehensive optimization framework for the spatial allocation of fourteen different NbS types aimed at mitigating urban flood risks in Gdańsk, Poland. Leveraging a genetic algorithm alongside the Urban Flood Risk Mitigation (UFRM) model of the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) software suite, we identified areas of the city most vulnerable to pluvial flooding and optimized NbS placement to maximize water retention and reduce runoff. The optimization process balanced multiple objectives, including minimizing implementation costs and maximizing water retention capacity, ensuring that the solutions are both economically feasible and environmentally effective. Three distinct scenarios were proposed: a cost-effective solution, a high-retention solution, and a balanced solution, offering urban planners a range of strategies to address flood risks based on their specific priorities and constraints. Results demonstrated considerable variations in water retention effectiveness across different NbS configurations, with denser urban regions showing the most significant improvements from targeted interventions. The optimized placement of NbS resulted in estimated total water retention improvement of approximately 15.5% for the best solution considered. These findings provide valuable insights for integrating NbS into urban flood management strategies, enhancing citywide resilience, sustainability, and long-term flood mitigation.

Dynamic interplay of anisotropy and matter-geometry coupling in stellar structures: A comprehensive study on the stability of pulsars 4U 1820-30 and LMC X-4

Dynamic interplay of anisotropy and matter-geometry coupling in stellar structures: A comprehensive study on the stability of pulsars 4U 1820-30 and LMC X-4

177. AMR-Informed Antibiotic Use Metrics

Abstract Background Antimicrobial resistance (AMR) poses a significant public health threat, and efforts are underway to monitor and reduce unnecessary antibiotic usage. The Standardized Antibiotic Administration Ratio (SAAR) measures unit and hospital consumption of antibiotics by adjusting for facility characteristics. However, SAAR currently does not consider the prevalence rates of AMR that could justify or challenge antibiotic usage. Our objective is to develop an optimization function that identifies the best antibiotic choice for sepsis caused by Gram-negative bacilli (GNB). Optimization functions determine the optimal solution given specific constraints. Methods This observational cohort study included all adult patients meeting the CDC sepsis criteria and admitted to any of the 10 BJC acute care hospitals between 2016 and 2021. We focused on GNB isolated from clinical cultures, specifically the 17 most common species. We selected 15 commonly used antibiotics with activity against GNB (Table 1). Initially, we calculated the antibiotic weights to determine the relative coverage of each antibiotic, expressed as the ratio of GNB susceptible to all cultured GNB. The optimization function then constrained the optimal choice to the antibiotic with the lowest weight that still exhibited activity against the ultimately isolated strain. The resulting optimal antibiotic weights were averaged per hospital and study year and reported as a ratio to the weights of antibiotics used, akin to SAAR. Results Our study included 11,515 GNB cultures obtained from 9,531 patients. GNB were isolated from urine (40.5%), blood (20.5%), and respiratory specimens (20.3%). We present the antibiotic weights in Table 1, along with the representative ratios of empiric and directed observed-to-optimal antibiotic weights across BJC hospitals during the study period (Figs 1 and 2). Additionally, we contrast the SAAR trends for the same hospitals in Fig. 3. Conclusion Our optimization function selects the narrowest spectrum antibiotic and facilitates comparisons in antibiotic spectrum usage while considering existing AMR dynamics. It provides actionable insights and complements SAAR's metric of antibiotic duration. Disclosures M Cristina Vazquez Guillamet, MD, BNGO: Stocks/Bonds (Public Company)|Carisma Therapeutics: Stocks/Bonds (Public Company)|Ocugen: Stocks/Bonds (Public Company)

Assessing in-vitro models for microglial development and fetal programming: a critical review

This review evaluates in-vitro models for studying how maternal influences during pregnancy impact the development of offspring microglia, the immune cells of the central nervous system. The models examined include primary microglia cultures, microglia cell lines, iPSC-derived microglia, PBMC-induced microglia-like cells, 3D brain organoids derived from iPSCs, and Hofbauer cells. Each model is assessed for its ability to replicate the in-vivo environment of the developing brain, with a focus on their strengths, limitations, and practical challenges. Key factors such as scalability, genetic and epigenetic fidelity, and physiological relevance are highlighted. Microglia cell lines are highly scalable but lack genetic and epigenetic fidelity. iPSC-derived microglia provide moderate physiological relevance and patient-specific genetic insights but face operational and epigenetic challenges inherent to reprogramming. 3D brain organoids, derived from iPSCs, offer an advanced platform for studying complex neurodevelopmental processes but require extensive resources and technical expertise. Hofbauer cells, which are fetal macrophages located in the placenta and share a common developmental origin with microglia, are uniquely exposed to prenatal maternal factors and, depending on fetal barrier maturation, exhibit variable epigenetic fidelity. This makes them particularly useful for exploring the impact of maternal influences on fetal programming of microglial development. The review concludes that no single model comprehensively captures all aspects of maternal influences on microglial development, but it offers guidance on selecting the most appropriate model based on specific research objectives and experimental constraints.

Ecopoetic encounters: unsettling anthropocentric assumptions via constraint-based anthologethnography

ABSTRACT The multiform ecological crises of the Anthropocene raise a need to overcome western ideologies of human exceptionalism and anthropocentrism, and to revive multispecies connectivities towards more viable ways of living and learning from beyond-human kin. This article reports on a creative writing research experiment wherein eighteen poets spread across the UK, HKSAR, Korea, and Australia sought to discover and shift our own unwittingly internalised limitations of anthropocentrism via constraint-based ecopoetry and anthologethnography (cultural inquiry via anthologising) Towards this aim, we begin with a discussion of the ecological and ideological problems at hand. We then consider ecopoetry, constraint-based poetry, and anthologethnography. Our discussion of findings relays three key themes: the persistence of anthropocentric thinking despite our attempts to overcome it; the insights gained despite these limitations; and the new problems and questions that arose via our explorations. Ultimately, we note that the specific constraints we used were problematic, but could be revised for future inquiries. We argue for the value in further exploring potentials of constraint-based writing in group situations for practices of inquiry in research and pedagogy.

Trustworthiness analysis of heuristic routing algorithm with special constraints for autonomous vehicles in surveillance and coverage operations

This article aims to develop a fast, trustworthy routing algorithm for autonomous vehicles without requiring learning data, where the routing problem with special constraints and features is an extended version of the orienteering problem. For this, firstly, the method for selecting trustworthiness requirements is discussed to map relevant trustworthiness methods to stages in the system lifecycle. Then, a heuristic approach is proposed to automate mission planning within a limited time. Routing is handled through a greedy algorithm that selects tasks based on their distance to a baseline path and score. B-spline path smoothing is implemented for smooth and continuous routes. The results are examined in terms of robustness, explainability, transparency, reproducibility, and non-technical requirements. Scenarios with extreme parameters are generated for validation and testing. The algorithm demonstrates promising performance and fulfils the selected trustworthiness requirements, indicating that classical techniques remain significant candidates for fast, reliable, and unsupervised routing solutions.

Cardiac Exposure Related to Adjuvant Radiotherapy in Patients Affected by Thymoma: A Dosimetric Comparison of Photon or Proton Intensity-Modulated Therapy.

Radiotherapy for thymoma is delivered post-operatively in selected cases. Given the particular location of the thymic bed and the excellent prognosis, late cardiac toxicities may be an issue. The purpose of this retrospective dosimetric study is to investigate whether intensity-modulated proton beam therapy (IMPT) compared to photon therapy could better spare cardiac substructures, given prespecified dose constraints. We retrospectively selected patients treated with adjuvant radiotherapy for thymoma in our institution. We manually contoured fourteen cardiac substructures (CSs), with the supervision of a team of cardioradiologists. The photon-based plans were re-optimized in adherence to the volumetric modulated arc therapy (VMAT) technique with specific dose constraints for the new contoured structures. The proton-based plans were optimized in adherence to intensity-modulated proton therapy (IMPT) using the beam spot scanning technique. Twenty-nine patients treated with adjuvant radiotherapy with a prescribed dose of 50 Gy in 25 daily fractions for radically resected thymoma were selected. IMPT demonstrated better sparing of most cardiac substructures in terms of Dmax, Dmean and V5Gy. Finally, IMPT plans more easily achieved the proposed dose constraints. Cardiac substructures can be successfully spared with IMPT. Clinical studies are needed to establish a relationship between dose parameters and the development of cardiac events.

Novel Design of Wrist With Gripper: Type Synthesis of a 2R1T Parallel Mechanism Possessing One Actuation Degree-of-Freedom

Abstract The parallel mechanism (PM) with a configurable movable platform (CMP) holds the potential for achieving a high-response speed and high-stiffness integrated design of a gripper-wrist system. The motion of two rotations and one translation (2R1T), closely resembling the movement of a human wrist joint, is highly complex. Therefore, research on 2R1T PM with CMP remains unexplored. Additionally, a systematic method for constructing PM with CMP is also lacking. This paper proposes fundamental design principles for PM with CMP. The topological arrangements of 2R1T PM with CMP are classified according to the number of limbs, their types, and the CMP structure. The basic constraint conditions and the redundant constraint conditions of 2R1T PM are analyzed. By establishing the relationship between finite motions, joint geometric parameters, and constraint screws, a series of limb structures that provide specific constraints in finite motions are constructed. Several configuration examples of gripper-wrist mechanism are provided, which can serve as a guide for subsequent researches. The design approach of CMP is discussed. The performance advantages of such mechanisms are validated through simulation-based comparisons.

Comparison of Different Nanomaterials in Anode Materials of Lithium Battery

The increasing demand for sustainable energy necessitates the enhancement of lithium-ion battery technology, especially in the advancement of superior anode materials. Contemporary lithium-ion batteries exhibit specific constraints, including comparatively poor energy density and restricted cycle life, which have stimulated growing interest in alternative battery materials with superior performance. This research conducts a literature analysis to investigate the potential of several nanomaterialsgraphene, clay mineral-derived materials, and transition metal sulfidesas improved anode materials for lithium-ion batteries. The study analyzes their characteristics, including energy storage capacity, charging speed, and cycling stability, emphasizing both their benefits and drawbacks. The study concludes that although these materials enhance battery performance considerably, obstacles persist regarding their commercialization and long-term stability. This research provides significant insights into the advancement of more efficient and sustainable lithium-ion battery technology, facilitating the global shift to renewable energy.

Realization of Versatile and Effective Quantum Metrology Using a Single Bosonic Mode

Quantum metrology offers the potential to surpass its classical counterpart, pushing the boundaries of measurement precision toward the ultimate Heisenberg limit. This enhanced precision is normally attained by utilizing large squeezed states or multiparticle entangled quantum states, both of which are often challenging to implement and prone to decoherence in real quantum devices. In this work, we present a versatile and on-demand protocol for deterministic parameter estimation that leverages two efficient state-transfer operations on a single bosonic mode. Specifically, we demonstrate this protocol in the context of phase estimation using the superposition of coherent states in the bosonic circuit quantum electrodynamics (cQED) platform. With low average photon numbers of only up to 1.76, we achieve quantum enhanced precision approaching the Heisenberg scaling, reaching a metrological gain of 7.5(6) dB. Importantly, we show that the gain or sensitivity range can be further enhanced on the fly by tailoring the input states, with different superposition weights, based on specific system constraints. The realization of this versatile and efficient scheme affords a promising path toward practical quantum enhanced sensing, not only for bosonic cQED hardware but also readily extensible to other continuous-variable platforms. Published by the American Physical Society 2025

Examining the Impact of Local Constraint Violations onEnergy Computations in DFT.

This work examines the impact of locally imposed constraints in Density Functional Theory (DFT). Using a metric referred to as the extent of violation index (EVI), we quantify how well exchange-correlation functionals adhere to local constraints. Applying EVIs to a diverse set of molecules for GGA functionals reveals constraint violations, particularly for semi-empirical functionals. We leverage EVIs to explore potential connections between these violations and errors in chemical properties. While no correlation is observed for atomization energies, a significant statistical correlation emerges between EVIs and total energies. Similarly, the analysis of reaction energies suggests weak positive correlations for specific constraints. However, definitive conclusions about error cancellation mechanisms cannot be made at this time. These observations revealed by EVIs may be useful for considerationwhen designing future generations of semilocal functionals.

Novel Data Fusion Strategy for Second-Order Data: Multivariate Curve Resolution for the Determination of Pharmaceuticals by Means of Fluorimetric Measurements.

A new strategy is proposed for second-order data fusion based on the simultaneous modeling of two data sets using the multivariate curve resolution-alternating least-squares (MCR-ALS) model, applying a new constraint during the ALS stage, called "Proportionality of Scores". This approach allows for the fusion of data from different sources, without requiring common dimensionality, and enables the application of specific constraints to each data set. This strategy was applied to the determination of five pharmaceutical contaminants (naproxen, danofloxacin, ofloxacin, sarafloxacin, and enoxacin) in environmental water samples, by fusing two sets of excitation-emission fluorescence matrices, measured before and after photochemical derivatization. The predictive performance of the fused model was compared to individual PARAFAC models built for each fluorescence data set, showing that data fusion significantly increases precision and accuracy, as indicated by the elliptical joint confidence region test. Data fusion allowed improvement of relative errors of prediction, from 13-32% to 8-15% in validation samples and from 25-121% to 13-20% in real samples. The advantages of data fusion were evident in both cases, particularly in instances of substantial signal overlap between analytes or the presence of uncalibrated interferents with similar profiles, as demonstrated by the superior predictive capacity achieved through the proposed strategy.

Gyro-free attitude reorientation control of spacecraft with state and input constraints

Gyro-free attitude reorientation control of spacecraft with state and input constraints

The influence of mechanical constraints on Li diffusion and stress in bilayer electrode of lithium-ion batteries

Lithium-ion batteries (LIBs) are widely used in portable electronic devices, electric vehicles, and other fields. With the rapid development of its application fields, there is an urgent need to further improve its energy density and safety. During the charging/discharging process of the LIBs, the diffusion of Li will cause local volumetric change in the electrode material. The degradation and damage of the electrode material structure caused by diffusion-induced deformation is a major obstacle to the development of LIBs. Generally speaking, the electrode materials in LIBs are always subject to specific external constraints, including both inevitable passive structural constraints within the battery and external active constraints that may be imposed by emerging technology application scenarios, which can also affect the mechanical properties of the electrode materials. Therefore, a deeper understanding of the diffusion-induced stress and Li concentration changes in the electrode material is an engineering requirement for developing new material design paradigms to enhance the overall performance of LIBs. In this work, a two-way diffusion-stress coupling model is used to discuss the effect of the four different levels of idealized deformation constraints on the Li concentration and stress in the bilayer plate electrode during the charging process through the numerical solution. From a mechanical perspective, the bilayer plate electrode structure has two degrees of freedom: lateral expansion and bending deformation. Weakened constraint conditions can partially or completely activate these stress release mechanisms, thereby reducing the overall stress level of the electrode structure and improving its mechanical stability. However, from an electrochemical perspective, the stress gradient generated by the forward bending deformation of the electrode structure can hinder the Li intercalation process. Enhanced constraints can partially or completely suppress the forward bending of the electrode, making the Li concentration in the active layer more uniform and thus improving the capacity utilization efficiency of the active layer. These results not only provide theoretical references for further understanding the chemical-mechanical response of the bilayer electrodes under more realistic or extreme service conditions, but also indicate from a design perspective that compromised external constraints are beneficial for balancing the structural durability and electrochemical performance of electrodes.

Effect of Glycyrrhiza glabra L. on Broiler Growth Performance, Slaughter and Carcass Characteristics, Blood biochemistry, and Hematological Parameters: A Systematic Review

Abstract In this systematic review, 79 research articles focusing on the impact of Glycyrrhiza glabra L. (licorice) supplementation in broilers were meticulously examined, assessing growth performance, slaughter and carcass traits, and blood parameters. Out of these, 12 studies met the predefined criteria. Licorice supplementation, particularly in the absence of specific dietary constraints, signifi-cantly improved feed consumption, body weight gain, and feed conversion efficiency in broilers. The addition of licorice influ-enced slaughter and carcass traits, increasing the dressing percentage, although results varied based on dosage and form of supplementation. Effects on abdominal fat and spleen weights were inconsistent, while liver, heart, and gizzard weights gen-erally increased. Licorice supplementation affected blood biochemistry, showing varied impacts on markers like glucose, cho-lesterol, and triglycerides. Notably, licorice exhibited potential antioxidant properties by reducing malondialdehyde levels, in-dicating decreased oxidative stress. The review highlighted diverse outcomes across growth performance, slaughter traits, car-cass parameters, and blood biochemistry due to licorice supplementation. Variability in dosage, form, and administration methods underscored the need for standardized protocols. Future research should concentrate on uncovering underlying mechanisms, expanding geographical diversity, and exploring interactions with other feed additives, especially in antibiotic-free diets. Despite its promise, further investigation is necessary to optimize licorice’s role in poultry production.

Climate and performance-driven architectural floorplan optimization using deep graph networks

PurposeThis study introduces a novel approach to generating and optimizing energy-efficient and climate-responsive architectural floorplans.Design/methodology/approachThe DGraph-cGAN model utilizes advanced deep-learning techniques to produce diverse, realistic layouts that meet specific design constraints and functional requirements.FindingsThe results show significant energy savings (32.1% overall) across different building types and climate conditions, with reductions in energy use intensity, CO2 emissions and annual energy costs. Case studies demonstrate notable improvements in energy savings, CO2 emission reduction, daylight autonomy, thermal comfort and cost savings.Practical implicationsThe DGraph-cGAN model has great potential for advancing architectural design optimization, with opportunities for further refinement and application in various contexts.Originality/valueThis study contributes to developing a novel approach to optimizing architectural floorplans using deep learning techniques. It provides a valuable tool for architects and designers to create energy-efficient, climate-responsive buildings.