Radiology AI and sustainability paradox: environmental, economic, and social dimensions by Burak Kocak, Andrea Ponsiglione, Valeria Romeo, Lorenzo Ugga, Merel Huisman, Renato Cuocolo

“…Socially, AI risks perpetuating healthcare disparities through biases in algorithms and unequal access to technology. On the other hand, AI has the potential to improve sustainability in healthcare by reducing low-value imaging, optimizing resource allocation, and improving energy efficiency in radiology departments. …”
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A Study on Spatial and Temporal Changes and Synergies/Trade-Offs of the Production-Living-Ecological Functions in Mountainous Areas Based on the Niche Width Model by Yaling Li, Ruoying Song, Ping Ren

“…As a typical ecologically fragile mountainous area, Liangshan Yi Autonomous Prefecture in Sichuan Province faces challenges of irrational land resource allocation and uncoordinated urbanization. …”
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Use of machine learning to predict creativity among nurses: a multidisciplinary approach by Rola H. Mudallal, Majd T. Mrayyan, Mohammad Kharabsheh

“…The developed decision support system may be valuable for helping nurse managers evaluate creativity among nurses; this allows for more informed decisions about staff allocation, development, and resource optimization. …”
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Dynamic Load Balancing for Enhanced Network Performance in IoT-Enabled Smart Healthcare With Fog Computing by Mohammed Alaa Ala'anzy, Raiymbek Zhanuzak, Ramis Akhmedov, Nader Mohamed, Jameela Al-Jaroodi

“…To address these limitations, an Optimised Load Balancing (OLB) algorithm is proposed, to allocate workloads effectively across fog nodes and to reduce communication and computational delays. …”
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NCREASING ECONOMIC PERFORMANCES USING OPTIMIZATION by Daniela-Elena ȘURCĂ

“…The optimization led to the improvement of the overall economic performance by the rational allocation of resources and the establishment of the crop structure in order to obtain large yields. …”
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Processing streams in a monitoring cloud cluster by Alexey N. Nazarov

“…The International Telecommunication Union’ (ITU) recommendations Y. 3510 present the requirements for cloud infrastructure that require monitoring the performance of deployed applications based on the collection of real-world statistics. Often, computing resources of monitoring clusters of cloud data centers are allocated for continuous parallel processing of high-speed streaming data, which imposes new requirements to monitoring technologies, necessitating the creation and research of new models of parallel computing. …”
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Certificateless Batch Authentication Scheme and Intrusion Detection Model Based on the Mobile Edge Computing Technology NDN-IoT Environment by Jianzhao Sun

“…Compared with the linear search algorithm, the algorithm proposed in this paper can significantly reduce the computation time.…”
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Dynamic Water Scheduling in the Northwest River Delta Basin Based on Minimum Discharge Flow Control in Cross-section by WU Di, FANG Chunyan, SHANG Yunfei

“…The whale optimization algorithm was used to find the optimal water allocation plan. …”
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Navigating the landscape: Unveiling the reflection of AI in the sharing economy through bibliometric-based approach by Veljko Uskoković, Milica Maričić, Aleksandra Dacić-Pilčević, Veljko Jeremić

“…Furthermore, AI optimizes resource allocation and improves matching algorithms on online labour platforms, thereby boosting platform efficiency. …”
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Temporal dependent rate-distortion optimization based on distortion backward propagation by Hongwei GUO, Ce ZHU, Xu YANG, Lei LUO

“…Rate-distortion optimization (RDO) is a crucial technique in block based hybrid video encoders.However, the widely used independent RDO is far from obtaining optimal coding performance.To improve the rate-distortion (R-D) performance of high efficiency video coding (HEVC), a temporal dependent RDO algorithm was proposed.Firstly, the formula to calculate temporal distortion propagation factor was derived by using an exponential R-D function.Then, the coding distortion and motion compensation predicted error were obtained by pre-encoding, and the temporal distortion propagation factor was estimated by using distortion backward propagation.Finally, the Lagrange multiplier and quantization parameter of coding tree unit were adaptively adjusted to optimize bit resources allocation.Experimental results show that compared with the original RDO method in HEVC under the low-delay configuration, the proposed algorithm achieves an average 4.4% bit rate reduction for all test sequences, and up to 13.0% bit rate reduction for test sequence BasketballDrill, at the same reconstructed video quality.…”
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Temporal dependent rate-distortion optimization based on distortion backward propagation by Hongwei GUO, Ce ZHU, Xu YANG, Lei LUO

“…Rate-distortion optimization (RDO) is a crucial technique in block based hybrid video encoders.However, the widely used independent RDO is far from obtaining optimal coding performance.To improve the rate-distortion (R-D) performance of high efficiency video coding (HEVC), a temporal dependent RDO algorithm was proposed.Firstly, the formula to calculate temporal distortion propagation factor was derived by using an exponential R-D function.Then, the coding distortion and motion compensation predicted error were obtained by pre-encoding, and the temporal distortion propagation factor was estimated by using distortion backward propagation.Finally, the Lagrange multiplier and quantization parameter of coding tree unit were adaptively adjusted to optimize bit resources allocation.Experimental results show that compared with the original RDO method in HEVC under the low-delay configuration, the proposed algorithm achieves an average 4.4% bit rate reduction for all test sequences, and up to 13.0% bit rate reduction for test sequence BasketballDrill, at the same reconstructed video quality.…”
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Evaluating accountability, transparency, and bias in AI-assisted healthcare decision- making: a qualitative study of healthcare professionals’ perspectives in the UK by Saoudi CE Nouis, Victoria Uren, Srushti Jariwala

“…Results Participants reported notable gains in diagnostic efficiency and resource allocation, underscoring AI’s potential to reduce turnaround times for routine tests and enhance detection of abnormalities. …”
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OliveTreeCrownsDb: A high-resolution UAV dataset for detection and segmentation in agricultural computer visionMendeley Data by Youness Hnida, Mohamed Adnane Mahraz, Ali Achebour, Ali Yahyaouy, Jamal Riffi, Hamid Tairi

“…It facilitates the development of innovative algorithms to optimize resource allocation and improve crop management. …”
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Centralized Shared Energy Storage Optimization Framework for AC/DC Distribution Systems with Dual-Time-Scale Coordination by Yidi Zhu, Qian Xiao, Hongjie Jia, Wenbiao Lu, Yu Jin

“…Conventional shared energy storage (SES) allocation and coordinated operation mechanism are mismatched with the actual time-varying demand of the distribution system, resulting in low utilization of energy storage and renewable energy sources (RES), which restricts the system operational efficiency and RES integration. …”
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SimpleMating: R‐package for prediction and optimization of breeding crosses using genomic selection by Marco Antônio Peixoto, Rodrigo Rampazo Amadeu, Leonardo Lopes Bhering, Luís Felipe V. Ferrão, Patrício R. Munoz, Márcio F. R. Resende Jr.

“…Herein, we describe a new computational package for mate allocation in a breeding program. SimpleMating is a flexible and open‐source R package originally designed to predict and optimize breeding crosses in crops with different reproductive systems and breeding designs. …”
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Patterns and risk factors of falls among older adults: a systematic review by Tetiana Yermakova

“…The first algorithm is based on extracting documents with the highest citation metrics. …”
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From 6G to <i>SeaX-G</i>: Integrated 6G TN/NTN for AI-Assisted Maritime Communications—Architecture, Enablers, and Optimization Problems by Anastasios Giannopoulos, Panagiotis Gkonis, Alexandros Kalafatelis, Nikolaos Nomikos, Sotirios Spantideas, Panagiotis Trakadas, Theodoros Syriopoulos

“…Finally, we propose a set of resource optimization scenarios, including dynamic spectrum allocation, energy-efficient communications and edge offloading in MCNs, and discuss how AI/ML and learning-based methods can offer scalable, adaptive solutions. …”
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Risk factors for tuberculosis treatment outcomes: a statistical learning-based exploration using the SINAN database with incomplete observations by Nguyen Ky Phat, Yoonah Lee, Dinh Hoa Vu, Nguyen Phuoc Long, Seongoh Park

“…Abstract Background Understanding early predictors of treatment outcomes allows better outcome prediction and resource allocation for efficient tuberculosis (TB) management. …”
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Optimising test intervals for individuals with type 2 diabetes: A machine learning approach. by Sasja Maria Pedersen, Nicolai Damslund, Trine Kjær, Kim Rose Olsen

“…<h4>Objective</h4>To demonstrate the potential of ML to guide resource allocation and tailored disease management, this study aims to predict the optimal testing interval for monitoring blood glucose (HbA1c) for patients with Type 2 Diabetes (T2D). …”
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Responsible artificial intelligence in public health: a Delphi study on risk communication, community engagement and infodemic management by Keyrellous Adib, David Novillo-Ortiz, Ben Duncan, Daniela Mahl, Mike S Schäfer, Stefan Adrian Voinea, Cristiana Salvi

“…Prioritised actions ranged from regulatory measures, resource allocation and feedback loops to capacity building, public trust initiatives and educational training.Conclusion To responsibly navigate the multifaceted opportunities, challenges and risks of AI for RCCE-IM in public health emergencies, clear guiding principles, ongoing critical evaluation and training as well as societal collaboration across countries are needed.…”
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