Expenditure Of Resources Research Articles

Factors Associated With Infusion Reactions in Patients With Breast Cancer Receiving Trastuzumab.

Trastuzumab (TRA) is a key drug in human epidermal growth factor receptor type 2 (HER2)-positive breast cancer treatment. Infusion reactions (IR) with TRA are frequently observed in practice. Although the efficacy of premedication has been previously reported, it remains uncommon. The probability of severe IR due to TRA is low; however, when it does occur, it is associated with patient discomfort and expenditure of medical resources. This study aimed to analyze the factors associated with the occurrence of IR in patients with breast cancer who received TRA. We retrospectively studied 204 patients who underwent TRA for breast cancer treatment between September 2008 and June 2023, identifying factors influencing the occurrence of IR at the time of TRA administration. A total of 182 patients were included in this study, and the incidence of IR was 25.8% (47/182 patients). Multiple logistic regression analysis showed that pertuzumab (PER) use, high alkaline phosphatase (ALP), and low high-density lipoprotein (HDL) cholesterol levels were associated with IR. IR should be considered when PER is combined with TRA. ALP and HDL cholesterol levels may be predictive markers of TRA-induced IR in patients with breast cancer.

Using Large Language Models to Recommend Repair Actions for Offshore Wind Maintenance

Abstract The Offshore Wind (OSW) industry is experiencing significant expansion, resulting in increased Operations & Maintenance (O&M) costs. Intelligent alarm systems offer the prospect of swift detection of component failures and process anomalies, enabling timely and precise interventions that could yield reductions in resource expenditure, as well as scheduled and unscheduled downtime. This paper introduces an innovative approach to tackle this challenge by capitalising on Large Language Models (LLMs). We present a specialised conversational agent that incorporates statistical techniques to calculate distances between sentences for the detection and filtering of hallucinations and unsafe output. This potentially enables improved interpretation of alarm sequences and the generation of safer repair action recommendations by the agent. Preliminary findings are presented with the approach applied to ChatGPT-4 generated test sentences. The limitation of using ChatGPT-4 and the potential for enhancement of this agent through re-training with specialised OSW datasets are discussed.

Dynamic Self‐Triggered Parallel Control of Guidance Intercept Systems With Constrained Inputs via Adaptive Dynamic Programming

ABSTRACTThis paper introduces an anti‐saturation optimization algorithm based on dynamic self‐triggered adaptive dynamic programming (ADP) for bounded acceleration guidance interception. By establishing a nonlinear input‐constrained guidance intercept and control system, the smooth bounded function is used to constrain the system input to ensure that the system operates in a controllable range. Subsequently, the appropriate performance function that can accurately reflect the system is designed. In alignment with the advantages of parallel control and ADP, a group of parallel systems are constructed by modeling the derivation of control input. A self‐learning control framework is explored, facilitating virtual‐actual interaction and mutual reinforcement between multiple controllers, optimizing the management of the interception system. Furthermore, event‐triggered control (ETC) policies are devised, which can be updated only when needed by setting trigger conditions, thus saving data resources. The stability proof of the closed‐loop system is given to ensure that the system can keep stable operation when the trigger condition is satisfied. On this basis, a dynamic self‐triggered control (DSTC) with soft computing is put forward, enabling trigger instant calculation without real‐time system monitoring and further extending the trigger interval. Simulation results evidence that the devised guidance scheme can intercept the maneuvering target at a reduced expenditure of communication resources.

Structural topology optimization based on deep learning

In the mechanical design of structures, traditional topology optimization methods involve numerous finite element iterative analyses, leading to a significant expenditure of computational resources. Therefore, the improved multi-scale gradient generative adversarial networks topology optimization technique is proposed. The topology optimization condition parameters are compressed into a low-dimensional latent space feature representation using the encoder, allowing the model to better extract features from these parameters. To speed up model training, the generator and discriminator networks use lightweight residual convolutional blocks. The hybrid attention mechanism extracts prominent region features from the topology optimization structure map. The model training process is guided by a multi-dimensional fusion loss function to enhance the quality of generated model samples. Finally, transferring the parameters of the low-resolution topology optimization model to the high-resolution model enables complete training on a limited amount of high-resolution topology optimization datasets. The experimental data on the low- and high-resolution topology optimization datasets demonstrate that, when compared to alternative methods, this method produces better-quality topology optimization structure maps. Additionally, it can generate high-resolution topology optimization structure maps in minimal time, enabling real-time topology optimization.

Near Infrared Spectroscopy (NIRS) Model Based on Transfer Learning

This study compares three transfer learning strategies for adapting convolutional neural network (CNN) models in near-infrared spectroscopy to new instruments. Strategy 1 freezes pre-trained weights except for the output layer; Strategy 2 fine-tunes all weights with new data; Strategy 3 adds new fully connected layers to the pre-trained model. Using tablet spectra to predict active pharmaceutical ingredient content, results show that fine-tuning all network weights (Strategy 2) yields the best performance. The study also finds that moderate training sample sizes balance model accuracy and resource expenditure. Transfer learning is effective for near-infrared model transfer.

Analysis of prescriptive monitoring regarding the current therapeutic landscape of rheumatoid arthritis: the experience of an Italian local health authority

ABSTRACT Introduction Rheumatoid Arthritis (RA) is among the most prevalent chronic inflammatory diseases affecting millions of people with a significant expenditure of resources by National Healthcare Systems. This study aimed to analyze all available treatments exclusively for RA to highlight the costs of each treatment type and raise awareness of the use of biosimilar drugs. Methods In an Italian healthcare authority, all prescriptions made for the diagnosis of RA were extracted to verify consumption expressed in Defined Daily Dose (DDD) and the expenditure incurred expressed in euros. Consequently, a grouping into three major drug categories has been performed: anti – tumor necrosis factor alpha agents (TNFα), other injectable formulations, and novel oral formulations. Results Prescriptions for the second half-year 2022 and 2023 have been analyzed, with a total cost of almost 7 million euros in the sample considered. All pharmaceutical categories showed an increase in consumption, but only anti- TNFα recorded a decrease in costs from 25% in 2022 to 22% in 2023, thanks to the lower cost of the biosimilar drug. Conclusion The costs of RA may represent a significant spending commitment for central governments. As a result, actions are needed to encourage the preferential use of biosimilar drugs.

Legal principles of pre-trial investigation war crimes

The modern legal principles of the activity of law enforcement agencies regarding the investigation of war crimes have been defined. It is emphasized that war crimes are complex in terms of their mechanism of commission, which is characterized by consequences in the form of significant destruction of civilian infrastructure objects, a large number of unidentified dead; instruments of commission, which neither pre-trial investigation bodies nor forensic experts have encountered before; by persons of criminals who change their locations, including those who are in the territory of another state, etc. It is noted that in Ukraine since the declaration of the legal regime of martial law by the Decree of the President of Ukraine, significant changes have taken place in the criminal procedural legislation, which established a special regime of pre-trial investigation and trial under martial law conditions, provided for in Section IX-1 of the Criminal Procedure Code of Ukraine. It is noted that the pre-trial investigation of war crimes requires considerable expenditure of resources, including human resources, due to which employees of various law enforcement agencies, including units of the National Police, are involved in carrying out certain procedural actions. Legal problems in the activities of the National Police of Ukraine regarding the organizational features of the pre-trial investigation that need to be resolved at the legislative and departmental levels are highlighted. Proposals are made to improve the legislation on the regulation of the term “war crime” in the Criminal Code of Ukraine. The requirements regarding the procedure for collecting evidence of war crimes for the purpose of further transfer of materials for consideration by the International Criminal Court have been updated. Attention is focused on the fact that the principles, subjects and forms of interaction at the national and international levels during the pre-trial investigation of war crimes should be properly regulated. During the writing of the article, the methods of dialectics, comparative legal and systemic analysis, and formal logic were used.

Use of benchmarking as a tool of lean production in the mechanism of cost management in an agricultural organization

The mechanism of cost management, reflecting the peculiarities of its organization at the microand macro-level, with the application of lean production principles (focus on creating value for the customer, continuous improvement, strategic orientation, loss reduction, visualization and transparency, etc.) is presented. Methodological approaches to cost rationing in the production of agricultural products, taking into account the conditions of its production (resource availability of organizations) and the level of efficiency of resource expenditure are substantiated. The novelty of the considered approach consists in the use of the tool of lean production – cost benchmarking. It allowed to calculate the reserves of growth of productive indicators of agricultural producers’ activity taking into account their provision with long-term and short-term assets, labor resources.

Enhanced cancer classification and critical feature visualization using Raman spectroscopy and convolutional neural networks

Cell misuse and cross-contamination pose a significant threat to the accuracy of cell research outcomes, often leading to the wasteful expenditure of time, manpower, and material resources. Consequently, the accurate identification of cell lines is paramount. However, traditional identification methods, which often involve staining and culturing procedures, are not only time-consuming but also laborious. This underscores the need for a novel approach that enables rapid and automated cell line identification, thereby enhancing research efficiency and accuracy. Raman spectroscopy, renowned for its label-free, rapid, and noninvasive nature, offers invaluable molecular insights into samples, making it a widely utilized technique in the biological field. In this study, the identification of one normal and five cancer cell lines was achieved using a sparrow search algorithm-convolutional neural networks (SSA-CNN), considering both the full spectra and fingerprint region perspectives. The SSA-CNN model demonstrated exceptional performance, not just in binary classification, but also in accurately distinguishing among six cell lines. It achieved the highest accuracy (around 95 %), and the lowest standard error (≤3%), for both the full spectra and fingerprint region. Based on the highly accurate SSA-CNN model, proposed the application of gradient-weighted class activation mapping (Grad-CAM) to visualize the Raman feature peaks. Upon comparing the visualized Raman features with reported biomarkers, found that not only were common biomolecules such as glucose, proteins, and liquids visualized, but specific feature peaks also aligned with reported biomarkers. The aforementioned results clearly demonstrated that the proposed strategy not only classifies cancer cell lines with remarkable accuracy but also served as a valuable tool for the discovery of novel biomarkers.

Slow but flexible or fast but rigid? Discrete and continuous processes compared

A tradeoff exists when dealing with complex tasks composed of multiple steps. High-level cognitive processes can find the best sequence of actions to achieve a goal in uncertain environments, but they are slow and require significant computational demand. In contrast, lower-level processing allows reacting to environmental stimuli rapidly, but with limited capacity to determine optimal actions or to replan when expectations are not met. Through reiteration of the same task, biological organisms find the optimal tradeoff: from action primitives, composite trajectories gradually emerge by creating task-specific neural structures. The two frameworks of active inference – a recent brain paradigm that views action and perception as subject to the same free energy minimization imperative – well capture high-level and low-level processes of human behavior, but how task specialization occurs in these terms is still unclear. In this study, we compare two strategies on a dynamic pick-and-place task: a hybrid (discrete-continuous) model with planning capabilities and a continuous-only model with fixed transitions. Both models rely on a hierarchical (intrinsic and extrinsic) structure, well suited for defining reaching and grasping movements, respectively. Our results show that continuous-only models perform better and with minimal resource expenditure but at the cost of less flexibility. Finally, we propose how discrete actions might lead to continuous attractors and compare the two frameworks with different motor learning phases, laying the foundations for further studies on bio-inspired task adaptation.

Understanding the Scope of Acute Care Vascular Surgery at a Tertiary Academic Medical System

The restructuring of nonelective general surgery and nationwide implementation of the acute care surgery paradigm has improved patient outcomes and healthcare resource utilization. Although vascular surgery maintains one of the highest acuity rates among surgical specialties, the acute care vascular surgery (ACVS) practice model has not been widely accepted. In the present study, we investigate the scope and burden of ACVS at a tertiary academic medical system. All vascular surgical procedures performed at three hospitals comprising a large tertiary academic medical system were retrospectively queried through electronic medical records. Data were collected on procedure, acuity, timing of intervention, primary service, admission type, and total costs and charges. Patients were stratified by acuity of surgical intervention, with ACVS being defined as urgent or emergent operation. A total of 12,689 vascular surgeries were performed from 2018 to 2022. ACVS procedures comprised 22.1% of this total (n=2,803; 12.5% urgent, 9.6% emergent), with an annual burden ranging from 19.1% to 28.3%. Vascular surgeons served as primary surgeon in 91.3% of ACVS and cosurgeon in 8.7%. Fourteen separate surgical specialties requested acute vascular assistance, with the most frequently consulting specialties, including trauma or acute care surgery (ACS) (n=109, 3.9%) and cardiac surgery (n=74, 2.6%). ACVS cases were more frequently performed during after-hours (30.7% vs 11.6%) and on weekends (27.1% vs 2.0%) compared with elective vascular procedures (P<0.0001 each). The majority of ACVS cases originated from inpatient (n=2,353, 85.0%) and emergency department (n=379, 13.5%) consultations. Overall, ACVS generated $37.5 million in charges, accounting for 14% of total procedure-related charges over the study period. ACVS comprises a substantial portion of modern vascular practice and is associated with significant human and healthcare resource expenditure. These data support the development of practice models dedicated to acute vascular surgical care.

David vs. Goliath: comparing conventional machine learning and a large language model for assessing students' concept use in a physics problem.

Large language models have been shown to excel in many different tasks across disciplines and research sites. They provide novel opportunities to enhance educational research and instruction in different ways such as assessment. However, these methods have also been shown to have fundamental limitations. These relate, among others, to hallucinating knowledge, explainability of model decisions, and resource expenditure. As such, more conventional machine learning algorithms might be more convenient for specific research problems because they allow researchers more control over their research. Yet, the circumstances in which either conventional machine learning or large language models are preferable choices are not well understood. This study seeks to answer the question to what extent either conventional machine learning algorithms or a recently advanced large language model performs better in assessing students' concept use in a physics problem-solving task. We found that conventional machine learning algorithms in combination outperformed the large language model. Model decisions were then analyzed via closer examination of the models' classifications. We conclude that in specific contexts, conventional machine learning can supplement large language models, especially when labeled data is available.

Automated robotic systems in surgical practice

The use of robotic systems has long gone beyond experimental medicine. More than 200 thousand operations per year are carried out with the use of just the most popular robotic complex Da Vinci. Further development of robotics will contribute to improvement of quality and accuracy of surgical interventions. Even now, it enables reduction of postoperative complications to almost zero. This review presents the analysis of the results of introducing robots into surgery. An overview of the data presented in PubMed, Cochrane Library, Science Direct and eLIBRARY was performed.

Synergistic nanocoating with layer-by-layer functionalized PCL membranes enhanced by manuka honey and essential oils for advanced wound healing

Chronic wounds represent a significant global health concern, statistically impacting 1–2% of the population in developed countries throughout their lifetimes. These wounds cause considerable discomfort for patients and necessitate substantial expenditures of time and resources for treatment. Among the emerging therapeutic approaches, medicated dressings incorporating bioactive molecules, including natural compounds, are particularly promising. Hence, the objective of this study was to develop novel antimicrobial dressings for wound treatment. Specifically, polycaprolactone membranes were manufactured using the electrospinning technique and subsequently coated with natural polyelectrolytes (chitosan as a polycation and a mixture of manuka honey with essential oils nanoemulsions as a polyanion) employing the Layer-by-Layer assembly technique. Physico-chemical and morphological characterization was conducted through QCM-D, FTIR-ATR, XPS, and SEM analyses. The results from SEM and QCM-D demonstrated successful layer deposition and coating formation. Furthermore, FTIR-ATR and XPS analyses distinguished among different coating compositions. The coated membranes were tested in the presence of fibroblast cells, demonstrating biocompatibility and expression of genes coding for VEGF, COL1, and TGF-β1, which are associated with the healing process (assessed through RT-qPCR analysis). Finally, the membranes exhibited excellent antibacterial activity against both Staphylococcus aureus and Pseudomonas aeruginosa, with higher bacterial strain inhibition observed when cinnamon essential oil nanoemulsion was incorporated. Taken together, these results demonstrate the potential application of nanocoated membranes for biomedical applications, such as wound healing.

Research on Optimizing Human Resource Expenditure in the Allocation of Materials in Universities

This paper establishes a multivariate function model for natural human load-carrying walking in some typical scenarios such as college equipment and material relocation by students and a large amount of identical freight relocation in commercial activities. For classified material relocation needs and constraints, we obtain the relationship between walking speed and load weight for a single person, as well as the time cost for different round trips. By establishing an integer programming model with the minimum total transportation time cost and shelf life as the objective function and the requirements of negative weight and speed as the constraint conditions, we reach the optimal item allocation methods considering time cost and shelf life. We discover that there is an approximate linear relationship between the change in natural walking speed and travel time when the load is small, thus obtaining the time cost of student transportation under different round-trip situations. The Monte Carlo simulation algorithm, which is more efficient compared with other methods such as the integer programming method, is used to obtain the optimal allocation scheme that meets the efficiency and quality requirements. The analysis methods and results can be used as guidance for task scheduling optimization for material relocation in educational organizations as well as commercial agencies.

Reducing Resource Expenditure in Value Co-Creation: A Path to Market Shaping

Reducing Resource Expenditure in Value Co-Creation: A Path to Market Shaping

Adaptive Modem Based on LSTM-AutoEncoder with Vector Quantization

Recently, researchers have achieved the goal of using unified architecture to achieve multiple modulation modes under specific conditions. However, existing research still suffers from the problem of large resource and time overhead. This paper proposes an adaptive modem based on a vector quantization (VQ) long short-term memory autoencoder (LSTM-AE), designed to implement modulation and demodulation of signals from the second to thirty-second order in a lightweight way. By leveraging the memory capacity of the LSTM module and the compression capability of the autoencoder, the model is able to support multi-order modulation methods. This study used the Adam optimizer for training and testing on a simple dataset extended by adding AWGN noise only and made modifications based on the MSE loss function to balance the accuracy and training speed of each part of the model. Experiments demonstrate that the proposed method not only achieves comparable modem performance to existing frameworks for second to thirty-second order signals, but also significantly reduces the number of parameters and training time. Experimental results indicate that the proposed methodology not only matches the performance of existing frameworks for signals ranging from the second to the thirty-second order, but also employs merely 79.6% of the average parameter count and a mere 7.4% of the average training duration. This represents a substantial reduction in resource expenditure.

Defending hash tables from algorithmic complexity attacks with resource burning

We consider the problem of defending a hash table against a Byzantine attacker that is trying to degrade the performance of query, insertion and deletion operations. Our defense makes use of resource burning (RB)—the verifiable expenditure of network resources—where the issuer of a request incurs some RB cost. Our algorithm, Depth Charge, charges RB costs for operations based on the depth of the appropriate object in the list that the object hashes to in the table. By appropriately setting the RB costs, our algorithm mitigates the impact of an attacker on the hash table's performance. In particular, in the presence of a significant attack, our algorithm incurs a cost which is asymptotically less that the attacker's cost.

Integrated Supervised and Unsupervised Machine Learning Approach to Map the Electrochemical Windows Over 4500 Solvents for Battery Applications.

The compatibility between solvent electrolytes and high-voltage electrode materials represents a significant impediment to the progress of rechargeable metal-ion batteries. Rapidly identifying suitable solvent electrolytes with optimized electrochemical windows (ECWs) within an extensive search space poses a formidable challenge. In this study, we introduce a combined supervised and unsupervised (clustering) machine learning (ML) approach to discern distinct clusters of solvent electrolytes exhibiting varying ECW ranges. Through supervised machine learning, we have accurately predicted optimal solvent electrolytes with desired ECWs from a vast pool of 4882 solvents. Our ML model boasts superior accuracy compared to previously reported data from density functional theory (DFT). Besides, the exploration of the vast solvent space through K-means clustering (unsupervised approach) yields 11 optimal clusters, each encompassing different solvents characterized by diverse ECW ranges and frequencies. The expedited reduction of solvent space achieved through clustering occurs within a very short time frame and with minimal resource expenditure. Consequently, this method is highly capable of streamlining the subsequent experimental investigations for battery applications, avoiding the need for a trial-and-error approach.

High Neural Efficiency in Unconscious Perceptual Processing among Table Tennis Athletes: An Event-Related Potential Study.

Neural efficiency refers to the brain's ability to function with reduced resource expenditure while maintaining high performance levels. Previous research has demonstrated that table tennis athletes have greater neural efficiency at the conscious level. However, it is unknown whether they exhibit greater neural efficiency at the unconscious level. Therefore, this study aims to investigate unconscious perceptual processing and neural efficiency in elite table tennis athletes through tasks involving the judgment of spin serves. Fifty healthy, right-handed individuals participated in this study, including 25 elite table tennis athletes and 25 control participants without professional training experience. To evaluate the unconscious perceptual characteristics of both groups, we used a combination of masked priming paradigm and event-related potential techniques. The behavioral results reveal that, compared to the control group, the table tennis athletes displayed reduced reaction times (p < 0.001) and increased priming effects (p < 0.001) under unconscious conditions. The electrophysiological findings indicated that both groups elicited N1, N2, and P2 components. Notably, compared to the control group, the table tennis athletes exhibited significantly lower amplitude responses at the occipital lobe electrodes PO3, POz, PO4, O1, Oz, and O2 (p < 0.001). These results further support the neural efficiency hypothesis, indicating that prolonged professional training enhances athletes' capacities for specialized unconscious cognitive processing.