Additional Cost Research Articles

Growth and Depuration of Off‐Flavors in Rainbow Trout Oncorhynchus Mykiss in a Partial Recirculating Aquaculture System (PRAS)

ABSTRACTIn recirculating aquaculture systems (RAS), off‐flavours accumulated in fish muscle tissue can be problematic in terms of consumer acceptance and the reputation of farmed fish products. Off‐flavours often give fish earthy, muddy, or other undesirable flavours. Typically, off‐flavours are removed during a depuration period in which fish are fasted and held in clean water. Unfortunately, this causes additional costs and delayed sales, while fish lose weight and show a decrease in lipid content. First, we studied fish growth in a partial RAS (PRAS) where the conditions are very similar to those in depuration with a water exchange rate of 4000 L kg−1 feed, compared to RAS with a 650‐L water kg−1 feed. Rainbow trout (Oncorhynchus mykiss) was reared in both systems. Our aim was to combine the benefits of a higher water exchange rate: the lack of need for biofilters and a lower accumulation of off‐flavours while obtaining stable rearing conditions. Additionally, we studied the effects of moderate feeding and H2O2 addition during depuration. The fish grew faster in a PRAS than in a RAS when fed ad libitum. Thirteen off‐flavour compounds were found in the fish flesh and 11 in the circulating water. The H2O2 addition led to decreased levels of off‐flavours in the tank water and in fish muscle. The results showed no significant differences in off‐flavours between the fed and not‐fed systems, showing that moderate feeding did not prevent a good depuration result. However, the lipid content and the overall fish weight were higher in the fed systems, which suggests more effective depuration. Increased depuration efficiency can be an important tool when considering ways to improve the profitability of production.

A lightweight finger multimodal recognition model based on detail optimization and perceptual compensation embedding

Multimodal biometric recognition technology has attracted the attention of many scholars due to its higher security and stability than single-modal recognition, but its additional parameter quantity and computational cost have brought challenges to the lightweight deployment of the model. In order to meet the needs of a wider range of application scenarios, this paper proposes a lightweight model DPNet using fingerprint and finger vein images for multimodal recognition, which adopts a double-branch lightweight feature extraction structure combining detail optimization and perception compensation. Among them, the detail extraction optimization branch uses multi-scale dimensionality reduction filtering to obtain low-redundant detail information, and combines the depth extension operation to enhance the generalization ability of detail features. The perception compensation branch expands and compensates the model's perceptual field of view through lightweight spatial location query and global information attention. In addition, this paper designs a perceptual feature embedding method to embed perceptual compensation information in the way of importance adjustment to improve the consistency of embedded features. The ABFM fusion module is proposed to carry out multi-level lightweight and deep interactive fusion of the extracted finger modal features from the global to the spatial region, so as to improve the degree and utilization rate of feature fusion. In this paper, the model recognition performance and lightweight advantages are verified on three multimodal datasets. Experimental results show that the proposed model achieves the most advanced lightweight effect and recognition performance in the experimental comparison of all datasets.

PPDM++: Parallel Point Detection and Matching for Fast and Accurate HOI Detection.

Human-Object Interaction (HOI) detection aims to understand human activities by detecting interaction triplets. Previous HOI detection methods adopt a two-stage instance-driven paradigm. Unfortunately, many non-interactive human-object pairs generated by the first stage are the main obstacle impeding HOI detectors from high efficiency and promising performance. To remedy this, we propose a novel top-down interaction-driven paradigm, detecting interactions first and bridging interactive human-object pairs through interactions. We formulate HOI as a point triplet human point, interaction point, object point and design a Parallel Point Detection and Matching (PPDM) framework. We further take advantage of two-stage methods and propose a novel framework, PPDM++, that detects the interactive human-object pairs by PPDM, then extracts region features for each pair to predict actions. The core of PPDM/PPDM++ is to convert the instance-driven bottom-up paradigm to an interaction-driven top-down paradigm, thus avoiding additional computation costs from traversing a tremendous number of non-interactive pairs. Benefiting from the advanced paradigm, PPDM/PPDM++ has achieved significant performance gains with high efficiency. PPDM-DLA-34 has achieved 19.94 mAP with 42 FPS as the first real-time HOI detector, and PPDM++-SwinB achieves 30.1 mAP with 17 FPS on HICO-DET dataset. We also built an application-oriented database named HOI-A, a supplement to the existing datasets.

Image Enhancement Guided Object Detection in Visually Degraded Scenes.

Object detection accuracy degrades seriously in visually degraded scenes. A natural solution is to first enhance the degraded image and then perform object detection. However, it is suboptimal and does not necessarily lead to the improvement of object detection due to the separation of the image enhancement and object detection tasks. To solve this problem, we propose an image enhancement guided object detection method, which refines the detection network with an additional enhancement branch in an end-to-end way. Specifically, the enhancement branch and detection branch are organized in a parallel way, and a feature guided module is designed to connect the two branches, which optimizes the shallow feature of the input image in the detection branch to be as consistent as possible with that of the enhanced image. As the enhancement branch is frozen during training, such a design plays a role in using the features of enhanced images to guide the learning of object detection branch, so as to make the learned detection branch being aware of both image quality and object detection. When testing, the enhancement branch and feature guided module are removed, and so no additional computation cost is introduced for detection. Extensive experimental results, on underwater, hazy, and low-light object detection datasets, demonstrate that the proposed method can improve the detection performance of popular detection networks (YOLO v3, Faster R-CNN, DetectoRS) significantly in visually degraded scenes.

Energy expenditure during physical work in cold environments: physiology and performance considerations for military service members.

Effective execution of military missions in cold environments requires highly trained, well-equipped, and operationally ready service members. Understanding the metabolic energetic demands of performing physical work in extreme cold conditions is critical for individual medical readiness of service members. In this narrative review, we describe 1) the extreme energy costs of performing militarily relevant physical work in cold environments, 2) key factors specific to cold environments that explain these additional energy costs, 3) additional environmental factors that modulate the metabolic burden, 4) medical readiness consequences associated with these circumstances, and 5) potential countermeasures to be developed to aid future military personnel. Key characteristics of the cold operational environment that cause excessive energy expenditure in military personnel include thermoregulatory mechanisms, winter apparel, inspiration of cold air, inclement weather, and activities specific to cold weather. The combination of cold temperatures with other environmental stressors, including altitude, wind, and wet environments, exacerbates the overall metabolic strain on military service members. The high energy cost of working in these environments increases the risk of undesirable consequences, including negative energy balance, dehydration, and subsequent decrements in physical and cognitive performance. Such consequences may be mitigated by the application of enhanced clothing and equipment design, wearable technologies for biomechanical assistance and localized heating, thermogenic pharmaceuticals, and cold habituation and training guidance. Altogether, the reduction in energy expenditure of modern military personnel during physical work in cold environments would promote desirable operational outcomes and optimize the health and performance of service members.

Fine-grained detoxification framework via instance-level prefixes for large language models

Large Language Models (LLMs) have demonstrated remarkable performance across various natural language processing (NLP) tasks. However, their practical usability is often compromised by a propensity to generate toxic content, such as insults, threats, and profanity, particularly in response to adversarial prompts. Several fine-tuning and decoding approaches have been employed to address this challenge to mitigate toxicity. Nevertheless, these methods typically necessitate additional resources, such as high-quality training data or auxiliary models, thereby incurring extra costs. In this paper, we propose a novel detoxification framework, Fine-Grained Detoxification via Instance-Level Prefixes (FGDILP), which effectively mitigates the generation of toxic text without incurring additional training costs. Specifically, FGDILP leverages contextualized representations in attention space by contrasting a positive prefix-prepended prompt against multiple negative prefix-prepended prompts at the instance level. This methodology facilitates the construction of fine-grained subtoxicity vectors, which are subsequently fused to adjust the original generation pathway when responding to raw prompts. Our results demonstrate that FGDILP enables controlled text generation concerning detoxification at both the utterance and context levels. While our method slightly impacts generation fluency and diversity, it significantly outperforms prompt-based baselines regarding detoxification effectiveness. Our code is available at https://github.com/xinykou/FGDILP.

Additive-Free, In Situ Rapid Repair of Vacancies in Fe[Fe(CN)6] Electrodes for Efficient Capacitive Deionization.

Fe[Fe(CN)6] (FeHCF) is considered a promising material for capacitive deionization-desalination of saline wastewater due to its excellent structure. However, additives are usually introduced during the synthesis of FeHCF in order to avoid [Fe(CN)6]3- vacancy defects filled by ligand water, which can result in the appearance of harmful byproducts and additional water treatment costs. In this study, an additive-free in situ vacancy repair strategy is proposed for the rapid synthesis of high-quality FeHCF in a saturated K3Fe(CN)6 solution. During the process of synthesizing FeHCF in solution, a high concentration of [Fe(CN)6]3- is found to facilitate the binding of Fe3+ to [Fe(CN)6]3- and hinder the hydrolysis and coordination reaction of Fe3+. After undergoing repair, FeHCF4 demonstrates an increased capacity and highly reversible electrochemical performance due to the robust structure. When utilized as Faraday cathodes in hybrid capacitive deionization (HCDI) systems, FeHCF4 exhibits a higher salt removal capacity (65.67 mg g-1) and lower energy consumption (0.68 kWh kg-1-NaCl) compared to unrepaired FeHCF1, while still maintaining excellent cycling performance. This environmentally friendly approach of repairing vacancies serves as a source of inspiration for the advancement of high-performance Prussian Blue analogues as capacitive sodium-removing materials.

Health and healthcare use of homeless population: Evaluation study of joint social work and healthcare provision

BackgroundHomelessness as an extreme form of poverty perpetuates and exacerbates health inequalities. People experiencing homelessness face a mortality rate 10 times higher than that of the general population, with an average age of death at 45. There is a significant disconnect between the mainstream healthcare system and the specific health needs of people experiencing homelessness, leading to substantial human and economic costs. ObjectiveThe objective of this evaluation study is to assess the impact of an intervention in nurse-led healthcare outreach services to people experiencing homelessness on their utilization of healthcare services. DesignThis study is a part of research program aimed at assuring health equity of most vulnerable members of a society. Detailed understanding of barriers to care is a necessary precondition for improvements in healthcare use. DataThe study analyzes data on hospitalization and emergency department visits by people experiencing homelessness across three cities in Czechia from 2014 to 2021. MethodsA quantitative difference-in-differences approach is complemented by insights from field studies in these three cities. ResultsThe intervention in people experiencing homelessness outreach led to a reduction in both hospital admissions and the emergency visits by people experiencing homelessness, alleviating pressure on health service capacity and reducing associated healthcare costs. Enhanced primary nurse-led healthcare outreach, along with cross-sectoral integration and activation, has lowered the barriers to accessing essential healthcare services. ConclusionA pivotal policy outcome of this study is the establishment of an insurance provision that allows medical doctors to claim additional costs incurred in treating people experiencing homelessness from a public insurance system.

The analysis of evolutionary strategies to facilitate the transformation of traditional buildings into prefabricated buildings

This paper establishes a two-party evolutionary game theory model comprising of the governments and prefabricated components suppliers as the players; and uses the replicator dynamic equations to analyze the evolution mechanisms each player’s strategy choices. Results from numerical simulations indicate that the evolution trajectory of the model is significantly influenced by not only the initial strategies of the players, but also by government incentives and regulatory penalties, as well as the costs and benefits resulted from each strategy choice. Also, prefabricated components suppliers exhibit greater responsiveness to variations in cost-related factors. When the subsidy and penalty respectively remain constant at 15% and 5% of the additional cost, the model exhibits both rapid and stable evolutionary trend towards the optimal evolutionarily stable strategy of full cooperation between the two players. This article proposes a suitable threshold for government incentives and penalties, besides serving as a guide for the steady and sustainable growth of the prefabricated buildings sector.

Cost-effectiveness analysis of selpercatinib versus chemotherapy and pembrolizumab in the first-line treatment of rearranged during transfection fusion-positive non-small cell lung cancer in the United States.

Although selpercatinib has shown clinical benefits for rearranged during transfection (RET) fusion-positive non-small cell lung cancer (NSCLC), its cost-effectiveness requires further evaluation. This study aimed to evaluate the cost-effectiveness of selpercatinib versus chemotherapy and pembrolizumab in the first-line treatment of RET fusion-positive NSCLC from the perspective of the United States (US) payer. A partitioned survival model was developed based on data from the LIBRETTO-431 trial. Cost and utility values for the health state were obtained from database data or published literature. The measured outcomes included quality-adjusted life-years (QALYs) and the incremental cost-effectiveness ratio (ICER). One-way sensitivity analysis and probabilistic sensitivity analyses (PSA) were conducted to assess the uncertainty of the model. Selpercatinib increased QALYs in patients with RET fusion-positive NSCLC by 0.90 compared to chemotherapy plus pembrolizumab, with an additional cost of $542,517.45, resulting in an ICER of $603,286.49/QALY, which exceeded the willingness-to-pay (WTP) threshold ($150,000) in the US. One-way sensitivity analysis suggested that the utility of progressed disease, the utility of progression-free survival, the price of selpercatinib, the discount, the price of pemetrexed, and the price of pembrolizumab had the greatest influence on the cost- effectiveness analysis process. In the PSA, 99.9% of the scatter points were distributed above the US WTP threshold of $150,000. From the perspective of the US payer, selpercatinib is not cost-effective compared to chemotherapy and pembrolizumab for first-line treatment in patients with RET fusion-positive NSCLC.

Risk assessment model used to predict discharge care after total hip and total knee arthroplasty: A population-based study

BackgroundTransfer to a post-acute care facility or hospital readmission after total joint arthroplasty represent additional costs and increased surgical and health care resource utilization. Accurate prediction of post-acute care factors could help providers to plan the patient's discharge destination and have a positive impact on postoperative outcomes and readmission rates. ObjectiveTo develop a risk assessment model to predict discharge care after total hip arthroplasty (THA) and total knee arthroplasty (TKA). DesignA retrospective longitudinal observational study. Settingsand participants: This study included 209 patients who underwent primary unilateral THA or TKA at a major academic medical center in Switzerland from January 2018 to December 2019. MethodsA collection of computerized- and paper-recorded data identified the discharge destination, socio-demographic factors, comorbidities, and other factors related to the patient. Univariate and multivariate analyses were performed to describe the predictors of post-surgical discharge destinations. ResultsThe characteristics associated with post-acute care after primary unilateral THA or TKA were the absence of a caregiver, advanced age, female gender, presence of walking aids, high ASA score, and comorbidity severity. A prediction model demonstrated that these six characteristics were associated 52 % with discharge to a post-acute care destination. ConclusionsThis study allowed us to identify predictors of discharge to a post-surgical destination. Predictive models can be efficiently used to better predict which patients are predisposed to post-acute care after hospital discharge. Further studies are needed to determine the optimal criteria for different destinations.

Attention-Guided Sample-Based Feature Enhancement Network for Crowded Pedestrian Detection Using Vision Sensors.

Occlusion presents a major obstacle in the development of pedestrian detection technologies utilizing computer vision. This challenge includes both inter-class occlusion caused by environmental objects obscuring pedestrians, and intra-class occlusion resulting from interactions between pedestrians. In complex and variable urban settings, these compounded occlusion patterns critically limit the efficacy of both one-stage and two-stage pedestrian detectors, leading to suboptimal detection performance. To address this, we introduce a novel architecture termed the Attention-Guided Feature Enhancement Network (AGFEN), designed within the deep convolutional neural network framework. AGFEN improves the semantic information of high-level features by mapping it onto low-level feature details through sampling, creating an effect comparable to mask modulation. This technique enhances both channel-level and spatial-level features concurrently without incurring additional annotation costs. Furthermore, we transition from a traditional one-to-one correspondence between proposals and predictions to a one-to-multiple paradigm, facilitating non-maximum suppression using the prediction set as the fundamental unit. Additionally, we integrate these methodologies by aggregating local features between regions of interest (RoI) through the reuse of classification weights, effectively mitigating false positives. Our experimental evaluations on three widely used datasets demonstrate that AGFEN achieves a 2.38% improvement over the baseline detector on the CrowdHuman dataset, underscoring its effectiveness and potential for advancing pedestrian detection technologies.

ОПРЕДЕЛЕНИЕ ОПТИМАЛЬНЫХ ПАРАМЕТРОВ КАПЕЛЬНОЙ ЛЕНТЫ ДЛЯ МЕХАНИЗИРОВАННОЙ УКЛАДКИ

The introduction of drip irrigation systems in irrigated areas is impossible without the creation of special machines for laying drip tape. The advantage of in-soil laying of the tape is the absence of losses due to evaporation of irrigation water and the inadmissibility of its heating to temperatures dangerous for the plant in the hot season. To ensure high-quality laying of the drip tape in the soil, constant tension of the tape is required when the speed of the machine fluctuates or it suddenly stops. The absence of constant tension will lead to jamming of the tape in the laying pipe or its twisting, which will make further operation of the drip irrigation system impossible and will require repair work that requires additional labor costs. Constant tension of the tape ensures its precise location in the soil, which will eliminate additional pressure losses when the position of the tape changes during operation of the drip irrigation system. To maintain constant tension, a permanent brake is required. This creates additional tensile forces applied to the tape. The tape is also affected by the resistance of laying it in the soil. The research was aimed at establishing the numerical values of the forces acting on the drip tape to assess their impact. The most popular tapes on farms are those with a thickness of 0.15...0.25 mm due to their low cost. Research has established that for underground installation it is desirable to use a tape with a thickness of at least 0.2 mm. When using a tape with a thickness of 0.15 mm, tensile forces can lead to dangerous deformations of the tape, making it impossible to further use

Pickup and Delivery Vehicle Routing Problem with Hybrid Missions and Third-Party Vehicle Rentals: A Case Study of Animal Feed Industry

The vehicle routing problem for small and medium-sized animal feed manufacturers is a complex challenge because each transport route can vary significantly between delivering products to customers and picking up raw materials to return to the factory. Currently, businesses lack well-planned management for utilizing a limited number of trucks. As a result, they often incur additional costs by relying on third-party vehicle rentals. This research aims to reduce transportation costs for small and medium-sized animal feed manufacturers by solving a formulated mixed-integer linear programming (MILP) model, using experimental data that accurately reflects the actual problems faced. The MILP model results help reduce transportation costs by up to 18.5% compared to current practices by optimizing route sequences and incorporating the use of third-party vehicle rentals. This also serves as a guideline for reducing transportation costs in other similar industries.

Perancangan Rute Optimal Distribusi Kargo Bahan Bakar Minyak dengan Metode Brute - Force Untuk Meminimasi Biaya Konsumsi Bahan Bakar Minyak Perusahaan Pelayaran PT XYZ

TSP of fuel oil distribution using tankers, the projected route search often does not contain the most minimal mileage, which can lead to adverse results such as significant additional costs. This research analyzes four different routes using several iterative proposals to reduce travel distance, time, and fuel costs compared to actual conditions. Problem solving is carried out using the Brute-Force method which enumerates all permutation results. The analysis results show that all proposed iterations were able to provide significant reductions in all three aspects. The four research routes achieved optimization results of 21,7% compared to the actual route modeling. This optimization proves that the iterative methods applied can substantially improve operational efficiency. Therefore, the determination of the tanker route must consider all the possibilities required in determining the route. The optimal route created is expected to have a new model with the aim of minimizing fuel costs based on the smallest distance and time calculations.

Comparative effectiveness of new pharmacotherapy regimens for mild COVID-19 based on symptom resolution dynamics

the treatment of COVID-19 remains a challenging issue worldwide due to the huge economic losses to society as a result of the disease's widespread prevalence, peculiarities of its course and complications of its development. The purpose of the work was comparative analysis of the effectiveness of new treatment regimens for outpatients with mild COVID-19. 64 medical charts of outpatient diseases with a mild course of COVID-19 were studied. Depending on the treatment regimen used, all patients were divided into two groups that received inhaled ACEs or antiviral therapy with the combination drug nortrelvir/ritonavir. The efficacy of each pharmacotherapeutic alternative would be determined on the basis of retrospective data for the timeframe of normalisation of clinical and laboratory parameters. A rule was established that the set of all clinical and laboratory parameters in the normal range is considered as a clinical endpoint. It was found that the use of ACC treats the disease until the rapid disappearance of clinical symptoms, especially in the first treatment, which works on its rapid therapeutic effect in the initial stages. Nirmatrelvir/ritonavir has also been shown to be effective in reducing symptoms, but its effect is incremental, reaching full relief slightly later than in ACC. Modeling of the dynamics of reaching the clinical point of elimination of all clinical symptoms indicates a pharmacoeconomic advantage of the use of ACC over nirmatrelvir/ritonavir, if the additional costs of treatment do not exceed the cost of three days of treatment.

Advances in Adenomyosis Treatment: High-Intensity Focused Ultrasound, Percutaneous Microwave Therapy, and Radiofrequency Ablation.

Background/Objectives: Adenomyosis is a debilitating gynecologic condition that affects both multiparous older women and nulliparous younger women, inducing a variety of symptoms such as dysmenorrhea, menorrhagia, and infertility. Thermal ablation techniques are new procedures that have been proposed for the treatment of adenomyosis. They include high-intensity focused ultrasound (HIFU), percutaneous microwave ablation (PMWA), and radiofrequency ablation (RFA). Because thermal ablation techniques are minimally invasive or noninvasive, fertility is not impaired while symptoms improve. In addition, hospital stays and financial costs are generally reduced, increasing the interest in these alternative management options. Methods: In this narrative review, we conducted a thorough literature search of PubMed/Medline from the database inception to September 2022. In our search, we focused on noninvasive treatment methods such as HIFU ablation, RFA ablation, and PMWA as well as adenomyosis-specific terms and noninvasive techniques (ultrasonography, ultrasound, or magnetic resonance imaging). The queries were a combination of MeSH terms and keywords. The search was limited to the English language. Abstracts were screened according to their content, and relevant articles were selected. Results: Overall, the results showed that the above-mentioned ablation techniques are effective and safe in providing adenomyosis treatment. Lesion size and uterus volume are reduced, leading to considerable symptom alleviation with all three methods. Positive results concerning safety and fertility preservation have been described as well. Conclusions: Nonetheless, more research is required in this field to compare the efficacy and safety of different ablation techniques with traditional therapies. Such research will help improve these procedures and their associated decision-making processes.

Perancangan Model Bisnis Kaki Bukit Coffee Menggunakan Metode Business Model Canvas

Kaki Bukit Coffee is a coffee shop located in West Bandung Regency, precisely on Jl. Maribaya No.16, Kayuambon, Lembang District, which was founded on December 16 2018. This coffee shop offers various types of drinks, both hot and cold, such as Basic Coffee, Flavored Coffee, Freshener, and Tea. However, Kaki Bukit Coffee faces several challenges, including not achieving its revenue target. This challenge is caused by several factors such as lack of employee capability, limited space capacity, lack of product innovation, many competitors, the influence of rainy weather, less than optimal promotional strategies, and limited use of promotional media only via Instagram. This research aims to identify and overcome the problems faced by Kaki Bukit Coffee through evaluating and designing a new business model using the Business Model Canvas (BMC) method. The improvement process begins with collecting data regarding the current condition of the business model, including customer profiles obtained through questionnaires and analysis of the business environment through literature studies. Next, a SWOT analysis is carried out to understand the strengths, weaknesses, opportunities and threats from internal and external aspects, as well as identifying the value proposition canvas to formulate the right strategy. Several proposals were put forward to solve problems such as in the Value Proposition block, proposals included increasing product variations, offering variations in glass sizes and toppings, as well as providing bundling packages. In the Customer Segments block, the focus is on increasing the number of Lembang customers. For Channels, proposals include increasing the use of online food delivery applications such as GoFood, increasing promotions via social media such as Instagram, adding the ShopeeFood and TikTok platforms, as well as displaying menu lists and linktr.ee on Instagram profiles. In the Customer Relationship block, proposals include offering discounts and promos, providing special promotions for local residents, as well as providing WiFi connections. In the Key Activities block, increasing sales and marketing activities, adding training and HR development programs, as well as improving indoor and outdoor facilities are priorities. In the Key Partners block, namely increasing cooperation with raw material suppliers. Meanwhile, in the Cost Structure block, the proposal includes additional costs for improving indoor and outdoor facilities, training and human resource development costs, as well as costs for WiFi connections. Lastly, in the Revenue Streams block, the focus is on increasing product sales offline and online.

Logistics optimization and inventory control in Peruvian grocery companies

The company being analyzed, which specializes in selling groceries, faces significant challenges in its logistics management. Lack of supply chain visibility prevents informed inventory decisions, resulting in product overages or shortages. Supply chain delays affect profitability, and inaccurate demand planning generates additional costs and financial losses. This study, which is relevant to the Sustainable Development Goals, mainly in combating poverty through the economic success of SMEs, evaluates the effectiveness of logistics management in inventory control. The results show an increase in ending inventory by 105.12% and a decrease in inventory turnover by 29.29%, suggesting a strategic accumulation of stock. In addition, an improvement in the company's liquidity was observed, with an increase in total current assets by 107.66% and a financial restructuring oriented towards expansion projects. These findings underscore efficient logistics management's importance in optimizing resources and improving customer satisfaction. Future research should focus on the integration of advanced technologies and the implementation of sustainable financing strategies to further improve operational efficiency and profitability.

Reducing the cost of capital to finance the energy transition in developing countries

Climate stabilization requires the mobilization of substantial investments in low- and zero-carbon technologies, especially in emerging and developing economies. However, access to stable and affordable finance varies dramatically across countries. Models used to evaluate the energy transition do not differentiate regional financing costs and therefore cannot study risk-sharing mechanisms for renewable electricity generation. In this study, we incorporated the empirically estimated cost of capital differentiated by country and technology into an ensemble of five climate–energy–economy models. We quantified the additional financing cost of decarbonization borne by developing regions and explored policies of risk premium convergence across countries. We found that alleviating financial constraints benefits both climate and equity as a result of more renewable and affordable energy in the developing world. This highlights the importance of fair finance for energy availability, affordability and sustainability, as well as the need to include financial considerations in model-based assessments.