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Spinal cord injuries (SCIs) pose significant challenges as complete nerve regeneration remains limited. The demand for improved technologies in SCI treatment is evident. One such emerging technology is three-dimensional printing (3DP), which, coupled with advancements in medical imaging and bioengineering, has significantly enhanced precision in surgical procedures. This systematic review aims to explore 3DP as a treatment option for SCIs, examining its cost, efficacy, safety, and the associated technological constraints. A systematic search of Medline was conducted through PubMed for literature published since 2019. The search results were exported to Rayyan for abstract and full-text screening following predefined criteria. The risk of bias in the selected studies was assessed using the RoB2 tool and the Newcastle-Ottawa Scale. From a total of 89 articles screened, 11 studies met the eligibility criteria, collectively assessing 237 individuals with various types of SCIs, including lumbar degeneration, en bloc resection of thoracolumbar metastasis, adult spinal deformity, and cervical degeneration. These studies examined the utilization of 3DP devices such as hand orthosis, interbody fusion cages, lamellar titanium cages, artificial vertebral bodies, and others. Most of the reviewed studies reported positive treatment outcomes, with the actual procedure costs varying from $65 to $5,000. Recent literature shows positive outcomes in the use of 3DP technologies for SCIs, highlighting its potential for enhancing both surgical and nonsurgical interventions. These advancements usher in a new era in SCI treatment, providing enhanced precision and a wider range of treatment options, ultimately leading to more comprehensive and effective patient care. [ABSTRACT FROM AUTHOR]

The communication environment in traffic corridors such as the Sichuan-Tibet Railway is generally unsatisfactory, making it challenging to upload Land surface temperature (LST) monitoring data to a cloud server for large-scale and accurate prediction in the seasonally frozen ground region. Instead, the LST prediction can be conducted individually on local edge devices; but, the prediction accuracy may decrease due to insufficient monitoring data. Here, we propose a federated learning-based approach for LST prediction considering cost-efficiency in seasonally frozen ground region. The proposed method can reduce communication cost while achieving higher accuracy in prediction. Federated learning is a machine learning paradigm that enables the training of models on decentralised data sources. In the proposed method, (1) the initial parameters are distributed to monitoring stations; (2) the parameters are updated by monitoring stations using local data and aggregated to obtain the latest global parameters; (3) the latest parameters are delivered to the selected clients for the subsequent updates. The proposed method was applied at three monitoring stations in Southeast Tibet by comparing with the individual learning-based approach. Results indicated that the proposed approach achieves an average reduction of 14.0% in Mean Absolute Error and 13.6% in Root Mean Square Error. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) applications are rapidly growing, and more applications are joining the market competition. As a result, the AI-as-a-service (AIaaS) model is experiencing rapid growth. Many of these AIaaS-based applications are not properly optimized initially. Once they start experiencing a large volume of traffic, different challenges start revealing themselves. One of these challenges is maintaining a profit margin for the sustainability of the AIaaS application-based business model, which depends on the proper utilization of computing resources. This paper introduces the resource award predictive (RAP) model for AIaaS cost optimization called RAP-Optimizer. It is developed by combining a deep neural network (DNN) with the simulated annealing optimization algorithm. It is designed to reduce resource underutilization and minimize the number of active hosts in cloud environments. It dynamically allocates resources and handles API requests efficiently. The RAP-Optimizer reduces the number of active physical hosts by an average of 5 per day, leading to a 45% decrease in server costs. The impact of the RAP-Optimizer was observed over a 12-month period. The observational data show a significant improvement in resource utilization. It effectively reduces operational costs from USD 2600 to USD 1250 per month. Furthermore, the RAP-Optimizer increases the profit margin by 179%, from USD 600 to USD 1675 per month. The inclusion of the dynamic dropout control (DDC) algorithm in the DNN training process mitigates overfitting, achieving a 97.48% validation accuracy and a validation loss of 2.82%. These results indicate that the RAP-Optimizer effectively enhances resource management and cost-efficiency in AIaaS applications, making it a valuable solution for modern cloud environments. [ABSTRACT FROM AUTHOR]

An oncological diagnosis carries a weight that will inevitably diminish the quality of the patients' life. Despite that, some factors could be influenced, so that the overall outcome to be at least preserved, if not slightly enhanced. The oncological patients are of a great complexity when it comes to rehabilitation care, the reason being the fact that besides the malignancy, these patients also present a variety of other diseases, just like any other patient. Moreover, psychological involvement is incomparably more present and harder to deal with. Psychological status in oncological patients is influenced by the ability to internalize the diagnosis, associated comorbidities such as depression, anxiety, carious and pulmonary diseases or nutritional status and toxicology. Cost-effectiveness is a necessity to be proved for the rehabilitation programs to be actually included in the oncological patients' care throughout every stage of the disease, but despite the need, it still has to be furtherly analyzed for obtaining valid results. It is essential to acknowledge that cancer patients should not be denied therapeutic approaches that positively impact their overall condition and functionality. Rehabilitation strategies are instruments in supporting the patient's journey, helping them maintain or regain a sense of control, independence, and an improved overall quality of life. [ABSTRACT FROM AUTHOR]

People's names are challenging to learn at all ages. Because people somewhat know this, they might spontaneously use cost-efficient encoding strategies and devote more resources to learn names that are most likely to be useful. To test this hypothesis, we created a pseudo-incidental learning situation in which young and older participants were exposed to 12 characters from a TV show and reviewed face-name-instrument triplets. Characters' probability of appearance was specified via importance labels (main or secondary characters, bit parts). A surprised cued recall test showed that young adults performed better than older ones, and that semantic information was better recalled than names. Consistent with cost-efficient encoding strategies, participants in both groups recalled names and semantic information about most important characters better. Interestingly, there were large individual differences: people who reported using cost-efficient strategies performed better. At the individual level, memory advantages for most important characters' names and semantic information correlated. [ABSTRACT FROM AUTHOR]

Inspections often perform imperfect outcomes during maintenance processes owing to human errors, management issues and other limitations. In particular, such imperfection affects the maintenance management of multistage deterioration significantly due to both false state identification and measurement errors, whose quantitative analysis, however, is seldom reported in the literature. To fill these gaps, this paper devises a condition‐based maintenance management strategy oriented to two‐stage continuous degradation under two‐dimensional inspection imperfection. Specifically, a threshold‐based replacement is executed under the normal‐working state if the detected degradation value exceeds the preset limit; additionally, preventive replacement is immediately performed once the defective state is identified. Notably, the detection outcome rather than the actual working condition decides how preventive maintenance operates. The long‐run cost rate is minimized via the optimization of the inspection cycle and replacement limit. Besides, numerical experiments conducted on train bogie bearing are provided, showing substantial superiorities over cost‐effectiveness promotion and performance improvement. [ABSTRACT FROM AUTHOR]

Objectives: This manuscript presents a comprehensive framework for the assessment of the value of real-world evidence (RWE) in healthcare decision-making. While RWE has been proposed to overcome some limitations of traditional, one-off studies, no systematic framework exists to measure if RWE actually lowers the burden. This framework aims to fill that gap by providing conceptual approaches for evaluating the time and cost efficiencies of RWE, thus guiding strategic investments in RWE infrastructure. Methods: The framework consists of four components: (114th Congress. 21st Century Cures Act.; 2015. https://www.congress.gov/114/plaws/publ255/PLAW-114publ255.pdf.) identification of stakeholders using and producing RWE, (National Health Council. Glossary of Patient Engagement Terms. Published 2019. Accessed May 18. 2021. https://nationalhealthcouncil.org/glossary-of-patient-engagement-terms/.) understanding value propositions on how RWE can benefit stakeholders, (Center for Drug Evaluation and Research. CDER Patient-Focused Drug Development. U.S. Food & Drug Administration.) defining key performance indicators (KPIs), and (U.S. Department of Health and Human Services - Food and Drug Administration: Center for Devices and Radiological Health and Center for Biologics Evaluation and Research. Use of Real-World Evidence to Support Regulatory Decision-Making for Medical Devices - Guidance for Industry and Food and Drug Administration Staff. 2017. http://www.fda.gov/BiologicsBloodVaccines/GuidanceComplianceRegulatoryInformation/Guida.) establishing metrics and case studies to assess value. KPIs are categorized as 'better, faster, or cheaper" as an indicator of value: better focusing on high-quality actionable evidence; 'faster,' denoting time-saving in evidence generation, and 'cheaper,' emphasizing cost-efficiency decision compared to methodologies that do not involve data routinely collected in clinical practice. Metrics and relevant case studies are tailored based on stakeholder value propositions and selected KPIs that can be used to assess what value has been created by using RWE compared to traditional evidence-generation approaches and comparing different RWE sources. Results: Operationalized through metrics and case studies drawn from the literature, the value of RWE is documented as improving treatment effect heterogeneity evaluation, expanding medical product labels, and expediting post-market compliance. RWE is also shown to reduce the cost and time required to produce evidence compared to traditional one-off approaches. An original example of a metric that measures the time saved by RWE methods to detect a signal of a product failure was presented based on analysis of the National Cardiovascular Disease Registry. Conclusions: The framework presented in this manuscript offers a comprehensive approach for evaluating the value of RWE, applicable to all stakeholders engaged in leveraging RWE for healthcare decision-making. Through the proposed metrics and illustrated case studies, valuable insights are provided into the heightened efficiency, cost-effectiveness, and improved decision-making within clinical and regulatory domains facilitated by RWE. While this framework is primarily focused on medical devices, it could potentially inform the determination of RWE value in other medical products. By discerning the variations in cost, time, and data utility among various evidence-generation methods, stakeholders are empowered to invest strategically in RWE infrastructure and shape future research endeavors. [ABSTRACT FROM AUTHOR]

Over the past decade, political advertising via social media has grown rapidly, spurred by microtargeting, which looks to deliver specific messages to tightly defined audiences. Microtargeting strategies have been claimed to be effective, but questions remain around their cost, when looking to optimise impressions for a given budget. We analyse 11,837 ads aired on Snapchat over a two-year period leading up to the 2020 presidential election in the United States, which differ in the number of targeting criteria applied. We compare the number of impressions and the spend per ad placement (measured in CPM), whilst also considering the length of advertising schedule. We find that using fewer targeting criteria and longer schedules increases impressions with comparable or lower spend than microtargeting. These findings are in line with those from traditional broadcast media, such as TV, highlighting the relevance of existing media scheduling knowledge from traditional platforms for political advertising on newer, digital media. [ABSTRACT FROM AUTHOR]

Cloud computing has revolutionized various domains over the past decade, providing accessible computational and storage resources at reduced costs. The exponential growth in data volumes and processing complexity, particularly due to the proliferation of IoT devices and applications across fields such as business, education, and agriculture, requires scalable computing resources and efficient processing. Workflow scheduling in cloud computing, an NP-hard optimization problem, involves allocating resources to tasks within a workflow and determining their execution sequence. Despite numerous heuristic, metaheuristic, and hybrid approaches, there remains a need for scheduling algorithms with lesser computational complexity to optimize makespan and cost efficiency, as well as ensure SLA compliance. This paper introduces a novel multi-objective metaheuristic solution, the Deadline and Budget constrained Archimedes Optimization Algorithm (ADB), which addresses workflow scheduling by optimizing makespan and cost while adhering to deadline and budget constraints. Extensive experiments on a well-known cloud simulation tool, Workflowsim, using scientific workflows demonstrate significant improvements in makespan (20%), cost (5%), resource utilization (15%), and energy consumption (9%). Performance observations on Pareto optimality metrics show that our approach has a higher hypervolume for 80% cases, it dominates state of the art by at least 83%, and the s-metric value of our approach is lower for 95% cases, alongside statistical validation using t-tests and ANOVA, confirming the efficacy of our method compared to state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Hongwei Huang,1 Jiacheng Mo,2 Gui Jiang,2 Zheng Lu1 1Intensive Care Unit, Guangxi Hospital Division of the First Affiliated Hospital, Sun Yat-Sen University, Nanning, Guangxi, 530022, People’s Republic of China; 2Intensive care unit, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, 530021, People’s Republic of ChinaCorrespondence: Zheng Lu, Intensive Care Unit, Guangxi Hospital Division of the First Affiliated Hospital, Sun Yat-sen University, Nanning, Guangxi, 530022, People’s Republic of China, Email iculuzheng@163.comPurpose: To evaluate the long-term efficacy and cost-efficiency of blood purification (BP) in severe acute pancreatitis (SAP) through single-center data.Patients and Methods: A total of 155 SAP patients were collected and followed up for 6 months. The participants were divided into control (49 cases) and BP group (106 cases) according to whether they received BP treatment or not. The primary outcomes were 6-month mortality, length of hospital stay, and hospitalization costs. Propensity score matching (PSM) analysis was performed based on various factors such as gender, age, etiology, SOFA score, JSS score, and creatinine value on day 1.Results: There were significant differences in all baseline data between BP and control groups (p< 0.05). However, there was a significant difference in the mortality, length of hospital stay, hospital costs and infection aggravation rate the in outcome data for 6-months (all p< 0.05). BP was not considered a death factor in any adjusted models, with p-values ranging from 0.81 to 0.93. The results of subgroup analysis after PSM showed that BP mode had no significant impact on prognostic indicators, but the length of ICU stay and total costs were significantly increased (all p< 0.001). There was no significant difference in mortality among the cases that did not require early intervention after 6 months (p=0.487). However, the patients in BP group had longer ICU stays (p=0.001) and higher hospitalization costs (p< 0.001) compared to the control group.Conclusion: The utilization of BP therapy did not decrease the 6-month mortality in SAP patients. Additionally, BP therapy has a significant impact on the duration of ICU stay or hospitalization expenses. However, the effectiveness and cost-efficiency of this therapy are unsatisfactory, and early intervention does not enhance survival benefits. Furthermore, there was no substantial variation in survival benefits between continuous veno-venous hemofiltration (CVVH) alone and compound BP.Keywords: severe acute pancreatitis, blood purification, long-term efficacy, cost-efficiency

We examine the cost efficiency of local police agencies in the US for 2016. We employ a multi-output translog stochastic frontier model and show that reductions in inefficiency would yield significant budgetary savings and improved performance in service delivery. [ABSTRACT FROM AUTHOR]

Bundled payment programs for total joint arthroplasty (TJA) have become popular among both private and public payers. Because these programs provide surgeons with financial incentives to decrease costs through reconciliation payments, there is an advantage to identifying and emulating cost-efficient surgeons. The objective of this study was to utilize the Centers for Medicare and Medicaid Services (CMS) Quality Payment Program (QPP) in combination with institutional data to identify cost-efficient surgeons within our region and, subsequently, identify cost-saving practice patterns. Data was obtained from the CMS QPP for total knee arthroplasty (TKA) and total hip arthroplasty (THA) surgeons within a large metropolitan area from January 2019 to December 2021. A simple linear regression determined the relationship between surgical volume and cost-efficiency. Internal practice financial data determined whether patients of identified surgeons differed with respect to x-ray visits, physical therapy visits, out-of-pocket payments to the practice, and whether surgery was done in hospital or surgical center settings. There were 4 TKA and 3 THA surgeons who were cost-efficiency outliers within our area. Outliers and nonoutlier surgeons had patients who had similar body mass index, American Society of Anesthesiologists Physical Status Score, and age-adjusted Charlson Comorbidity Index scores. Patients of these surgeons had fewer x-ray visits for both TKA and THA (1.06 versus 1.11, P <.001; 0.94 versus 1.15, P <.001) and lower out-of-pocket costs ($86.10 versus $135.46, P <.001; $116.10 versus $177.40, P <.001). If all surgeons performing > 30 CMS cases annually within our practice achieved similar cost-efficiency, the savings to CMS would be $17.2 million for TKA alone ($75,802,705 versus $93,028,477). The CMS QPP can be used to identify surgeons who perform cost-efficient surgeries. Practice patterns that result in cost savings can be emulated to decrease the cost curve, resulting in reconciliation payments to surgeons and institutions and cost savings to CMS. [ABSTRACT FROM AUTHOR]

Background Control of schistosomiasis (SCH) relies on the regular distribution of preventive chemotherapy (PC) over many years. For the sake of sustainable SCH control, a decision must be made at some stage to scale down or stop PC. These "stopping decisions" are based on population surveys that assess whether infection levels are sufficiently low. However, the limited sensitivity of the currently used diagnostic (Kato-Katz [KK]) to detect low-intensity infections is a concern. Therefore, the use of new, more sensitive, molecular diagnostics has been proposed. Methods Through statistical analysis of Schistosoma mansoni egg counts collected from Burundi and a simulation study using an established transmission model for schistosomiasis, we investigated the extent to which more sensitive diagnostics can improve decision making regarding stopping or continuing PC for the control of S. mansoni. Results We found that KK-based strategies perform reasonably well for determining when to stop PC at a local scale. Use of more sensitive diagnostics leads to a marginally improved health impact (person-years lived with heavy infection) and comes at a cost of continuing PC for longer (up to around 3 years), unless the decision threshold for stopping PC is adapted upward. However, if this threshold is set too high, PC may be stopped prematurely, resulting in a rebound of infection levels and disease burden (+45% person-years of heavy infection). Conclusions We conclude that the potential value of more sensitive diagnostics lies more in the reduction of survey-related costs than in the direct health impact of improved parasite control. [ABSTRACT FROM AUTHOR]

At the beginning of factor investing research, the investment universe concentrated on developed markets and transaction costs were paid little attention. Expensive trading costs of factor investing in emerging equity markets influence optimal portfolio decisions. Based on a total costs estimate of factor-based portfolio tilts, a simple cost-mitigation approach increases net performance. Exploiting the structure of market impact, we indirectly control the costs by limiting order sizes relative to their underlying stocks' short-term liquidity. This cost-efficient strategy yields better implementability and lower-priced turnover while a possible negative effect on gross performance is more than offset. [ABSTRACT FROM AUTHOR]

This paper examines the impact of cost-efficiency on bank profitability in the context of the Covid-19 pandemic using quarterly data from a panel of banks operating in five (5) Sub-Saharan African countries. The findings show that cost-efficient banks are more profitable amid the pandemic. Additionally, we found that revenue diversification is associated with strong financial performance. Our findings contribute to literature by proposing the adoption of aggressive cost control and revenue diversification strategies by the management of banks to ensure sustainable and resilient financial performance in times of crises when interest incomes are sticky. [ABSTRACT FROM AUTHOR]

A major challenge for the Air Traffic Management system is the need to boost airspace capacity, which is near saturation in some situations. Separation minima are one of the factors related to airspace capacity, and the SESAR program promotes research into advanced separation modes. Ad Hoc separation, a novel separation mode, involves applying different pairwise separation minima in the same volume of airspace depending on a set of factors, such as encounter geometry, aircraft models, and flight level, among others. This research examines the impact of implementing this concept in different en-route scenarios. The goal is to determine whether applying this concept proves advantageous or, conversely, results in an increase in the complexity of the system without significant benefits in the key performance areas of capacity, environment, and cost-efficiency. Fast Time Simulations are conducted in RAMS software, with the concept being implemented in the LECMZMU, LECMTLU, and LECMDGU sectors of the Madrid ACC. The results reveal favorable capacity outcomes with increases of around 2% and the LECMZMU sector exhibits the most significant environmental and cost-efficiency benefits. Furthermore, implementing the Ad Hoc concept in a larger scenario could yield even greater environmental and cost-efficiency benefits. [ABSTRACT FROM AUTHOR]

The poor cost-effectiveness of mechanized planting (MECP) is the main reason for the low mechanization rate of planting. In this study, we investigated the productivity of the mechanized excavator-based planting of Norway spruce (Picea abies [L.] H. Karst.) seedlings based on data collected by the Risutec Asta documentation system. We also compared the costs of a MECP chain with two different manual planting (MAP) chains, where mounding was carried out by a crawler excavator (EXC) or a continuously advancing mounder (CONT). The MECP of seedlings was carried out using an EXC equipped with a Risutec PM-160 planting device. Generally, the nine study sites in western Finland contained few surface obstacles (e.g., the logging residues had mainly been harvested), which made the conditions very suitable for MECP. The average production time taken by the MECP was 9 h ha-1. The operating hour (G15-h) productivity averaged 215 seedlings G15-h-1 , with the mean planting time being 13.8 s seedling-1. Loading 160 seedlings into the seedling cassette took approximately 10 min (3.8 s seedling-1). Overall, the cost of the MECP was about 5% lower than for the EXC + MAP. However, when productivity was set at

Restoration of degraded habitat is frequently used in ecological compensation. However, ecological restoration suffers from innate problems of long delivery times of features shown to be good proxies for biodiversity, e.g., large dead trees. We tested a possible way to circumvent this problem; the translocation of hard-to-come deadwood substrates from an impact area to a compensation area. Following translocation, deadwood density in the compensation area was locally equivalent to the impact area, around 20 m3 ha−1, a threshold for supporting high biodiversity of rare and red-listed species. However, deadwood composition differed between the impact and compensation area, showing a need to include more deadwood types, e.g., late decomposition deadwood, in the translocation scheme. To guide future compensation efforts, the cost for translocation at different spatial scales was calculated. We conclude that translocation of deadwood could provide a cost-efficient new tool for ecological compensation/restoration but that the method needs refinement. [ABSTRACT FROM AUTHOR]

Deep neural networks bear substantial cloud computational loads and often surpass client devices' capabilities. Research has concentrated on reducing the inference burden of convolutional neural networks processing images. Unstructured pruning, which leads to sparse matrices requiring specialized hardware, has been extensively studied. However, neural networks trained with tabular data and structured pruning, which produces dense matrices handled by standard hardware, are less explored. We compare two approaches: 1) Removing neurons followed by training from scratch, and 2) Structured pruning followed by fine-tuning through additional training over a limited number of epochs. We evaluate these approaches using three models of varying sizes (1.5, 9.2, and 118.7 million parameters) from Kaggle-winning neural networks trained with tabular data. Approach 1 consistently outperformed Approach 2 in predictive performance. The models from Approach 1 had 52%, 8%, and 12% fewer parameters than the original models, with latency reductions of 18%, 5%, and 5%, respectively. Approach 2 required at least one epoch of fine-tuning for recovering predictive performance, with further fine-tuning offering diminishing returns. Approach 1 yields lighter models for retraining in the presence of concept drift and avoids shifting computational load from inference to training, which is inherent in Approach 2. However, Approach 2 can be used to pinpoint the layers that have the least impact on the model's predictive performance when neurons are removed. We found that the feed-forward component of the transformer architecture used in large language models is a promising target for neuron removal.

Our community is still far away from achieving self‐sustainable ambient intelligence, since it calls for rational energy layouts to satisfy the ubiquitous power demands from diverse terminal products. Harnessing energy directly from the surroundings thus provides ideal solutions. The majority of existing environmental harvesters rely on sophisticated procedures and expensive or toxic materials; while others attempt to streamline the complexity at the cost of compromising performance. This entails transducers that exhibit superb outputs and also employ cost‐effective, even recycled materials and straightforward protocols to render ubiquitous deployments. Here, a high‐efficiency droplet energy nanogenerator (DENG) is devised to satisfy all the requirements. The DENG is fabricated by directly coating a composite layer on a recycled digital video disk surface. It achieves superb electricity generation from one droplet, with an output voltage of >190 V at an instantaneous power density of 65 W m−2, and an energy conversion efficiency of 3.60%. Diverse demonstrations confirm the applicability of the DENG in environmental networks, encompassing self‐sustainable "on plants" sensing systems, smart building windows, and remote environmental monitoring platforms. In light of these superiorities, it is believed that the DENG may open up new alternative routes for future energy strategies. [ABSTRACT FROM AUTHOR]

Spontaneous brain activity exhibits a highly structured modular organization that varies across individuals and reconfigures over time. Although it has been proposed that brain organization is shaped by an economic trade-off between minimizing costs and facilitating efficient information transfer, it remains untested whether modular variability and its changes during unconscious conditions might be constrained by the economy of brain organization. We acquired functional MRI and FDG-PET in rats under three different levels of consciousness induced by propofol administration. We examined alterations in brain modular variability during loss of consciousness from mild sedation to deep anesthesia. We also investigated the relationships between modular variability with glucose metabolism and functional connectivity strength as well as their alterations during unconsciousness. We observed that modular variability increased during loss of consciousness. Critically, across-individual modular variability is oppositely associated with functional connectivity strength and cerebral metabolism, and with deepening dosage of anesthesia, becoming increasingly dependent on basal metabolism over functional connectivity. These results suggested that, propofol-induced unconsciousness may lead to brain modular reorganization, which are putatively shaped by re-negotiations between energetic resources and communication efficiency. [ABSTRACT FROM AUTHOR]

The financial viability of Sub-Sahara Africa (SSA) electricity sectors has become a central energy policy issue in recent years. This follows persistent under-recovery of costs which are amongst some of the highest in the world. Discussions, however, focus on tariff and utility reforms and inadequately on costs efficiency and the role of broader sector reforms in persistently high costs. Through a synthesis of reform theories and case studies and using small electricity systems as a surrogate for liberalized electricity sectors without competitive markets, this paper examines the connection between sector reforms and costs. It brings an economic perspective to the discussion on utility financial performance in SSA electricity systems and the need for a holistic policy approach for sustainable cost-recovery. In this, we recommend the promotion of mobile power plants to facilitate contestability in generation and need for non-island small systems to participate in regional power markets to neutralize the scale limitations of their autarkic demand. Utilities and regulatory agencies should form platforms to share information on cost opportunities and possibilities to inform procurement designs and regulatory benchmarks. Regional markets could partner with national governments to develop subsidy schemes such as Contracts for Differences to remove rigidities imposed by national power purchasing contracts to promote deeper participation of small systems in regional power markets. Yardstick competition in the distribution segment remains viable in many small electricity systems and should be pursued at the regional level in the short run to medium term and at the national levels in the long term following deconcentration and the introduction of private sector participation. [ABSTRACT FROM AUTHOR]

Context: Wildfires have severe impacts on landscapes' hydrological and sediment processes. They are linked to events such as flash floods and droughts, and high erosion rates which lead to loss of soil organic matter and detachment of seeds and seedlings. Mulching is an effective measure implemented directly after a fire to reduce soil erosion and increase soil water retention. However, its implementation has proved a challenge, mainly due to factors such as cost and public acceptance. Objectives: This research aims to optimize the application of post-fire mulching by using decision-making criteria to select "how" and "where" the technique should be used. The specific objectives were to: (i) investigate the decision-making criteria on "how" to apply mulch by interviewing experts; (ii) define the cost-effectiveness relations of erosion modelling scenarios. Methods: The Monchique 2003 wildfire in Southern Portugal was used as a case study Experts' interviews and literature review were used to construct prioritization scenarios. Post-fire soil erosion was then modelled with the Revised Universal Soil Loss Equation (RUSLE) model and the Sediment Connectivity Index for the three resulting scenarios (the "Soil" scenario, considering the net potential erosion; the "Water" scenario, focusing on th1e protection of water bodies from sedimentation; and the "Road" scenario, focusing on road protection); and at two erosion thresholds (1 and 10 Mg ha−1 year−1). Results: The interviews and the literature review highlighted the importance of socio-economic parameters when it comes to mulch application. Moreover, models showed that small interventions, aimed at areas nearby water bodies and road networks can be more cost-effective than large interventions. Conclusions: Models helped to create a hierarchy of scenarios, enabling land managers to assess decision making tools at the landscape level, linking their priorities with practical issues of emergency stabilization practices. [ABSTRACT FROM AUTHOR]

In the realm of sustainable IoT and AI applications for the well-being of elderly individuals living alone in their homes, falls can have severe consequences. These consequences include post-fall complications and extended periods of immobility on the floor. Researchers have been exploring various techniques for fall detection over the past decade, and this study introduces an innovative Elder Fall Detection system that harnesses IoT and AI technologies. In our IoT configuration, we integrate RFID tags into smart carpets along with RFID readers to identify falls among the elderly population. To simulate fall events, we conducted experiments with 13 participants. In these experiments, RFID tags embedded in the smart carpets transmit signals to RFID readers, effectively distinguishing signals from fall events and regular movements. When a fall is detected, the system activates a green signal, triggers an alarm, and sends notifications to alert caregivers or family members. To enhance the precision of fall detection, we employed various machine and deep learning classifiers, including Random Forest (RF), XGBoost, Gated Recurrent Units (GRUs), Logistic Regression (LGR), and K-Nearest Neighbors (KNN), to analyze the collected dataset. Results show that the Random Forest algorithm achieves a 43% accuracy rate, GRUs exhibit a 44% accuracy rate, and XGBoost achieves a 33% accuracy rate. Remarkably, KNN outperforms the others with an exceptional accuracy rate of 99%. This research aims to propose an efficient fall detection framework that significantly contributes to enhancing the safety and overall well-being of independently living elderly individuals. It aligns with the principles of sustainability in IoT and AI applications. [ABSTRACT FROM AUTHOR]

Xanthan gum, an anionic polysaccharide with an exceptionally high molecular weight, is produced by the bacterium Xanthomonas sp. It is a versatile compound that has been utilized in various industries for decades. Xanthan gum was the second exopolysaccharide to be commercially produced, following dextran. In 1969, the US Food and Drug Administration (FDA) approved xanthan gum for use in the food and pharmaceutical industries. The food industry values xanthan gum for its exceptional rheological properties, which make it a popular thickening agent in many products. Meanwhile, the cosmetics industry capitalizes on xanthan gum's ability to form stable emulsions. The industrial production process of xanthan gum involves fermenting Xanthomonas in a medium that contains glucose, sucrose, starch, etc. as a substrate and other necessary nutrients to facilitate growth. This is achieved through batch fermentation under optimal conditions. However, the increasing costs of glucose in recent years have made the production of xanthan economically unviable. Therefore, many researchers have investigated alternative, cost‐effective substrates for xanthan production, using various modified and unmodified raw materials. The objective of this analysis is to investigate how utilizing different raw materials can improve the cost‐efficient production of xanthan gum. [ABSTRACT FROM AUTHOR]

Non-point source (NPS) pollution has become a primary threat to the water environment. With the implementation of numerous control projects, striking a balance between economic costs and efficiency has become an urgent issue for decision-makers. To identify the optimization solutions for best management practices (BMPs) at the watershed scale, a genetic algorithm and the Soil and Water Assessment Tool (SWAT) model were combined to evaluate the trade-offs of NPS control strategies. The BMPs considered in this model included soil testing and fertilizer application technology, integrated pest management projects, conservation tillage, lakeside belt ecological construction, forest construction, and vegetation filter strips. The decision variables are the type and size of the BMPs, with the objective of minimizing costs while simultaneously maximizing both total phosphorus (TP) reduction and ecosystem service value. TP reduction was simulated usinga calibrated SWAT model that successfully assessed NPS pollution in the Dianchi lake watershed. Considering NPS generation and transfer, three NPS control scenarios were designed: source reduction, process retention, and integrated control. The upper limits of the costs for three scenarios were 157, 4921, and 5013 million yuan, respectively. It is suggested that the ecological benefits should be taken into account in the BMPs design. This finding is conducive to generating optimized control schemes that can effectively balance the trade-offs between socioeconomic costs and environmental sustainability.

The rapid growth in oil palm production has raised environmental concerns due to the massive discharge of liquid and solid waste. To address this issue, waste generated need to be optimized by composting and converting the waste into organic fertilizer. The practice of pure inorganic fertilization must also be discontinued due to the rising cost of imported fertilizers and their detrimental effect on the soil. Furthermore, several studies have established that recycling self-produced organic waste as fertilizer is an excellent method to achieve sustainability. Therefore, this study investigates a significant response to organic fertilization treatment. The bioorganic empty fruit bunch (EFB) compost was tested on Podzols soil (Typic Placorthods) in the nursery and in the young mature stage of the palm that was treated from the beginning of planting. The results showed that organic fertilization using low and high concentrations of fresh and composted EFB had a more significant effect on seedling growth. A high dose of composted EFB positively affected the N and K content of the leaf. Composted EFB had added value, especially in improving soil chemical properties and assimilation of N nutrients by oil palm seedlings, especially Podzols (Typic Placorthods). Pure inorganic fertilization on Podzols (Typic Placorthods) could not have been more practical due to inadequate nutrient uptake. Composted EFB had a positive effect superior that of the fresh EFB on the chemical composition of the soil, such as improving the saturation rate of the cation exchange capacity and the exchangeable K and Mg. Fresh EFB was good, but composted EFB was better for maintaining yield and less attractivity to Oryctes, which is essential in replanting.

The effects of rising concentration on markups in the meatpacking industry have sparked ongoing global debate. In this vein, our study aimed to decompose these effects into two opposing components: market power and cost‐efficiency effects. We applied our model to the broiler packing industry in South Korea. Our findings indicate that cost‐efficiency effects decreased sharply from 74.642 in 2017 to ‐145.860 in 2021, while market power effects remained stable during the study period. We found that the decrease in cost‐efficiency effects has begun to counteract the market power effects, which can largely be attributed to profix‐maximizing behavior of broiler packers in response to the bird flu outbreak. Moreover, we highlighted that non‐material price changes were a significant source of the reduction in cost‐efficiency effects, which underscores the necessity of policy efforts to manage factor market situations in order to mitigate the negative consequences of market concentration. [EconLit Citations: D43, L11, Q13]. [ABSTRACT FROM AUTHOR]

In this work, a cost-efficient space-time adaptive algorithm based on the Dual Weighted Residual (DWR) method is developed and studied for a coupled model problem of flow and convection-dominated transport. Key ingredients are a multirate approach adapted to varying dynamics in time of the subproblems, weighted and non-weighted error indicators for the transport and flow problem, respectively, and the concept of space-time slabs based on tensor product spaces for the data structure. In numerical examples, the performance of the underlying algorithm is studied for benchmark problems and applications of practical interest. Moreover, the interaction of stabilization and goal-oriented adaptivity is investigated for strongly convection-dominated transport. [ABSTRACT FROM AUTHOR]

Evidence on the cost of acute malnutrition treatment, particularly with regards to simplified approaches, is limited. The objective of this study was to determine the cost of acute malnutrition treatment and how it is influenced by treatment protocol and programme size. We conducted a costing study in Kabléwa and N'Guigmi, Diffa region, where children with acute malnutrition aged 6–59 months were treated either with a standard or simplified protocol, respectively. Cost data were collected from accountancy records and through key informant interviews. Programme data were extracted from health centre records. In Kabléwa, where 355 children were treated, the cost per child treated was USD 187.3 (95% CI: USD 171.4; USD 203.2). In N'Guigmi, where 889 children were treated, the cost per child treated was USD 110.2 (95% CI: USD 100.0; USD 120.3). Treatment of moderate acute malnutrition was cheaper than treatment of severe acute malnutrition. In a modelled scenario sensitivity analysis with an equal number of children in both areas, the difference in costs between the two locations was reduced from USD 77 to USD 11. Our study highlighted the significant impact of programme size and coverage on treatment costs, that cost can differ significantly between neighbouring locations, and that it can be reduced by using a simplified protocol. [ABSTRACT FROM AUTHOR]

• No universal consensus to manage patients with a Pregnancy of Unknown Location. • Prediction models are available to guide management. • Prediction models can risk-stratify this group of patients. • This can have patient and socio-economic benefits. The time period while delineating the final diagnosis following presentation with a pregnancy of unknown location (PUL) can be an anxious time, as well as being time and resource intensive. Prediction models have been utilised in order to tailor counselling, frame expectations and plan care. We aimed to review diagnoses of PUL in our population and assess the value of two prediction models. We reviewed all 394 PUL diagnoses over a three year period in a tertiary level maternity hospital. We then retrospectively applied the M1 and M6NP models to assess their accuracy when compared to the final diagnosis. PUL comprises of 2.9% (394/13401) of attendances in our unit, requiring 752 scans and 1613 separate blood tests. Just under one in ten women (9.9%, n = 39) presenting with a PUL had a viable pregnancy at discharge, however of the remainder, only 18.0% (n = 83) required medical or surgical treatment for a PUL. The M1 model was more successful at predicting an ectopic pregnancy than the M6NP, with the latter over-predicting viable pregnancies (33.4%, n = 77). We demonstrate that the management of women with a PUL could be stratified through the application of outcome prediction models, having positive results for framing expectations and potentially reducing this resource-intensive diagnosis. [ABSTRACT FROM AUTHOR]

Cloud computing and deep learning, the recent trends in the software industry, have enabled small companies to scale their business up rapidly. However, this growth is not without a cost – deep learning models are related to the heaviest workloads in cloud data centers. When the business grows, the monetary cost of deep learning in the cloud also grows fast. Deep learning practitioners should be prepared and equipped to limit the growing cost. We emphasize monetary cost instead of computational cost although often the same methods decrease both types of cost. We performed a systematic literature review on the methods to control the cost of deep learning. Our library search resulted in 16,066 papers from three article databases, IEEE Xplore, ACM Digital Library, and Scopus. We narrowed them down to 112 papers that we categorized and summarized. We found that: 1) Optimizing inference has raised more interest than optimizing training. Widely used deep learning libraries already support inference optimization methods, such as quantization, pruning, and teacher-student. 2) The research has been centered around image inputs, and there seems to be a research gap for other types of inputs. 3) The research has been hardware-oriented, and the most typical approach to control the cost of deep learning is based on algorithm-hardware co-design. 4) Offloading some of the processing to client devices is gaining interest and can potentially reduce the monetary cost of deep learning.

This paper presents a numerical assessment of the influence of green roofs applied in the urbanized catchment on the rainwater outflow hygrogram as well as costs and economic efficiency analysis of the proposed green architecture application. The campus basin of the Lublin University of Technology, Poland, was selected as the object of the study. Three variants of extensive green roof applications were designed. The numerical model of surface runoff was developed in US EPA's SWMM 5.2 software. The simulations were performed for three different rainfall events of various intensities and durations. The cost efficiency of the proposed green architecture was assessed by the Dynamic Generation Costs indicator, while economic effectiveness was tested by Benefits–Costs Ratio and Payback Period determined for all assumed variants. The determination of economic efficiency indicators was based on investment and maintenance costs estimation, assumed discount rate, and time duration of assessment. Results of numerical calculations showed up to 16.81% of peak flow and 25.20% of runoff volume reduction possibly due to the green roof application. All proposed variants of green roof applications in the studied urbanized catchment were assessed as generally profitable due to possible financial benefits related to heating and cooling energy savings and avoiding periodical change of bitumen roof cover. [ABSTRACT FROM AUTHOR]

Distributed denial of service (DDoS) detection is still an open and challenging problem. In particular, sophisticated attacks, e.g., attacks that disguise attack packets as benign traffic always appear, which can easily evade traditional signature-based methods. Due to the low requirements for computing resources compared to deep learning, many machine learning (ML)-based methods have been realistically deployed to address this issue. However, most existing ML-based DDoS detection methods are highly dependent on the features extracted from each flow, which incur remarkable detection delay and computation overhead. This article investigates the limitations of typical ML-based DDoS detection methods caused by the extraction of flow-level features. Moreover, we develop a cost-efficient window-based method that extracts features from a fixed number of packets periodically, instead of per flow, aiming to reduce the detection delay and computation overhead. The newly proposed window-based method has the advantages of well-controlled overhead and wide support of common routers due to its simplicity and high efficiency by design. Through extensive experiments on real datasets, we evaluate the performance of flow-based and window-based methods. The experimental results demonstrate that our proposed window-based method can significantly reduce the detection delay and computation overhead while ensuring detection accuracy. [ABSTRACT FROM AUTHOR]

A major challenge for the Air Traffic Management system is the need to boost airspace capacity, which is near saturation in some situations. Separation minima are one of the factors related to airspace capacity, and the SESAR program promotes research into advanced separation modes. Ad Hoc separation, a novel separation mode, involves applying different pairwise separation minima in the same volume of airspace depending on a set of factors, such as encounter geometry, aircraft models, and flight level, among others. This research examines the impact of implementing this concept in different en-route scenarios. The goal is to determine whether applying this concept proves advantageous or, conversely, results in an increase in the complexity of the system without significant benefits in the key performance areas of capacity, environment, and cost-efficiency. Fast Time Simulations are conducted in RAMS software, with the concept being implemented in the LECMZMU, LECMTLU, and LECMDGU sectors of the Madrid ACC. The results reveal favorable capacity outcomes with increases of around 2% and the LECMZMU sector exhibits the most significant environmental and cost-efficiency benefits. Furthermore, implementing the Ad Hoc concept in a larger scenario could yield even greater environmental and cost-efficiency benefits.

Lake Ecological Compensation (LEC) mechanism is a localized approach of payment for ecosystem services to address the conflict between economic development and ecological conservation. However, how to motivate regional stakeholders to participate in water quality protection is a challenge. Based on the traditional Coase Theorem, vertical eco-compensation mechanism has been proposed to solve pollution of lake basin. The quantification of LEC is characterized by key water quality indicators (NH3-N and COD) in this paper. Integrating LEC mechanism with the ecological-economic model, this paper proposes a bi-level optimization framework for the conservation of the lake water environment. Referring to Coase’s theory, the leader’s goal is to distribute waste load permits equally to sub-regions while followers aim to minimize environmental costs. The appropriate application of this method to Taihu Basin demonstrates its efficiency and practicality. The LEC mechanism with different scenarios is analyzed and suggestions for lake water quality are made. The results show that: (1) Considering equitable principle, the new COD allocation scheme has reduced the total amount of emissions by 17% compared to the data in 2020, and the total amount of NH3-N has decreased by 16%. (2) With the cooperation of lake basin institutions, the LEC mechanism is proved to be an effective measure in promoting the conservation of the lake water environment. (3) The spillover effect of environmental and ecological policies in lake water indicates the need to upgrade industrial structure. This paper proposes to provide a more reliable the conservation of the lake water environment paradigm.

Economists are divided over the potential impact of robotisation, especially when the effects on labour are considered. Several experts fear that the labour substitution effects will outpace the number of new jobs created, while others find that balancing forces will work this time as well. In this article, I focus on the impact of these processes on the Hungarian manufacturing industry, as Hungary is a good example of an FDI-dependent, production-focused country. The analysis is based on several interviews with experts and managers, mostly from the automotive industry. The result of the discussions shows that there is a growing robotisation trend in Hungary, along with the other Visegrad Four countries. While this trend decreases the number of workers needed for a certain production volume, it does not pose a serious immediate threat as it is balanced by the tendency of a lack of workforce with the necessary skills.

Despite the fast-developing momentum of perovskite solar cells (PSCs) toward flexible roll-to-roll solar energy harvesting panels, their long-term stability remains to be the challenging obstacle in terms of moisture, light sensitivity, and thermal stress. Compositional engineering including less usage of volatile methylammonium bromide (MABr) and incorporating more formamidinium iodide (FAI) promises more phase stability. In this work, an embedded carbon cloth in carbon paste is utilized as the back contact in PSCs (having optimized perovskite composition), resulting in a high power conversion efficiency (PCE) of 15.4%, and the as-fabricated devices retain 60% of the initial PCE after more than 180 h (at the experiment temperature of 85 °C and under 40% relative humidity). These results are from devices without any encapsulation or light soaking pre-treatments, whereas Au-based PSCs retain 45% of the initial PCE at the same conditions with rapid degradation. In addition, the long-term device stability results reveal that poly[bis(4–phenyl) (2,4,6–trimethylphenyl) amine] (PTAA) is a more stable polymeric hole-transport material (HTM) at the 85 °C thermal stress than the copper thiocyanate (CuSCN) inorganic HTM for carbon-based devices. These results pave the way toward modifying additive-free and polymeric HTM for scalable carbon-based PSCs. [ABSTRACT FROM AUTHOR]