Availability Constraints Research Articles

A techno-economic perspective on efficient hybrid renewable energy solutions in Douala, Cameroon’s grid-connected systems

Cameroon is currently grappling with a significant energy crisis, which is adversely affecting its economy due to cost, reliability, and availability constraints within the power infrastructure. While electrochemical storage presents a potential remedy, its implementation faces hurdles like high costs and technical limitations. Conversely, generator-based systems, although a viable alternative, bring their own set of issues such as noise pollution and demanding maintenance requirements. This paper meticulously assesses a novel hybrid energy system specifically engineered to meet the diverse energy needs of Douala, Cameroon. By employing advanced simulation techniques, especially the Hybrid Optimization Model for Electric Renewable (HOMER) Pro program, the study carefully examines the intricacies of load demands across distinct consumer categories while accommodating varied pricing models. The paper offers a detailed analysis of the proposed grid-connected PV/Diesel/Generator system, aiming to gauge its performance, economic feasibility, and reliability in ensuring uninterrupted energy supply. Notably, the study unveils significant potential for cost reduction per kilowatt-hour, indicating promising updated rates of $0.07/kW, $0.08/kW, and $0.06/kW for low, medium, and high usage groups, respectively. Furthermore, the research underscores the importance of overcoming operational challenges and constraints such as temperature fluctuations, equipment costs, and regulatory compliance. It also acknowledges the impact of operational nuances like maintenance and grid integration on system efficiency. As the world progresses towards renewable energy adoption and hybrid systems, this investigation lays a strong foundation for future advancements in renewable energy integration and energy management strategies. It strives to create a sustainable energy ecosystem in Cameroon and beyond, where hybrid energy systems play a pivotal role in mitigating power deficiencies and supporting sustainable development.

A periodic-modulation-oriented noise resistant correlation method for industrial fault diagnostics of rotating machinery under the circumstances of limited system signal availability

The periodical impulses caused by localized defects of components are the vital characteristic information for fault detection and diagnosis of rotating machines. In recent years, multitudinous spectrum analysis-based signal processing methods have been developed and authenticated as the powerful tools for excavating fault-related repetitive transients from the measured complex signals. Nonetheless, in practice, their applications can be severely confined by the constraints of limited system signal availability and incomplete information extraction under intricate noise interferences. To tackle the aforementioned issues, this paper proposes a periodic-modulation-oriented noise resistant correlation (PMONRC) method for target period detection and fault diagnosis of rotating machinery. Firstly, the envelope of raw signal is obtained via a novel sequential procedure of signal element-wise squaring, spectral Gini index-guided adaptive low-pass filtering, and signal element-wise square root computation, to highlight the modulated wave component that is more likely to be related to the potential fault-induced periods. Subsequently, a series of sub-signals, which can encode the fault-related repetitive information and enhance noise resistance, are constructed utilizing the envelope signal. Based upon the envelope signal and the obtained sub-signals, a weighted envelope noise resistant correlation function can be derived with the assistance of the L-moment ratio-based indicator and Sigmoid transformation. Finally, the specific fault type of the rotating machinery can be identified and affirmed accordingly. The proposed PMONRC method, which is nonparametric and completely adaptive to the signal being processed itself, overcomes the deficiencies of spectral analysis-based approaches, and is applicable for the engineering circumstances of system signal limitation and low signal-to-noise ratio (SNR), possessing immense practical merit. Both simulation analyses and experimental validations profoundly demonstrate that the proposed method is superior to other existing state-of-the-art time-domain correlation methods. Moreover, as an attempt as well as exemplar to apply this method, the PMONRC-based incipient fault diagnostic results of rolling bearing data from the well-known experimental platform PRONOSTIA are presented and discussed as well, to further elucidate the effectiveness and practical engineering significance of the proposed method.

Discounting Water for Optimal Carbon Gain as a Basis of Stomatal Closure

AbstractThe exchange of carbon dioxide and water vapor between terrestrial ecosystems and the atmosphere is regulated by stomata (small pores in the leaves of plants). Unsurprisingly, environmental factors controlling the opening and closure of stomata has been sought as early as 1800. One approach, popularized in the early 1970s, is a stomatal optimization framework. This framework is based on the hypothesis that plants optimize carbon gain subject to water loss or water availability constraints. This constraint optimization problem was solved in various forms assuming instantaneous adjustments of stomatal aperture to maximize a reward function with no future foresight or legacy effects. Holtzman et al. (2024, https://doi.org/10.1029/2023av001113) offers a novel approach that can diagnose the effective timescale over which the reward function maximization must be time‐integrated. The developed method thus optimizes an integrated carbon gain function but adjusted by a discount factor subject to water availability in the root zone. The discount factor considers how the plant values carbon gain to save water and its timescale can be inferred from observations because the model is analytically tractable. The results suggest that the most important climate factor that determines this discount timescale is multi‐annual mean of the longest dry period during the growing season. The findings highlight how local climate traits influence the spatial variation in ecosystem‐level water use strategies. This sets the stage for expanding such a framework to cases where multiple constraints act in concert while operating at distinct time scales.

AN INSIGHT INTO EBONY AND EBONIZATION OF WOOD

Ebony, a dense black/brown hardwood coming from several species of genus Diospyros, is facing a scarcity in market due to trade ban. This constraint in material availability has led to the adoption of ebonization techniques in other wood, wherein wood is treated to mimic the distinctive dark black colour of ebony. Ebonization process has become an important factor in industries looking for imitated wood products to meet the consumer preferences for aesthetic appeal of ebony-like wood. In this paper a concise insight into the current status of ebony (Diospyros ebenum) in India has been given with emphasis on the ebonization. The paper reviews the effectiveness of various ebonization methods, with focus acetate, as the most promising approach and challenges in this process.

Characterization of Acid Hydrolyzed Taro Boloso-I (Colocasia esculenta Cultivar) Starch as a Diluent in Direct Compression of Tablets.

Corn, wheat, rice, potato, and cassava starches have been widely used as pharmaceutical excipients. However, the search for cost-effective local starch alternatives is necessary due to the availability and usage constraints. In Ethiopia, various plant species, including Taro Boloso-I, have been explored as potential sources of pharmaceutical starch. It is a variety of Colocasia esculenta with a high tuber yield and high starch content. However, the native starch requires modifications to enhance its functionality. Therefore, this study aimed to improve the native starch through acid modification and evaluate its performance as a direct compressible tablet excipient. The native starch was treated with a 6% w/v HCl solution for 192 hours, resulting in acid-modified Taro Boloso-I starch, which was then evaluated for suitability for direct compression. XRD patterns of both the native and modified starch showed characteristic A-type crystals, with significantly higher relative crystallinity observed in the latter. Additionally, the acid-modified starch exhibited a lower moisture content and improved flow properties. The compaction study also demonstrated its improved compactibility (tensile strength: 16.82 kg/cm2), surpassing both the native starch (13.17) and Starch 1500® (11.2). The modified starch also showed a lower lubricant sensitivity compared to the native starch and Starch 1500®. Furthermore, paracetamol tablets made with the modified starch exhibited higher mechanical strength and lower friability in all paracetamol concentrations. It incorporated up to 40% paracetamol while maintaining acceptable tablet characteristics, whereas the native starch and Starch 1500® were limited to 30% (w/w). Based on these findings, the modified starch showed promise as an alternative direct compressible excipient in tablet manufacturing.

Assessing the Teaching Strategies and Techniques Used by TVL Teachers: Basis for Pedagogical Training Design

This research study aimed to evaluate the teaching strategies and techniques employed by Technical-Vocational-Livelihood (TVL) teachers, with a focus on informing the design of pedagogical training programs. Employing a qualitative approach through thematic analysis, the study engaged ten TVL Senior High School teachers from five schools in Lanao del Sur. Utilizing semi-structured questionnaires and individual interviews, the research delved into the diverse array of teaching methodologies utilized by TVL educators. The findings elucidate the pivotal role of these teachers in employing various pedagogical approaches such as lectures, demonstrations, oral questioning, and discussions to foster effective student learning. Despite the prevalence of conventional teaching methods, the adaptability of TVL instructors was evident as they tailored their strategies to accommodate factors like resource availability, experience levels, and financial constraints, ensuring sustained student engagement and learning effectiveness. However, the study uncovered significant challenges stemming from resource constraints, particularly in practical skill development. The scarcity of materials, facilities, laboratories, and adequately trained personnel hinders the attainment of desired learning outcomes and poses obstacles in aligning with the TVL curriculum. Notwithstanding these impediments, TVL teachers showcased a steadfast commitment to student-centered learning principles and technological integration. Leveraging advancements in technology, they seek to enrich teaching practices and augment the overall learning experience. Nevertheless, inequities in access to training opportunities emerged as a barrier to professional development, impeding the full exploitation of digital tools' potential. In conclusion, this thematic analysis offers valuable insights into the nuanced landscape of TVL education, highlighting the indispensable role of teachers in navigating challenges and maximizing opportunities for student learning and skill development. The findings underscore the imperative for targeted pedagogical training interventions to address existing gaps and empower TVL educators to deliver high-quality education effectively.

Initial Evaluation of the Merit of Guar as a Dairy Forage Replacement Crop during Drought-Induced Water Restrictions

Dairy is the largest sector of the agricultural industry in New Mexico, but the sector’s need for high quality forage in the face of ongoing drought and climate change causes concern. The economic merit of using guar to replace alfalfa under imposed constraints in irrigation water availability is evaluated. Nutritional quality of guar (Cyamopsis tetragonoloba L.) grown at the NMSU Agricultural Science Center at Clovis was estimated using Near Infrared Spectroscopy (NIRS) analysis. Results show that 45.6 percent of analyzed samples were comparable to Supreme or Premium alfalfa (Medicago sativa) hay using Relative Feed Value index, and 23.7% met this criteria using crude protein. While alfalfa uses 4.4 acre-feet of water (approximately 5400 cubic meters) per season, the guar sampled used 5 inches (500 cubic meters) of applied irrigation. Microsoft Excel Version 2404 LP Solver was used to identify an optimum crop mix of alfalfa and guar under different levels of irrigation water constraints. With increasing levels in modeled irrigation restrictions, the use of guar increased the potential forage production by 3% under a 0.4 acre-foot restriction up to almost 59% under a 1.5 acre-feet irrigation water limit. Our results merit additional research, including forage trials to determine the impact of guar forage on milk production and further agronomic research into growing guar to maximize forage yield.

Effect of wind variability on the recruitment of anchovy Engraulis ringens in the southern Humboldt upwelling ecosystem

AbstractRecruitment is a critical process in the dynamics of fish populations since it represents the abundance of new fish that enter a population each year. In Ekman‐type upwelling ecosystems, wind is a critical factor for small pelagic fish recruitment, as it controls food availability and physical constraints such as turbulence and offshore advection. In this study, we evaluate the effect of wind, chlorophyll‐a, and spawning biomass as independent variables on anchovy recruitment. Using generalized additive models (GAMs), we tested (i) the significance of each factor, (ii) the shape of the relationship between recruitment and predictors variables, (iii) recruitment regimes in the series, and (iv) whether extended windy events are associated with low recruitment levels potentially related to offshore transport of early life stages. The study period spanned from 1990 to 2021, focusing on the austral spring in south‐central Chile (35–40°S) when both upwelling and anchovy spawning are maximized. GAMs revealed a parabolic relationship between the velocity of upwelling‐favorable winds and anchovy recruitment. Maximized recruitment occurred at wind speeds around 6 m s−1 but decreased with winds above 7 m s−1 and below 5 m s−1, and during periods of extended windy events (i.e., those lasting more than 15 days). Chlorophyll‐a showed no significance on anchovy recruitment, suggesting that phytoplankton were not a limiting factor for anchovy early survival. We also found that anchovy recruitment regimes were associated with specific wind intensities. We discussed the pertinence of incorporating a recruitment index based on wind in the fishery management of the anchovy in south‐central Chile.

Seasonal shifts in the habitat selection patterns of male American Marten (Martes americana) at a fine spatial scale.

Old-growth forests harbor a large amount of complex structural features that result in a wide array of wildlife habitats. However, intensive forest management is gradually converting old-growth forest into younger, even-aged stands, reducing structural complexity and threatening the persistence of old-growth-dependent species. Maintaining elements of complex stand structure is critical to the conservation of old-growth forest specialists that use different habitat components at different periods of their annual cycle, and it requires a comprehensive understanding of seasonal variation in the habitat needs of these species. However, difficulties in observing free-ranging animals have sometimes limited our ability to assess such variations in habitat requirements, especially for small, elusive species. To address this, we used GPS telemetry collars to describe fine-scale habitat selection patterns of 6 male American Martens (Martes americana) during 2 contrasting periods of the year (snow-free, from mid-April to mid-November; snow-covered, from mid-November to mid-April), an objective formerly hard to achieve using conventional VHF telemetry. We used resource selection functions conducted at the fourth order of selection to compare habitat characteristics found at the sites used by martens (GPS locations, n = 100) to those found on an equal number of available sites (random points, n = 100) within each individual seasonal home range. We conducted vegetation surveys on these 200 sites to describe habitat and built candidate models representing different concurrent hypotheses. Our results showed that proxies of prey availability, predator avoidance, and thermal constraints were the primary factors influencing marten habitat selection during both periods, although their respective importance differed between periods. Martens selected sites with a high density of large-diameter snags (≥30·ha-1), high conifer canopy closure (≥53%), and a dense lateral cover (≥81%) during the snow-free period, but selected sites with a high volume of coarse woody debris (≥64 m3·ha-1) and high conifer canopy closure (≥48%) during the snow-covered period. Our results highlight the importance of contrasting seasonal changes in habitat selection patterns of small carnivores and may help maintain structural attributes in the landscape that are suitable for male American Martens.

СТРАТЕГИЧЕСКИЕ ПОДХОДЫ К ОБЕСПЕЧЕНИЮ ПРОДОВОЛЬСТВЕННОЙ БЕЗОПАСНОСТИ В УСЛОВИЯХ НЕОПРЕДЕЛЕННОСТИ

The current global situation is characterized by a high degree of uncertainty and risk. Epidemics, natural disasters, economic crises and other factors can dramatically affect food security. Therefore, developing strategies that can adapt to changing conditions becomes an important task. The paper examines the issue of ensuring food security in conditions of uncertainty. The concept of food security strategy and the importance of food market infrastructure in achieving this goal are considered. There are two key approaches to ensuring food security: traditional and flexible. The characteristics and features of each of these strategies are analyzed in detail. Traditional strategy focuses on stability and predictability, while flexible strategy aims to adapt to changing conditions. Particular attention is paid to the proposed algorithm for selecting a food security strategy, which takes into account a number of factors, including the level of risk, degree of uncertainty, product availability and budget constraints. This algorithm provides a scientifically proven method for selecting the optimal strategy, taking into account the context and specifics of the situation. Research is that both approaches, traditional and flexible, have their own unique advantages and can be applied in different contexts depending on the level of uncertainty and degree of risk. The choice between them depends on the specific needs and characteristics of the country or region, allowing for effective management of food security in a changing environment. The author proposes a comprehensive approach to ensuring food security, including the development of strategies, methods and selection algorithms, which makes it an important contribution to the field of food security and risk management.

The Impact of the Stimulus Packages on the Economy during COVID-19 in Bangladesh: A Mixed-Method Approach

With the unexpected onset of COVID-19, governments across the world responded with a range of preventive measures, including the imposition of lockdowns. To mitigate the adverse effects of lockdowns arising from supply chain shocks and employment loss, governments worldwide chose to implement policies to stimulate their economies and keep them working. This study assesses the impact and effectiveness of four of these packages in Bangladesh, employing a mixed-method approach. These packages include “salary support for workers in export-oriented RMG industries”, “working capital loans for affected industries and service sectors”, “working capital loans for cottage, micro, small, and medium enterprises”, and initiatives for “revitalizing the rural economy and job creation”. Each package was examined individually because of their differences in beneficiary groups, implementation methods, and individual objectives. Quantitative analysis involved propensity score matching (PSM), the difference in difference model (DID), and structural equation modelling (SEM). Stakeholders, including policy implementers, Bangladesh Bank officials, policy analysts, academics, workers, and beneficiaries, contributed to the qualitative analysis through extensive key-informant interviews, providing a comprehensive assessment of intervention outcomes. Ultimately, the results show that the packages achieved their socio-economic relief objectives for beneficiaries. The research examined both positive impacts and challenges in their implementation. It suggests that all four packages successfully achieved their goals, such as providing social and economic support, sustaining livelihoods, addressing marginalized groups’ needs, ensuring survival for large industries and small businesses, and promoting employment. In order to better address future shocks, establishing a beneficiary database integrated with the national system is recommended for smoother policy rollout. Despite acknowledged limitations, including challenges in beneficiary identification, data availability, and time constraints, the study’s unbiased estimations provide valuable insights to guide future policy directions in similar situations.

Quality of Preanesthesia Teleconsultation: The TELECAM Randomized Controlled Trial.

Introduction: Preanesthesia teleconsultation helps reduce availability constraints as well as direct and indirect expenses. The TELECAM trial was performed to assess the quality of preanesthesia teleconsultation in terms of clinical parameters evaluation, feasibility, patient satisfaction and preoperative anxiety, and anesthesiologist satisfaction. Methods: TELECAM was an investigator-initiated, prospective, single-center, randomized, controlled, parallel group, evaluator-blinded, open-label study. Patients with a scheduled ambulatory surgery (orthopedic or hand surgery) were randomized into the in-person preanesthesia consultation group or the preanesthesia teleconsultation (conducted at the patient's home or workplace) group. The quality of the teleconsultation was evaluated through agreement on intubation difficulty, predictable mask ventilation difficulty, and American Society of Anesthesiologists (ASA) scores between the preanesthesia consultation and the preanesthesia in-person visit. Results: A total of 241 patients were included, and 208 were considered in the analyses. The feasibility of teleconsultation was high, with a feasibility ratio of 87.5%. The quality of the preanesthesia consultation regarding the evaluation of predictable intubation, mask ventilation difficulties, and ASA score, did not differ between the two groups (p = 0.23, 0.29, and 0.06, respectively). The preoperative satisfaction was higher for patients who had a preanesthesia teleconsultation (p = 0.04). Patients' preoperative anxiety did not differ between the two groups (p = 0.90). The median satisfaction of the anesthesiologists who performed the teleconsultation reached a maximum of 10 (IQR: 8.0; 10.0). Conclusion: This study showed positive results for the quality of preanesthesia teleconsultation on the evaluation of clinical parameters, with high feasibility and satisfaction of the patients and anesthesiologists. The trial was registered in ClinicalTrials (NCT03470896).

Availability Aware Online Virtual Network Function Backup in Edge Environments

With the rapid advancement of edge computing and network function virtualization, it is promising to provide flexible and low-latency network services at the edge. However, due to the vulnerability of edge services and the volatility of edge computing system states, i.e., service request rates, failure rates, and resource prices, it is challenging to minimize the online service cost while providing the availability guarantee. This paper considers the problem of online virtual network function backup under availability constraints (OVBAC) for cost minimization in edge environments. We formulate the problem based on the characteristics of the volatility system states derived from real-world data and show the hardness of the formulated problem. We use an online backup deployment scheme named Drift-Plus-Penalty (DPP) with provable near-optimal performance for the AVBAC problem. In particular, DPP needs to solve an integer programming problem at the beginning of each time slot. We propose a dynamic programming-based algorithm that can optimally solve the problem in pseudo-polynomial time. Extensive real-world data-driven simulations demonstrate that DPP significantly outperforms popular baselines used in practice.

End-to-end automated speech recognition using a character based small scale transformer architecture

This study explores the feasibility of constructing a small-scale speech recognition system capable of competing with larger, modern automated speech recognition (ASR) systems in both performance and word error rate (WER). Our central hypothesis posits that a compact transformer-based ASR model can yield comparable results, specifically in terms of WER, to traditional ASR models while challenging contemporary ASR systems that boast significantly larger computational sizes. The aim is to extend ASR capabilities to under-resourced languages with limited corpora, catering to scenarios where practitioners face constraints in both data availability and computational resources. The model, comprising a compact convolutional neural network (CNN) and transformer architecture with 2.214 million parameters, challenges the conventional wisdom that large-scale transformer-based ASR systems are essential for achieving high accuracy. In comparison, contemporary ASR systems often deploy over 300 million parameters. Trained on a modest dataset of approximately 3000 h – significantly less than the 50,000 h used in larger systems – the proposed model leverages the Common Voice and LibriSpeech datasets. Evaluation on the LibriSpeech test-clean and test-other datasets produced character error rates (CERs) of 6.40% and 16.73% and WERs of 16.03% and 35.51% respectively. Comparisons with existing architectures showcase the efficiency of our model. A gated recurrent unit (GRU) architecture, albeit achieving lower error rates, incurred a computational cost 24 times larger than our proposed model. Large-scale transformer architectures, while achieving marginally lower WERs (2%–4% on LibriSpeech test-clean), require 200 times more parameters and 53,000 additional hours of training data. Modern large language models are used to improve the WERs, but require large computational resources. To further enhance performance, a small 4-g language model was integrated into our end-to-end ASR model, resulting in improved WERs. The overarching goal of this work is to provide a practical solution for practitioners dealing with limited datasets and computational resources, particularly in the context of under-resourced languages.

Combined modelling of production of merchantable phosphoric ores using parallel processors and their transportation by pipeline

This paper focuses on managing the production and transfer of phosphoric merchantable ores (MOs) in a pull-flow mode of a supply chain that is currently addressed with limited effectiveness. MOs are obtained by washing extracted source ores in parallel lines, using alternative routings that change their weight and composition, before blending in tanks to achieve the required quality defined by a set of compositional constraints. The pipe transfer program defines the batches to be transported and their schedule, considering downstream demand (a roughly stable demand from the chemistry and irregular export demands (export orders of large quantities of MOs)), preventive maintenance of pipe or washing lines and prevention of defusing and saturation in the tanks of the delivery pipe station. A daily used Decision Support System (DSS) encapsulates two models used sequentially and sharing technical, availability, capacity, and quality constraints; then, the optimal solution of the transfer model is adapted to create the order book processed by the original blending model to obtain an optimal joint production and pipe transfer program. It also provides ways to improve SC performance while considering its impact on the chemical production performance.

Gender and kidney transplantation.

Kidney transplantation provides the best form of kidney replacement therapy with improvement in quality of life and longevity. However, disparity exists in its availability, utilisation and outcomes, not only due to donor availability or financial constraints but also arising from the influence of biological sex and its sociocultural attribute i.e., Gender. Women make up the majority of kidney donors but are less likely to be counselled regarding transpantation, be waitlisted or receive living/deceased donor kidney. Biological differences also contribute to differences in kidney transplantation among the sexes. Women are more likely to be sensitised owing to pregnancy, especially in multiparous individuals, complicating donor compatibility. A heightened immune system in women, evidenced by more autoimmune illnesses, increases the risk of allograft rejection and loss. Differences in the pharmacokinetics of transplant drugs owing to biological variances could also contribute to variability in outcomes. Transgender medicine is also increasingly becoming a relevant topic of study, providing greater challenges in the form of hormonal manipulations and anatomic changes. It is thus important to determine and study transplantation and its nuances in this backdrop to be able to provide relevant sex and gender-specific interventions and design better practices for optimum kidney transplant utilisation and outcomes.

A multiobjective approach for weekly Green Home Health Care routing and scheduling problem with care continuity and synchronized services

Home Health Care (HHC) services are essential for delivering healthcare programs to patients in their homes, with the goal of reducing hospitalization rates and improving patients’ quality of life. However, HHC organizations face significant challenges in scheduling and routing caregivers for home care visits due to complex criteria and constraints. This paper addresses these challenges by considering both caregiver assignments and transportation logistics. The objective is to minimize the total travel distance and CO2 emissions while ensuring a balanced workload for caregivers, meeting patients’ preferences, synchronization, precedence, and availability constraints. To tackle this problem, we propose a multiperiodic Green Home Health Care (GHHC) framework. In the first stage, we utilize multiobjective programming and the NSGA-II algorithm to generate Pareto front solutions that consider travel distance and CO2 emissions. In the second stage, a Mixed-Integer Linear Programming (MILP) model is proposed to balance caregivers’ workload by assigning them to the patient routes generated in the first stage. The results highlight the trade-off between shorter routes and lower emissions. Furthermore, we examine the impact of prioritizing continuity of care and patient satisfaction. This research provides valuable insights into addressing the scheduling and routing challenges in HHC services, contributing to a more efficient and environmentally friendly healthcare delivery.

The role and impact of health economics in the optimization of patient care in osteoarthritis: insights from a practical example.

Osteoarthritis (OA) is a degenerative joint disease with a substantial global burden, causing chronic pain and reduced quality of life. Managing OA efficiently while maximizing healthcare resources is crucial. Health economics and health technology assessment (HTA) are central tools providing a framework to evaluate the clinical, economic, and ethical aspects of healthcare technologies and interventions. This article presents some insights into the role of health economics and the HTA process in OA management. It also illustrates an example of cost-effectiveness analysis in a specific healthcare context, on the basis of a recent clinical trial involving hyaluronic acid treatment for knee OA. While HTA offers valuable insights, it faces challenges like data availability and resource constraints. Integrating health economics into decision-making can enhance patient care and allocate resources effectively in OA and other healthcare domains.

Enhancing Hierarchical Transformers for Whole Brain Segmentation with Intracranial Measurements Integration.

Whole brain segmentation with magnetic resonance imaging (MRI) enables the non-invasive measurement of brain regions, including total intracranial volume (TICV) and posterior fossa volume (PFV). Enhancing the existing whole brain segmentation methodology to incorporate intracranial measurements offers a heightened level of comprehensiveness in the analysis of brain structures. Despite its potential, the task of generalizing deep learning techniques for intracranial measurements faces data availability constraints due to limited manually annotated atlases encompassing whole brain and TICV/PFV labels. In this paper, we enhancing the hierarchical transformer UNesT for whole brain segmentation to achieve segmenting whole brain with 133 classes and TICV/PFV simultaneously. To address the problem of data scarcity, the model is first pretrained on 4859 T1-weighted (T1w) 3D volumes sourced from 8 different sites. These volumes are processed through a multi-atlas segmentation pipeline for label generation, while TICV/PFV labels are unavailable. Subsequently, the model is finetuned with 45 T1w 3D volumes from Open Access Series Imaging Studies (OASIS) where both 133 whole brain classes and TICV/PFV labels are available. We evaluate our method with Dice similarity coefficients(DSC). We show that our model is able to conduct precise TICV/PFV estimation while maintaining the 132 brain regions performance at a comparable level. Code and trained model are available at: https://github.com/MASILab/UNesT/wholebrainSeg.

Construction and application case study of a “Primary Care-focused” county health system development index

Background“Focusing on primary care” is the primary principle guiding health system in China. However, there is a lack of effective measurement tools in practice. ObjectiveThe study sought to construct an index of "primary care-focused" county health system development index and perform empirical case analysis with two counties. MethodsThe study was conducted from 2021 to 2022. The index (including its sub-indicators, scoring methods and weights) was constructed based on a literature review and experts’ consensus (via a Delphi technique). Then, it was applied in 2 counties in Guangxi Zhuang Autonomous Region, based on data collected from a range of sources from 2020 to 2022, including surveys for community residents, patients and health professionals survey, institutional survey, medical insurance claims data, as well as data of key informant interviews. ResultsThe results of Delphi method showed that the authority coefficient of experts was 0.91, the coefficients of variation of two rounds of consultation were 0.14 and 0.13, respectively. The constructed index system contains 3 domains, 15 dimensions, and 36 indicators, with a comprehensive index calculation method. The results showed that the overall scores for the two counties in Guangxi Zhuang Autonomous Region were 58.62 and 52.57, respectively. The scores of both counties were below the current national benchmark (60.00 points). ConclusionWithin the constraint of data availability, the index system constructed in this study could be applied to monitor and evaluate the extent that county health systems have achieved “primary care focus”. Its application may improve visibility and priority of primary care development, facilitate the development and implementation policies to strengthen primary care.