Cost Constraints Research Articles

The American Headache Society First Contact-Headache in Primary Care program: Current metrics, knowledge assessments, and direction for future initiatives.

This study examines the American Headache Society First Contact-Headache in Primary Care program metrics to date in order to assess the program's reach and provide direction for future initiatives. Approximately 4 million primary care office visits annually are headache-specific encounters. Therefore, it is important that primary care providers are knowledgeable about headache management. Recognizing the need, the American Headache Society First Contact designed the comprehensive First Contact-Headache in Primary Care program with input from an advisory board comprised of a diverse group of physicians and advanced practice providers with backgrounds in family and internal medicine, pediatrics, obstetrics and gynecology, and neurology. This is the first study to assess the reach of the program and critically examine how to best meet the needs of clinicians and patients going forward. We report descriptive statistics for the First Contact website metrics from October 2020 to June 2023 and grand rounds program data from May 2020 to December 2023. We also conducted a cross-sectional analysis of survey data from presentations conducted at two large national family medicine symposia, as well as a thematic analysis of the question: "Please indicate what areas of your practice could be enhanced or improved with additional education?" The First Contact program homepage was the second most visited page on the American Headache Society website (>100,000 views). A total of 20 podcast episodes were created for the program (>3500 plays). The First Contact program held 99 events (72 institutional grand rounds, 22 State-level meetings, and five national meetings), reaching >7000 clinicians. The institutional grand rounds and state-level meetings were held across 27 States and Washington D.C. Only 31.9% (30/94) of First Contact program events (excluding national meetings) occurred in the West census region, which has the fewest headache subspecialists and lowest headache subspecialist density in the United States. When examining survey data of participants who attended the two virtual national family medicine symposia (39.3% response rate, N = 636/1620), 85.7% (544/635) reported being "completely confident" or "veryconfident" in their ability to recognize and accurately diagnose patients presenting with a primary complaint of headache and 81.5% (517/634) reported being "completely confident" or "very confident" in their ability to develop evidence-based treatment plans that are tailored to the needs of individual patients. The use of diagnostic tools to recognize patients with migraine (60.4%, 384/636) and translating standards of care to the practice setting (42.5%, 270/636) were the most reported intended changes by participants. Most participants reported that program content was of clinical relevance and would improve their patients' outcomes (90.5% [571/631] and 90.6% [572/631], respectively). Over three-quarters (77.8%, 495/636) of participants reported areas of their practice that can be improved by additional education specifically regarding workflow, diagnosis, and management. This study evaluates one of the first national initiatives for primary care education. Data from the two First Contact Family Medicine national symposia indicate the program is generally well received with most participants reporting improved confidence and intention to implement key changes in practice to improve care for patients with headache; however, there remain areas of exploration for education that could further enhance participant experience and expand the reach of the initiatives. Areas for future programming include continued education on multifactorial approaches to headache treatment and suggestions for addressing cost, insurance, and time constraints. Also, future work may examine where the First Contact program might focus initiatives based on specific areas of need in headache care, such as geographic "desert" areas, racial and ethnic disparities, and uninsured/underinsured populations.

Robust Workforce Management with Crowdsourced Delivery

Revolutionize Workforce Management of Crowdsourced Delivery Platforms The surge in online shopping is driving e-retailers to revamp their logistics for efficient, cost-effective deliveries. Many are embracing crowdsourced delivery, where independent couriers use personal vehicles for swift shipments. Major players like Amazon and Walmart have pioneered this shift in delivery methods. To tackle uncertainties in this model, platforms are blending ad hoc couriers with prehired couriers. This hybrid approach ensures reliability in customer service while managing costs effectively. However, balancing this mix poses challenges, as future demands are still being determined. This study proposes a robust satisficing framework to optimize workforce management in delivery platforms. This innovative method aims to enhance cost-effectiveness and service quality by addressing uncertainties in ad hoc couriers’ availability and behavior. It offers a strategic tool for platforms to navigate workforce resources efficiently amidst fluctuating demands and cost constraints.

Energy-efficient time and cost constraint scheduling algorithm using improved multi-objective differential evolution in fog computing

The recent surge in Internet of Things (IoT) applications and smart devices has led to a substantial rise in the data generation. One of the major issues involved is to meet strict quality of service (QoS) requirements for computing these applications in terms of execution time, cost and in an energy-efficient manner. To extract useful information, fast processing and analysis of data is needed. Consequently, moving all the data to centralized cloud data centers would lead to high processing times, increased cost and energy consumption and more bandwidth usage; thus, processing of applications with strict latency requirements becomes challenging. The addition of fog layer between cloud and IoT devices has provided promising solutions to such issues. However, efficient employment of computing resources in the hybrid infrastructure of fog and cloud nodes is of great significance and demands an optimal scheduling strategy. Toward this direction, a novel Pareto-based algorithm in fog computing, namely energy-efficient time and cost (ETC) constraint scheduling algorithm, is introduced in this paper for scheduling workflow applications. ETC attempts to optimize monetary cost along with time and energy objectives. Improved multi-objective differential evolution (I-MODE) meta-heuristic is introduced and incorporated with deadline-aware stepwise frequency scaling approach that is based on our previously proposed energy makespan multi-objective optimization (EM-MOO) algorithm. Synthetic and real-world application workflows are used to conduct evaluation of the proposed work with existing well-known algorithms from the literature. The experimental results for synthetic workflows reveal that the proposed algorithm lessens energy utilization by 14–21%, execution time by almost 25% and cost consumption by 22–27%, while for real-world application workflows, energy consumption is reduced by 12–24%, execution time by 14–16% and cost consumption by 23–29%.

Zebrafish Experimental Animal Models for AD: A Comprehensive Review.

AD disease (AD) is a multifaceted and intricate neurodegenerative disorder characterized by intracellular neurofibrillary tangle (NFT) formation and the excessive production and deposition of Aβ senile plaques. While transgenic AD models have been found instrumental in unravelling AD pathogenesis, they involve cost and time constraints during the preclinical phase. Zebrafish, owing to their simplicity, well-defined behavioural patterns, and relevance to neurodegenerative research, have emerged as a promising complementary model. Zebrafish possess glutaminergic and cholinergic pathways implicated in learning and memory, actively contributing to our understanding of neural transmission processes. This review sheds light on the molecular mechanisms by which various neurotoxic agents, including okadaic acid (OKA), cigarette smoke extract, metals, and transgenic zebrafish models with genetic similarities to AD patients, induce cognitive impairments and neuronal degeneration in mammalian systems. These insights may facilitate the identification of effective neurotoxic agents for replicating AD pathogenesis in the zebrafish brain. In this comprehensive review, the pivotal role of zebrafish models in advancing our comprehension of AD is emphasized. These models hold immense potential for shaping future research directions and clinical interventions, ultimately contributing to the development of novel AD therapies.

Development of a Machine-learning-based Framework to Assess the Performance of Water Distribution Systems

Recent statistics indicate that the national water supply coverage is approximately 99%, reflecting a high level of service. However, the aging infrastructure continues to cause issues such as pipe failures and water quality concerns, creating operational difficulties. To solve these issues, performance assessments are used to quantitatively evaluate the system conditions and ensure effective maintenance. In Korea, these assessments are based on a scoring system that combines direct and indirect assessments. However, direct assessment of all pipes is limited by cost and time constraints. To address this issue, a deep neural network is used to assess the aging of water distribution systems. This study develops a framework to estimate direct results from indirect data by analyzing the correlation between indirect and direct assessments. Additionally, data augmentation is considered to compensate for the lack of training data in direct assessments, improve the reliability of performance assessments, and support better prioritization in system maintenance.

Optimizing Road Pavement Projects Through Earned Value Concepts

Every construction project faces constraints of cost, schedule, and quality that must be managed to achieve the desired objectives. This research aims to control the aspects of time and cost in construction projects by applying the Earned Value Concept and Crash Program methods. The subject of the study is the Preservation of Roads and Bridges project in Bireuen City - BTS, Lhokseumawe/Aceh Utara, Krueng Mane Buket Rata, SP. Krueng Geukeueh - PEL. Krueng Geukeueh (PEL. Lhokseumawe). The data analyzed includes the Budget Plan (RAB), project schedule, and reports on planned and actual work. This data is processed into three main indicators: BCWS, BCWP, and ACWP, which are then analyzed through Schedule Variance (SV), Cost Variance (CV), Schedule Performance Index (SPI), and Cost Performance Index (CPI). The analysis results show that during weeks 22 to 23, the project had a positive Schedule Variance (SV) (0), which then turned negative during weeks 24 to 28, with an average Schedule Performance Index (SPI) of 1.0096, indicating that project execution was ahead of schedule. Meanwhile, the Cost Variance (CV) was positive throughout the project, and the average Cost Performance Index (CPI) was 1.2118, indicating that actual costs did not exceed the budget. The application of the Crash Program method resulted in changes in heavy equipment rental costs and worker wages, from IDR 1,440,469,946.99 over 50 days to IDR 1,853,072,988.06 over 25 days, with a cost increase of IDR 412,603,041.07 and an acceleration ratio of 1.29. This research demonstrates that the Earned Value Concept and Crash Program methods are effective in controlling time and cost in construction projects, thereby helping to achieve better efficiency in project management.

Robust Control of Electrical Machines in Renewable Energy Systems: Challenges and Solutions

The increasing global demand for energy driven by population growth necessitates a shift towards renewable energy sources to address sustainability and environmental concerns. Renewable energy sources and how they function in tandem with electrical equipment to generate power are covered extensively in this study. Renewable sources like solar-thermal, hydro, wind, wave, tidal, geothermal, and biomass-thermal utilize electric machines to convert various forms of stored energy into electricity. The paper classifies these machines based on their design and application in renewable systems, highlighting the most common types, including DC, induction, and synchronous machines. It also evaluates their performance according to efficiency, power density, and cost-effectiveness. A primary focus is on the challenges associated with controlling electrical machines in renewable energy systems, including issues related to intermittency, grid integration, nonlinear dynamics, fault tolerance, harmonics, efficiency optimization, thermal management, scalability, real-time control, and cost constraints. The paper concludes by discussing the need for advanced control strategies and solutions to address these challenges, aiming to enhance a reliability, efficiency, and performance of renewable energy systems.

Improving Workplace Well-being in Modern Organizations: A Review of Large Language Model-based Mental Health Chatbots

The global rise in mental disorders, particularly in workplaces, necessitated innovative and scalable solutions for delivering therapy. Large Language Model (LLM)-based mental health chatbots have rapidly emerged as a promising tool for overcoming the time, cost, and accessibility constraints often associated with traditional mental health therapy. However, LLM-based mental health chatbots are in their nascency, with significant opportunities to enhance their capabilities to operate within organizational contexts. To this end, this research seeks to examine the role and development of LLMs in mental health chatbots over the past half-decade. Through our review, we identified over 50 mental health-related chatbots, including 22 LLM-based models targeting general mental health, depression, anxiety, stress, and suicide ideation. These chatbots are primarily used for emotional support and guidance but often lack capabilities specifically designed for workplace mental health, where such issues are increasingly prevalent. The review covers their development, applications, evaluation, ethical concerns, integration with traditional services, LLM-as-a-service, and various other business implications in organizational settings. We provide a research illustration of how LLM-based approaches could overcome the identified limitations and also offer a system that could help facilitate systematic evaluation of LLM-based mental health chatbots. We offer suggestions for future research tailored to workplace mental health needs.

Demand management and pricing in renewable integrated plug-in electric vehicle charging station using reinforcement learning

ABSTRACT The increasing adaption of Plug-in Electric Vehicles (PEVs) has increased the power demand for charging. Renewable generation can significantly help to meet this demand. The uncertainty of renewable generation and randomness in PEV charging makes the demand management of charging stations challenging. To address these challenges, this study proposes a novel demand management pricing strategy for renewable integrated charging stations. The proposed strategy uses reinforcement learning for charging coordination of PEVs. The renewable generation is estimated using weather data and accordingly PEVs are scheduled for demand management. The strategy decides the PEV power price based on real-time price, renewable tariff and PEV load. The strategy uses the bidirectional power flow with battery degradation cost. The distribution transformer operating cost and loading constraints are included to resemble the real-time environment. The PEV randomness and uncertainty of renewable generations are incorporated in the study. The results of the numerical case study show that the proposed strategy can manage the charging station load efficiently. The proposed strategy has optimized the cost of charging, discharging and charging station profit and increased the service capability of the charging station.

Modified ICP based Framework for 3D Localization and Mapping in GNSS-denied Environments using LiDAR

Abstract. Lidar odometry and mapping has emerged as fundamental technology in various fields, including accurate positioning and mapping, autonomous vehicles, robotics, and environmental monitoring. The ability to create accurate maps and calculate trajectories is essential for localization and navigation in complex environments, particularly in scenarios where GNSS signal are unavailable. In this context, we propose a novel method for simultaneous localization and mapping (SLAM) specifically designed for GNSS-denied environments and without the need for high-accuracy ranging or inertial measurement units (IMUs). Our approach aims to address the challenges posed by GNSS-denied environments and the cost constraints associated with using multiple sensors. By using only lidar sensors, specifically the Velodyne VLP 16 sensor, we offer a cost-effective solution. Our method utilizes a modified version of the Iterative Closest Point (ICP) algorithm to handle consecutive lidar scans seamlessly. In addition to our method, we conducted thorough accuracy assessments on the created map. The reason behind extensive accuracy evaluation is to ensure the quality of registration. By comparing alignment between the features of different frames within the map and determining the error between the registered features and the original, we gain a more comprehensive understanding of registration quality.

Challenges in Diagnosis and Management of SLE in Africa: An Online Survey.

We surveyed African physicians about challenges in diagnosis and management of systemic lupus erythematosus (SLE). We used a cross-sectional, online questionnaire-based survey of African specialist physicians on availability of laboratory tests, medications, and specialized services for the diagnosis of SLE. Our 226 respondents from 31 countries were dermatologists (38%), rheumatologists (28%), and nephrologists (23%), the majority practicing at university/state-funded hospitals (80.8%), but over half of patients (59.6%) were self-funded for laboratory tests and medications. Antinuclear antibody (ANA), antiphospholipid antibody, and complement tests were available to 79.4%, 67.6%, and 62.3% of respondents, respectively, but fewer in the East and West African regions. Median turnaround time for the ANA test was within two weeks but more than four weeks for 5.6% of respondents, and longer in West Africa compared with other regions (P = 0.0002). Availability of urine protein-to-creatinine test, skin and renal histopathology was 82%, 82.5%, and 76.2%, respectively. Median turnaround times were within one to two weeks, but more than four weeks for 13.8% of respondents for skin histology results and usually within four weeks but more than four weeks for 24.5% of respondents for renal histology. Glucocorticoids and antimalarials were readily available across all regions, with variable availability of immunosuppressants from 93.7% for methotrexate to 65% for calcineurin inhibitors and only 58.4% for the biologic rituximab. Intensive care units/high care facilities, hemodialysis, and renal transplantation were available to 69.8%, 91.9%, and 56.5% of respondents, respectively. Variable availability of laboratory tests, medications, and supportive services coupled with cost constraints are major impediments to early diagnosis and optimal management of SLE in most of Africa and are likely factors contributing to underreporting and poor prognosis of SLE in Africa.

A review of neural networks in handwritten character recognition

Handwritten character recognition has been a significant research focus in the fields of pattern recognition and artificial intelligence over the past decade. With the advent of neural networks, particularly deep learning models, the accuracy and efficiency of offline handwritten character recognition have dramatically improved. This paper presents a comprehensive review of recent developments in applying neural networks to handwritten character recognition. The literature review covers studies conducted between 2016 and 2023, providing insights into the methodologies, data processing techniques, and evaluation metrics used. The review spans various neural network architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid models. It categorizes and compares their performance across multiple benchmark datasets, highlighting specific improvements in recognition accuracy and efficiency. Furthermore, the review discusses the challenges faced in large-scale datasets, such as the diversity of handwriting styles and computational cost constraints. Notably, CNNs have shown outstanding performance, but the integration of advanced techniques like transfer learning and Generative Adversarial Networks (GANs) is explored as a potential avenue to enhance future recognition systems.

Predicting the Transition From Depression to Suicidal Ideation Using Facebook Data Among Indian-Bangladeshi Individuals: Protocol for a Cohort Study.

Suicide stands as a global public health concern with a pronounced impact, especially in low- and middle-income countries, where it remains largely unnoticed as a significant health concern, leading to delays in diagnosis and intervention. South Asia, in particular, has seen limited development in this area of research, and applying existing models from other regions is challenging due to cost constraints and the region's distinct linguistics and behavior. Social media analysis, notably on platforms such as Facebook (Meta Platforms Inc), offers the potential for detecting major depressive disorder and aiding individuals at risk of suicidal ideation. This study primarily focuses on India and Bangladesh, both South Asian countries. It aims to construct a predictive model for suicidal ideation by incorporating unique, unexplored features along with masked content from both public and private Facebook profiles. Moreover, the research aims to fill the existing research gap by addressing the distinct challenges posed by South Asia's unique behavioral patterns, socioeconomic conditions, and linguistic nuances. Ultimately, this research strives to enhance suicide prevention efforts in the region by offering a cost-effective solution. This quantitative research study will gather data through a web-based platform. Initially, participants will be asked a few demographic questions and to complete the 9-item Patient Health Questionnaire assessment. Eligible participants who provide consent will receive an email requesting them to upload a ZIP file of their Facebook data. The study will begin by determining whether Facebook is the primary application for the participants based on their active hours and Facebook use duration. Subsequently, the predictive model will incorporate a wide range of previously unexplored variables, including anonymous postings, and textual analysis features, such as captions, biographic information, group membership, preferred pages, interactions with advertisement content, and search history. The model will also analyze the use of emojis and the types of games participants engage with on Facebook. The study obtained approval from the scientific review committee on October 2, 2023, and subsequently received institutional review committee ethical clearance on December 8, 2023. Our system is anticipated to automatically detect posts related to depression by analyzing the text and use pattern of the individual with the best accuracy possible. Ultimately, our research aims to have practical utility in identifying individuals who may be at risk of depression or in need of mental health support. This initiative aims to enhance engagement in suicidal ideation medical care in South Asia to improve health outcomes. It is set to be the first study to consider predicting participants' primary social application use before analyzing their content to forecast behavior and mental states. The study holds the potential to revolutionize strategies and offer insights for scalable, accessible interventions while maintaining quality through comprehensive Facebook feature analysis. DERR1-10.2196/55511.

Contrastive domain-invariant generalization for remaining useful life prediction under diverse conditions and fault modes

As industrial equipment becomes increasingly complex, necessitating operation under varied conditions and often exhibiting diverse failure modes, traditional deep learning models built on data from the original environment become inapplicable. Moreover, in actual industrial scenarios, the generalization capability of Domain Adaptation and classic Domain Generalization methods is severely impacted when there is a lack of multiple source domain and target domain data, due to the cost or feasibility constraints associated with collecting extensive monitoring data. In this paper, a single domain Contrastive Domain-Invariant Generalization (CDIG) method for estimating the remaining useful life under different conditions and fault modes is proposed. This method first defines homologous signals as the foundational data. Subsequently, it learns domain-invariant features by encouraging two feature extraction processes to extract latent features of homologous signals as similarly as possible. Additionally, multiple condition-based attention, pooling, and a novel equalization loss function are utilized to regulate the generation of domain-invariant features. Ultimately, the RUL predictor is trained by source domain data, operational conditions, and temporal information to facilitate its applicability across diverse domains. Case studies demonstrate that CDIG achieves satisfactory predictive results under unseen conditions, highlighting the potential of the proposed method as an effective predictive tool.

Optimization of graded and partial metamaterial treatments for enhanced broadband sound insulation in partition panels

Resonant metamaterials can achieve unprecedented vibroacoustic attenuation in lightweight partition panels through small resonators attached on a subwavelength scale. Conventional metamaterial treatments are effective only in a narrow band and typically cover the entire panel surface, posing important challenges for industrialization due to manufacturing and cost constraints. To address these issues, in this study we propose graded and partial metamaterial treatments, which are selectively distributed where they are the most effective, avoiding unnecessary coverage, and include a spatially varying resonance frequency for broadband effectiveness. The related design challenges are tackled by developing a numerical methodology that simultaneously optimizes the distribution and the grading of the treatment. The optimization objective is to maximize the broadband diffuse sound transmission loss (STL) of the metamaterial panel, while constraining the maximum fraction of treated area. Leveraging effective medium modelling, the approach efficiently incorporates local changes in the effective mass density of the metamaterial panel to represent partial treatments. Different design cases are considered for different target bands and panel dimensions. Results demonstrate that graded partial metamaterial treatments can achieve broadband improvements with a limited treated area, effectively suppressing the STL dips due to the modes of the host panel and to coincidence effects.

Integrating targeted genetic markers to genotyping-by-sequencing for an ultimate genotyping tool.

New selection methods, using trait-specific markers (marker-assisted selection (MAS)) and/or genome-wide markers (genomic selection (GS)), are becoming increasingly widespread in breeding programs. This new era requires innovative and cost-efficient solutions for genotyping. Reduction in sequencing cost has enhanced the use of high-throughput low-cost genotyping methods such as genotyping-by-sequencing (GBS) for genome-wide single-nucleotide polymorphism (SNP) profiling in large breeding populations. However, the major weakness of GBS methodologies is their inability to genotype targeted markers. Conversely, targeted methods, such as amplicon sequencing (AmpSeq), often face cost constraints, hindering genome-wide genotyping across a large cohort. Although GBS and AmpSeq data can be generated from the same sample, an efficient method to achieve this is lacking. In this study, we present the Genome-wide & Targeted Amplicon (GTA) genotyping platform, an innovative way to integrate multiplex targeted amplicons into the GBS library preparation to provide an all-in-one cost-effective genotyping solution to breeders and research communities. Custom primers were designed to target 23 and 36 high-value markers associated with key agronomical traits in soybean and barley, respectively. The resulting multiplex amplicons were compatible with the GBS library preparation enabling both GBS and targeted genotyping data to be produced efficiently and cost-effectively. To facilitate data analysis, we have introduced Fast-GBS.v3, a user-friendly bioinformatic pipeline that generates comprehensive outputs from data obtained following sequencing of GTA libraries. This high-throughput low-cost approach will greatly facilitate the application of DNA markers as it provides required markers for both MAS and GS in a single assay.

Linear fractional transportation problem in bipolar fuzzy environment

This article proposes a solution methodology for the Linear Fractional Transportation Problem (LFTP) by incorporating bipolar fuzzy sets (BFSs) to accommodate both positive and negative judgmental perspectives. The approach explores Zimmermann's extension within bipolar fuzzy environment to compare outcomes. In this context, the cost function and constraint coefficients are depicted using interval-valued trapezoidal bipolar fuzzy numbers (IVTrBFNs) and defuzzified by (s, t)-cut. The initial approach employs the simplex method and fuzzy optimization method, renowned for its effectiveness in obtaining optimal solutions. In the alternative method, Bipolar fuzzy programming approach (BFPA) is utilized for better outcome. In this method the LFTP is altered to a Multi-Objective Transportation Problem (MOTP), and BFPA extends Zimmermann's technique under suitable positive and negative membership functions, converting MOTP to a one-objective transportation problem (TP) and solved using LINGO software. Supporting this proposed method some theorems are formulated to demonstrate that the most effective solution of the single-objective TP is a Pareto optimal solution for the corresponding MOTP. A quantitative example is provided for better understanding of the proposed BFPA method alongside two other approaches.

Navigating delayed diagnoses, self-neglect, and lost livelihoods: a qualitative exploration of fisherfolk’s health perspectives and management of non-communicable diseases in Kerala, India

IntroductionNon-Communicable Diseases (NCDs) place a significant burden on India's healthcare system, accounting for approximately 62% of all deaths in the country in 2017. The southern Indian state - Kerala - has some of the highest rates of NCDs. Within the state, the fisherfolk community has a high prevalence of risk factors such as tobacco use and alcohol consumption. Working in the fisheries industry demands physical exertion, night shifts alongside extended periods of time at sea (for seafaring fisherfolk). Evidence is scant on how these conditions relate to the health-seeking experiences of fisherfolk, particularly in the context of NCDs. We conducted a qualitative study in two districts of Kerala to fill this gap.MethodsIn-depth individual and small group interviews– as per participant preference – with male and female fishing community members living with NCDs were conducted between October 2022 and February 2023 in two districts of Kerala. Interviews were conducted to explore community members’ experiences with health-seeking for NCDs. Transliterated English transcripts were coded using ATLAS.ti software and analysed using thematic analysis with inductive generation of codes, with indexing against Levesque, Harris, and Russell’s 2013 access to healthcare framework.ResultsThirty-three interviews with 42 participants were conducted. We found that NCDs were usually diagnosed late- either when admitted/consulted for other illnesses or when the symptoms became unbearable. Health-seeking patterns differed between seafaring and inland fishing subgroups, who were sampled from two districts in the state. Seafaring fisherfolk preferred public facilities for regular checkups and medicines while in-land fisherfolk relied on private facilities, although it was considered expensive. Ability to seek care was impacted by the working hours of the health centre which did not suit their working hours. Health constaints and related expenses also impacted their financial status and occupation, with some opting for less strenuous jobs.ConclusionThis study highlights the NCD-related health-seeking experiences of the fisherfolk community in Kerala, India. Fisherfolk reported self neglect, delayed diagnosis, cost and livelihood constraints owing to the onset of NCDs, even as dual practice and medicine access in the public sector were appreciated. Overall, larger studies and policymaking processes should consider in depth the experiences faced by particular economic groups like fisherfolk, who may face unique health and care-seeking challenges.

Ex Ovo Chorioallantoic Membrane Assay as a Model of Bone Formation by Biomaterials.

Biomaterials play an increasingly critical role in bone tissue engineering. However, achieving effective clinical translation requires a careful choice of biomimetic materials and thorough assessment of their efficacy and safety. Existing in vitro and in vivo models have drawbacks including time and cost constraints, invasive procedures, and discordance between animal models and clinical outcomes. Therefore, there is a demand for an alternative model. We hypothesized that the chick embryo chorioallantoic membrane can serve as a bioreactor to evaluate the initial sign of bone formation on scaffolds. In parallel, we investigated the osteogenic potential of a previously fabricated fibrin-alginate-calcium phosphate biomaterial (FACaP). Blood vessels were observed to infiltrate the scaffolds with early signs of bone formation, confirmed via RUNX-2 and alpha smooth muscle actin markers. The scaffolds' chemical composition was evaluated by Fourier-transform infrared spectroscopy, and ion chromatography was used to assess calcium ion release. Finally, the topography was examined by atomic force microscopy. In conclusion, this system offers simple refinement for in vivo models in bone tissue engineering and highlights the great potential of FACaP as an angiogenic and osteogenic biomaterial for non-load-bearing applications.

Estimation of within-gap regeneration height growth in managed temperate deciduous forests using bi-temporal airborne laser scanning data

Key messageMulti-temporal airborne laser scanning (ALS) data were used to estimate regeneration stem height growth within gaps in uneven-aged deciduous forests. The height and height growth measured in the field were used to calibrate and validate ALS estimates. This method provided highly precise estimates of height and unbiased height increment estimates of regeneration at stem level.ContextAssessing regeneration height growth is essential for evaluating forest dynamics and optimizing silvicultural operations. However, regeneration description at high spatiotemporal resolution has remained limited to restricted areas by the limiting cost constraints of field measurements. Highly precise airborne laser scanning (ALS) data are currently acquired over wide areas. Such datasets are promising for characterizing regeneration dynamics.AimsWe aimed to estimate height and height growth within regenerating areas at the stem level using multi-temporal ALS data.MethodsALS data were acquired from 56,150 ha of uneven-aged deciduous forest in Belgium in 2014 and 2021. Stem tops were detected using local maxima (LM) within regenerating areas in both ALS datasets and matched. Field data were collected in 2021 and used to calibrate the ALS-estimated heights using linear and non-linear models at stem level. Height growth estimation was then validated using field-measured increments.ResultsWithout height calibration, the 2021 ALS-estimated height had a − 1.06 m bias and 1.39 m root-mean-squared error (RMSE). Likewise, the 2014 ALS-estimated height had a − 0.58 m bias and 1.14 m RMSE. The non-linear calibration seemed more appropriate for small regeneration stems (height < 4 m). Using height calibration, the 2021 ALS-estimated height had a − 0.01 m bias and 0.84 m RMSE. In 2014, the bias and RMSE were 0.02 and 0.91 m, respectively. ALS-estimated height growth was unbiased and had an RMSE of 0.10 m·year−1.ConclusionsThis original method is based on the bi-temporal ALS datasets calibrated by limited field measurements. The proposed method is the first to provide unbiased regeneration height growth of regeneration stems in uneven-aged forests and new perspectives for studying and managing forest regeneration.