Monitoring Costs Research Articles

Spatial-temporal analysis of groundwater well features from neural network prediction of hexavalent chromium concentration

Groundwater monitoring is a crucial part of groundwater remediation that produces data from various strategically placed wells to maintain a water quality standard. Using the United States Department of Energy’s Hanford 100-HRD area well data, recurrent neural networks are trained in the form of one-dimensional Convolutional Neural Networks (CNNs), Long Short Term Memory (LSTM) networks, and Dual-stage Attention-based LSTM (DA-LSTM) networks to reduce monitoring costs and increase data sampling responsiveness that is subject to laboratory analysis delays, with the best network being DA-LSTM achieving an R2 score of 0.82. There is also the provision of novel well relation analysis through two method of explanation coming from SHapley Additive exPlanations (SHAP) and attention mechanisms within DA-LSTM. Extracting feature well importance from these two methods and using target wells as vehicles for pipeline demonstration, it is found that wells 199-D5-160, 199-D4-68, 199-D2-11, 199-D8-94, and 199-D2-10 have had the most year-long impact on Hexavalent Chromium (CrVI) concentration at the target during the testing year, while wells 199-D8-94, 199-D4-95, 199-D8-53, 199-D4-85, and 199-D4-99 are found to be strong candidates affecting target CrVI instantaneous change during a particular timeframe.

Analyzing the Reliability and Cost of the Most Commonly Used Dosimeters for Personal Ultraviolet Radiation Monitoring—A Rapid Review

To identify the most used dosimeters for monitoring ultraviolet radiation (UVR) and analyze their reliability and cost for individual UV exposure monitoring, this study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. An extensive search of the PubMed, Scopus, and Web of Science databases, covering 2005–2023, was conducted, including examining reference lists of retrieved studies. Of the 1202 records, 52 were eligible for analysis. Three types of dosimeters were identified: photosensitive, photochromic, and electronic dosimeters. Photosensitive dosimeters were utilized for 1236 samples across the studies, while photochromic dosimeters were employed for 360 samples. Electronic dosimeters, with a sample size of 3632, were the most extensively studied. This study highlights the variety of resources available for UVR assessment and the significance of specific dosimeter types in this field. Although few studies have explored the costs associated with dosimeter use, electronic dosimeters are the most cost-effective for radiation monitoring and provide the highest accuracy for measuring UVR exposure. Electronic dosimeters, known for real-time data and high precision, are reliable but costly, being approximately 16.5 times more expensive than photosensitive dosimeters and 160 times more expensive than photochromic dosimeters. Photosensitive dosimeters suit large-scale personal use, and photochromic sensors such as polysulphone dosimeters are also reliable. Additional costs for data analysis software, laboratory equipment, or external analysis services may be incurred, especially for advanced research-grade sensors.

Quantifying Patient Preferences and Expectations About Diabetic Retinopathy Monitoring

Diabetic retinopathy (DR) is the leading cause of blindness among adults in the US. The US Centers for Disease Control and Prevention recommends annual DR monitoring for all individuals with diabetes, yet monitoring rates remain below 70%. To evaluate how patient preferences and expectations about DR monitoring are associated with expected monitoring adherence behaviors. In this survey study, a web-enabled survey instrument was developed and implemented with a discrete-choice experiment to characterize patient preferences for outcomes of DR monitoring and graded-pair questions to quantify patients' expectations about the impact of DR monitoring on blindness risk. The survey was conducted through ResearchMatch, a US National Institutes of Health-developed online platform, among adults with self-reported, physician-diagnosed diabetes. Recruitment occurred between September 15, 2023, and October, 17, 2023, and data analysis occurred between October 2023 and December 2023. Results from the 2 tasks were combined to derive patients' expected monitoring behavior following a recently proposed treatment adherence framework. The survey instrument was pretested in cognitive interviews and validated for the purposes of this study. Survey-based discrete-choice experiment and graded-pair questions. Participants' relative preferences for DR-related blindness risk reductions, monitoring time, and out-of-pocket monitoring costs were quantified, as well as the degree to which participants expected adherence to monitoring to affect the risk of blindness. By combining how much participants valued specific reductions in blindness risk (relative to monitoring costs) and their expected risk reduction through monitoring, the rate at which patients would maximize the benefit of monitoring appointments was assessed. The survey was completed satisfactorily by 304 respondents of 542 individuals invited to participate. Mean (SD) respondent age was 40.5 (11.2) years, and 169 respondents (56.1%) were female. Reductions in blindness risk were valuable to participants. Participants required a 3.87 (95% CI, 1.91-5.88) percentage-point reduction in 5-year blindness risk to be fully adherent to an annual 53-minute monitoring visit with a $26 co-payment, but respondents expected DR monitoring to reduce the 5-year blindness risk by 0.71 (95% CI, 0.21-1.28) percentage points. In this online survey study among adults with diabetes, measurement of patient preferences and expectations about DR monitoring with properly validated instruments offered an opportunity to assess patient health behaviors. The association between preferences and monitoring expectations was generally consistent with monitoring nonadherence among adults with diabetes and offers insights that may help address inconsistent DR monitoring.

ANALISIS PENERAPAN AKUNTANSI LINGKUNGAN (GREEN ACCOUNTING) MENURUT PERSPEKTIF ISLAM DALAM PENGELOLAAN LIMBAH PADA PT. SUMBER GRAHA SEJAHTERA

The results of the study show the application of environmental accounting (Green accounting) according to the Islamic perspective of waste management of PT. Sources of Graha Sejahtera (SGS) include Avoiding Air Pollution, Good Waste Management and Green Spaces. PT incurs costs for environmental activities such as waste management, environmental monitoring costs, employee training costs, and environmental activity audit costs. All environmental costs, and allocating these costs based on cost types in a structured environmental accounting contribute well to environmental performance. The impact of the application of environmental accounting in waste management at PT. Sumber Graha Sejahtera (SGS) produces goodness and cleanliness because when a company carries out activities support environmental management, the management must wisely record every cost incurred related to environmental activity. These costs are referred to as environmental costs that must be charged to functions within the company appropriately, for example, production functions, marketing functions and other functions.

A memristor‐sourced key for encrypted power and data synchronous transmission in switched mode power supplies

AbstractIn power and data synchronous transmission (PDST), it is crucial to modulate additional signals onto the voltage bus of the converters. This reduces the cost of monitoring and control of the power grid. There is thus the need to ensure that the data transmitted is well secured. This study, designs an electronic system that uses the common coupling point for PDST. Then Chua's memristive circuit is introduced as an entropy source for generating a random bit sequence for encrypted PDST in a switched mode power supply. The encrypted data signal is modulated using phase shift key modulation. Thereafter, the carrier signal is pulse width modulation modulated to control the switching operation of the power switch. The use of Chua's memristive circuit for generating a random number is not new; however, there is little study directly adopting its use in PDST. This allows for resource sharing since multiple signal sources can share the same bus without interference or security risk. The generated random bits are tested for randomness using National Institute of Standards and Technology test suite and autocorrelation test. MATLAB/Simulink and experimental results both confirm the effectiveness of the proposed data encryption method.

Centralized Versus Decentralized R&D Within a Multiproduct Monopolistic Firm Under Research Spillovers

ABSTRACTThis study investigates the R&D organizational structure of a multi‐product monopolistic firm where the central office decides between centralized and decentralized R&D under research spillovers. We demonstrate that decentralized R&D becomes advantageous when the difference between monitoring and coordination costs is relatively high while products are sufficiently differentiated with lower spillovers. We also find that decentralized R&D can yield higher social welfare under moderate spillovers and product differentiation conditions. Our findings underscore the governmental need for a nuanced approach in designing R&D structures, considering the trade‐offs between centralized and decentralized R&D to align producer and consumer incentives.

Cost analysis of adding hypertension and diabetes management into routine HIV care in Mbarara and Ibanda districts, Uganda

BackgroundIn 2016, Uganda introduced services for hypertension and diabetes in selected HIV clinics. We evaluated the costs associated with scaling up these services in HIV clinics in Mbarara and Ibanda districts, Uganda.MethodsWe estimated the annual costs of providing hypertension and diabetes services using an activity-based costing approach from the health system perspective in ten randomly selected HIV clinics in Mbarara and Ibanda districts. Cost inputs included 2023 data on costs of medications, health provider time, salaries, training costs, and monitoring costs. We determined the average annual cost and medication costs for hypertension and diabetes treatment per enrolled adult patient, stratified by type of health facility.ResultsThe total annual cost of hypertension and diabetes management services in ten selected HIV clinics was estimated to be $413,850 (range: $8,386 − 186,973). The annual average clinic-level cost per enrolled patient was estimated at $14 (range: $7 − 31). Of the total annual cost, the cost of provider time for initial and follow-up visits represented the largest cost component in 5/10 clinics (mean: 37%, range [13–58%]). In 4/10 clinics, the major cost components were the costs of medication, diagnostic tests, and related supplies (mean: 37%, range [10–75%]). The average cost per enrolled adult patient was $11 at public facilities and $21 in private not-for-profit facilities. The average medication cost per patient for hypertension was $24 (range: $7 − 97) annually; $13 at public facilities and $50 at private not-for-profit facilities. For diabetes treatment, the average annual medication cost per patient was estimated at $14 (range: $6 − 35); $11 at public facilities and $22 at private not-for-profit facilities.ConclusionAdding hypertension and diabetes management to routine HIV care might be feasible based on the estimated annual cost per patient. Hypertension and diabetes treatment was more costly in private not-for-profit facility-based clinics than at public facilities. This variation was primarily driven by higher medication procurement prices at private facilities, revealing a potential area for optimizing costs through improved procurement practices.

Transfer learning model for anomalous event recognition in big video data

Video surveillance faces challenges due to the need for improved anomalous event recognition techniques for human activity recognition. Growing security concerns make standard CCTV systems insufficient because of high monitoring costs and operator exhaustion. Therefore, automated security systems with real-time event recognition are essential. This research introduces a semantic key frame extraction algorithm based on action recognition to minimize frame volume big video data. This approach has not been previously applied with ResNet50, VGG19, EfficientNetB7, and ViT_b16 models for recognizing anomalous events in surveillance videos. The findings demonstrate the effectiveness of this method in achieving high accuracy rates. The proposed method addresses the challenges posed by large volumes of frames generated by surveillance videos, requiring effective processing techniques. A large number of videos from the UCF-Crime dataset were used for proposed model evaluation, including both abnormal and normal videos during the training and testing phase. EfficientNetB7 achieved 86.34% accuracy, VGG19 reached 87.90%, ResNet50 attained 90.46%, and ViT_b16 excelled with 95.87% accuracy. Compared to state-of-the-art models from other studies, the transformer model (ViT_b16) outperformed these algorithms, demonstrating significant improvements in recognizing anomalous events.

AI-Powered Test Automation for Financial Cloud Applications Using LLMs and Serverless Computing

This article proposes a conceptual framework for AI-powered testing of financial cloud applications by integrating Large Language Models (LLMs) with serverless computing architecture. The framework addresses critical challenges in financial software testing, including regulatory compliance, security validation, and performance scalability. Through theoretical analysis, the design incorporates sophisticated prompt engineering techniques and automated compliance monitoring systems to achieve high compliance validation accuracy while potentially reducing monitoring costs. The architecture suggests superior performance capabilities, potentially processing concurrent test executions efficiently while reducing operational costs compared to traditional testing infrastructures. The framework design anticipates improvements in test generation efficiency, regulatory compliance coverage, and overall testing costs while maintaining robust security standards through enhanced vulnerability detection mechanisms. The theoretical integration of LLMs with serverless computing shows promise in addressing the complex challenges of financial software testing, offering a potential pathway to a scalable, cost-effective solution that could meet the stringent requirements of modern financial cloud applications while ensuring regulatory compliance and security.

Human-in-the-Loop Reinforcement Learning in Continuous-Action Space.

Human-in-the-loop for reinforcement learning (RL) is usually employed to overcome the challenge of sample inefficiency, in which the human expert provides advice for the agent when necessary. The current human-in-the-loop RL (HRL) results mainly focus on discrete action space. In this article, we propose a Q value-dependent policy (QDP)-based HRL (QDP-HRL) algorithm for continuous action space. Considering the cognitive costs of human monitoring, the human expert only selectively gives advice in the early stage of agent learning, where the agent implements human-advised action instead. The QDP framework is adapted to the twin delayed deep deterministic policy gradient algorithm (TD3) in this article for the convenience of comparison with the state-of-the-art TD3. Specifically, the human expert in the QDP-HRL considers giving advice in the case that the difference between the twin Q -networks' output exceeds the maximum difference in the current queue. Moreover, to guide the update of the critic network, the advantage loss function is developed using expert experience and agent policy, which provides the learning direction for the QDP-HRL algorithm to some extent. To verify the effectiveness of QDP-HRL, the experiments are conducted on several continuous action space tasks in the OpenAI gym environment, and the results demonstrate that QDP-HRL greatly improves learning speed and performance.

Effectiveness of photoplethysmography-based rhythm monitoring after atrial fibrillation ablation using a smartphone application: DIGITOTAL study

BackgroundRecurrence after atrial fibrillation (AF) ablation is frequent. Monitoring with long-term electrocardiograms (ECGs) is constrained by limited monitoring time, measurement dispersion, and cost. Selected photoplethysmography (PPG) smartphone applications have demonstrated excellent accuracy for AF detection and could mitigate these limitations. ObjectiveWe aimed to compare the effectiveness of digital follow-up using a PPG-based smartphone application against conventional ECG-based follow-up for the detection of atrial arrhythmia recurrence after ablation. MethodsPatients undergoing AF ablation were consecutively enrolled and monitored by a 24-hour ECG at 3, 6, and 12 months on top of the ECGs conducted for clinical indications (conventional follow-up). In addition, patients were instructed to perform PPG measurements twice daily or whenever symptoms were perceived during the course of 1 year (digital follow-up). ResultsIn total, 96 patients (69% male; mean age, 64 ± 9 years) performed 39,895 PPG measurements. The compliance rate (number performed/prescribed) was 92.6% for ECGs and 78.2% for PPG recordings. After 1 year of follow-up, atrial arrhythmia recurrence was detected in 17.7% of patients by conventional follow-up and in 38.5% of patients by digital follow-up (odds ratio, 3.4; 95% confidence interval [CI], 1.7–7.1). The CI lower limit exceeded the predefined noninferiority margin (P for noninferiority > .001). Hence, superiority was tested (P for superiority = .001). The negative predictive value of digital follow-up for atrial arrhythmia detected with conventional follow-up was 98.3% (95% CI, 90.9%–99.9%). ConclusionDigital rhythm follow-up using a smartphone application with PPG was noninferior to conventional follow-up in detecting atrial arrhythmia recurrence between 3 and 12 months after ablation. Moreover, digital follow-up significantly increased the detection of atrial arrhythmia.

Information technology and lender competition

We study how information technology (IT) affects lender competition, entrepreneurs’ investment, and welfare in a spatial model. The effects of an IT improvement depend on whether it weakens the influence of lender–borrower distance on monitoring costs. If it does, it has a hump-shaped effect on entrepreneurs’ investment and social welfare. If not, competition intensity does not vary, improving lender profits, entrepreneurs’ investment, and social welfare. When entrepreneurs’ moral hazard problem is severe, IT-induced competition is more likely to reduce investment and welfare. We also find that lenders’ price discrimination is not welfare-optimal. Our results are consistent with received empirical work on lending to SMEs.

Water Leak Detection: A Comprehensive Review of Methods, Challenges, and Future Directions

This paper provides a comprehensive review of the methods and techniques developed for detecting leaks in water distribution systems, with a focus on highlighting their strengths, weaknesses, and areas for future research. Given the substantial economic, social, and environmental impacts of undetected leaks, timely detection and precise location of leaks are critical concerns for water authorities. This review categorizes existing methods into traditional approaches, such as manual sounding, and modern techniques involving smart water management and sensor technologies. A multidimensional bibliometric analysis was employed to systematically identify, select, and evaluate 600 scholarly articles on water leak detection, sourced from the Scopus database over a 23-year period (2000–2023). The paper evaluates each method based on leak sensitivity, burst detection, continuous monitoring, alarm accuracy, and implementation costs. Novel insights include an analysis of emerging smart water technologies and their integration into real-world water distribution networks, offering improved efficiency in leak detection. The paper also identifies key gaps in current research and suggests future directions for advancing the accuracy and cost-effectiveness of these technologies.

Patient preferences for ocular hypertension monitoring: a discrete choice experiment

Background/aimsTo elicit the preferences and calculate the willingness to pay (WTP) of patients with ocular hypertension (OHT) for eye monitoring services in the UK.MethodsPatients with OHT aged at least 18...

A dynamic solvent chamber propagation estimation framework using RNN for warm solvent injection in heterogeneous reservoirs

Warm solvent injection (WSI), injecting low-temperature solvent into formations to reduce the viscosity of heavy oil, is a clean technology for heavy oil production through reducing greenhouse gas emissions and water usage. The success of WSI operation depends on the uniform development and propagation of solvent chambers in reservoirs. However, reservoir heterogeneity stemming from shale barriers plays a detrimental role in the conformance of solvent chamber development and oil production rate. In this work, we developed a novel recurrent neural network (RNN)-based framework with the capability of efficiently tracking and estimating the solvent chamber positions in heterogeneous reservoirs based on only production time-series data. The developed estimation model utilizes the “sequence-to-sequence" mapping methodology to correlate observed production time-series sequence and solvent chamber edge sequence via a long short-term memory (LSTM) algorithm. The trained RNN models exhibit high accuracy, evidenced by the predicted dynamic solvent chamber locations match the corresponding true locations from numerical simulation, with a high coefficient of determination (R2) and a low mean squared error. Specifically, the achieved R2 values exceed 0.98 on both the training and testing data. The developed RNN-based workflow was tested via several cases from both regularly- and irregularly-shaped shale barriers, and the results were promising. The predicted solvent chambers showed strong agreement with those obtained from numerical simulations. The major benefits of this workflow include reducing computational time and saving overall monitoring and tracking costs for conventional techniques. The present work would provide a good demonstration of the capability of practical integration of machine learning methods in solving engineering problems.

Evolution mechanism of deviated well fiber-optic strain induced by single-fracture propagation during fracturing in horizontal wells

Considering the high construction cost of horizontal adjacent well monitoring and the lack of vertical adjacent well fiber optic for obtaining fracturing information, this paper proposes the use of deviated wells with fiber optics for monitoring purposes. To demonstrate the advantages of deviated well fiber optics and the feasibility of their deployment, this paper constructs a forward model based on the finite element coupled with cohesive element approach to simulate the strain induced by the propagation of a single hydraulic fracture in horizontal wells on deviated well fiber optics, and conducts a numerical simulation analysis of the strain induced by the propagation of a single hydraulic fracture on deviated well fiber optics. The results show that the strain evolution induced by single-fracture propagation in deviated well fiber optics can be divided into four stages: strain-enhancing, strain-converging, tensile strain-expanding, and linear strain-converging. The strain evolution characteristics of deviated well fiber optics are manifested as follows: a “heart-shaped” tensile strain convergence zone with a certain deviation appears in the middle, which subsequently converges into a tensile strain convergence band, with compressive strain convergence zones on both sides, and an expanding tensile strain convergence zone on the outer side of the compressive strain convergence band. The analysis finds that when the well inclination angle is greater than 45°, the strain response characteristics of deviated well fiber optics are mainly governed by the width expansion of the fracture, and when less than 45°, they are mainly governed by the height expansion of the fracture. Changes in the azimuth angle can cause a deviation of the “heart-shaped” tensile strain area and the compressive strain convergence zone in the fiber-optic strain waterfall plot, with larger deviations corresponding to smaller azimuth angles. The depth at which the deviated well fiber optics are deployed, reaching the depth of the horizontal section of the horizontal well, can reflect the upward expansion of the fracture height. The results of the analysis illustrate the advantages of deviated well fiber optics in obtaining both fracture width and height expansion information simultaneously and propose a method for selecting suitable deviated well fiber-optic construction parameters based on fracturing monitoring needs. This research can reduce the construction cost of deploying fiber optics in adjacent wells and has significant implications for guiding the layout of adjacent well fiber optics.

Anomaly Sign Detection for Automatic Ticket Gates by the Histogram Limitation Method

It is crucial to appropriately maintain automatic ticket gates (ATGs) to keep transportation operating smoothly in urban areas. Although the average failure rate of new ATGs is extremely low, continuous operation for many years might lead to unstable performance due to deterioration, and the need for periodic maintenance to avoid fatal faults might halt operations for extended periods. To detect anomalies at an early stage, “anomaly signs” can be utilized to flag ATGs for maintenance by service engineers before anomalies occur. In addition, to minimize the cost of ATG monitoring, the necessary computing resources should be minimized, which means using only light-weight statistical methods rather than deep learning or machine learning. In this paper, we focus on the automatic separation modules inside ATGs that separate multiple tickets by complicated mechatronic controls because this module is the major cause of maintenance calls from station attendants. We propose a simple anomaly sign detection, called the histogram limitation method (HLM). We evaluated the anomaly sign scores over time with maintenance timing and compared them with the conventional fast unsupervised anomaly detection method, Histogram-Based Outlier Score (HBOS) widely used in various domains. The experimental results using real field ATG monitoring data show that HLM successfully detected anomaly signs before a maintenance call was necessary, which is better performance compared with HBOS. Despite being a simple modification based on HBOS, HLM also provides anomaly sign scores that agree adequately with assessments by maintenance service engineers.

A cost-effectiveness analysis of stereotactic ablative radiotherapy versus conventionally fractionated radiotherapy in the management of stage 1 non-small-cell lung cancer: Results from the TROG 09.02 CHISEL study.

Stereotactic ablative radiotherapy (SABR) is a standard of care treatment for medically inoperable early-stage non-small-cell lung cancer (NSCLC). The CHISEL trial was a phase 3 randomised controlled trial that compared SABR to conventional radiation therapy (CRT). Using patient-level data, we compared the cost-effectiveness of SABR and CRT for early-stage NSCLC. Data on treatment exposure, outcomes (recurrence, survival) and quality of life (QoL; EORTC QLQ-C30) were sourced from the trial. Quality-adjusted life years (QALYs) were estimated for the trial period using Australian utility weights for the EORTC QLQ-C30-derived QLU-C10D. Costs related to simulation, planning, delivery, verification and post-treatment monitoring were estimated by applying Australian Medicare Benefits Schedule fees. The costs of post-progression therapy and grade ≥3 toxicity were estimated using trial data and relevant literature sources. Cost-effectiveness was investigated as the incremental cost per QALY gained for SABR compared to CRT. Complete QoL data were available for 21 patients: 14 in the SABR arm and 7 in the CRT arm. Mean QALYs discounted at 5% per annum were similar between arms: 12.68 months for SABR and 12.12 months for CRT. The mean costs of delivering SABR and CRT were $4763 and $6817, respectively. The costs of monitoring were similar in both arms, $4856 and $4853 for SABR and CRT. The mean costs of post-progression therapy were $24,572 for SABR and $42,801 for CRT. The mean costs of grade ≥3 toxicity were $809 in the SABR arm and $132 in the CRT arm. Therefore, the total mean cost for SABR over the period of interest was lower for SABR than CRT. Given lower mean costs and numerically higher QALYs for SABR compared with CRT, an incremental cost-effectiveness ratio was not calculated. Compared to CRT, SABR is a cost-effective treatment for early-stage NSCLC as the estimated upfront treatment cost and the cost of subsequent care are lower for SABR for comparable mean QALYs. Assessment of the lifetime QALYs and projections of cost estimation will provide a better indication of the long-term cost-effectiveness of SABR.

Therapeutic Hydroxychloroquine Blood Levels Are Associated With Fewer Hospitalizations and Possible Reduction of Health Disparities in Lupus.

Nonadherence to receiving hydroxychloroquine (HCQ) is associated with a three-fold higher risk of lupus-related hospitalization. Monitoring HCQ blood levels could improve adherence to receiving HCQ and efficacy. Yet, HCQ level monitoring is not routinely done partially due to cost and coverage concerns. To establish HCQ level monitoring cost-effectiveness, we reported the following: (1) risk of acute care by HCQ blood levels, and (2) cost of HCQ monitoring versus acute care visits. HCQ blood levels were measured during routine lupus visits. HCQ levels were categorized as follows: (1) subtherapeutic (<750 ng/mL), (2) therapeutic (750-1,200 ng/mL), or (3) supratherapeutic (>1,200 ng/mL). All lupus-related acute care visits (emergency room visits/hospitalizations) after the index clinic visit until next follow-up were abstracted. In our primary analysis, we examined associations between HCQ levels and time to first acute care visit in all patients and subgroups with higher rates of acute care. A total of 39 lupus-related acute care visits were observed in 181 patients. Therapeutic HCQ blood levels were associated with 66% lower rates of acute care. In our cohort, two groups, Black or Hispanic patients and those with public insurance, faced three to four times higher rates of acute care. Levels within 750 to 1,200 ng/mL were associated with 95% lower rates of acute care use in subgroups with higher acute care use. HCQ blood levels within 750 to 1,200 ng/mL are associated with lower rates of acute care in all patients with lupus, including groups with higher rates of acute care. Future clinical trials should establish the causal association between HCQ level monitoring and acute care in patients with lupus.

Offsetting the void of overboarding risk: The role of financial expertise in an emerging market

To contribute to the growing literature on the implications of board busyness for firm outcomes, this study investigates the relationship between overboarding risk (OBRisk) and firm value (FV) in an emerging market setting and whether financial expertise matters for this relationship. We apply the least square dummy variable (LSDV) two-way fixed effect estimator to panel data of non-financial publicly-listed South African firms from 2014–2020. We provide empirical evidence that OBRisk negatively affects FV. We further find substantial evidence that the benefits of independent directors’ financial expertise outweigh the agency costs of weak monitoring orchestrated by their overboardedness. Specifically, the marginal effect of independent directors’ financial expertise at the maximum level attenuates the effect of OBRisk on FV, indicating that high-level financial expertise is value-relevant. This study comes at an appropriate time when directorship regulation is a topical issue among sub-Saharan African regulators.