Significant Cost Research Articles

Observing System Simulation Experiments (OSSEs) in Support of Next-Generation NOAA Satellite Constellation

Abstract Between 2014 and 2018, the National Oceanic and Atmospheric Administration conducted the NOAA Satellite Observing System Architecture (NSOSA) study to plan for the next generation of operational environmental satellites. The study generated some important questions that could be addressed by observing system simulation experiments (OSSEs). This paper describes a series of OSSEs in which benefits to numerical weather prediction from existing observing systems are combined with enhancements from potential future capabilities. Assessments include the relative value of the quantity of different types of thermodynamic soundings for global numerical weather applications. We compare the relative impact of several sounding configuration scenarios for infrared (IR), microwave (MW), and radio occultation (RO) observing capabilities. The main results are 1) increasing the revisit rate for satellite radiance soundings produces the largest benefits but at a significant cost by requiring an increase in the number of polar-orbiting satellites from 2 to 12; 2) a large positive impact is found when the number of RO soundings per day is increased well beyond current values and other observations are held at current levels of performance; 3) RO can be used as a mitigation strategy for lower MW/IR sounding revisit rates, particularly in the tropics; and 4) smaller benefits result from increasing the horizontal resolution along the track of the satellites of MW/IR satellite radiances. Furthermore, disaggregating IR and MW instruments into six evenly distributed sun-synchronous orbits is slightly more beneficial than when the same instruments are combined and collocated on three separate orbits.

Irritable bowel syndrome following infectious COVID-19: East Java, Indonesia, 2023

Irritable bowel syndrome (IBS) is a functional disorder that causes chronic abdominal pain without a known cause. It is a common, chronic gastrointestinal (GI) motility disorder with bothersome symptoms that often lower quality of life and activity. In addition, Patients and healthcare facilities also face significant financial costs. COVID-19 directly damages the digestive system and alters the complex interaction of physical, mental, and social factors that cause digestive problems. SARS-CoV-2 survivors in personal isolation will be examined for IBS prevalence. The dates of this descriptive cross-sectional study are January through April 2023. Rome IV criteria and an online questionnaire were used to confirm the diagnosis of IBS. The principal location where polls have been sent is East Java, Indonesia. The 96 COVID-19 survivors aged 18–60 of both genders was included during self-quarantine. There were 59 females (61.46%) and 37 males (38.54%). The prevalence of IBS was discovered to be 19 (19.79%) among a total of 96 patients. This could be because self-quarantined people have more stable living conditions than hospitalized people. Based on these findings, it is suggested that future research consider gender as the primary proxy for identifying irritable bowel syndrome (IBS).

Structured Pruning for Deep Convolutional Neural Networks: A Survey.

The remarkable performance of deep Convolutional neural networks (CNNs) is generally attributed to their deeper and wider architectures, which can come with significant computational costs. Pruning neural networks has thus gained interest since it effectively lowers storage and computational costs. In contrast to weight pruning, which results in unstructured models, structured pruning provides the benefit of realistic acceleration by producing models that are friendly to hardware implementation. The special requirements of structured pruning have led to the discovery of numerous new challenges and the development of innovative solutions. This article surveys the recent progress towards structured pruning of deep CNNs. We summarize and compare the state-of-the-art structured pruning techniques with respect to filter ranking methods, regularization methods, dynamic execution, neural architecture search, the lottery ticket hypothesis, and the applications of pruning. While discussing structured pruning algorithms, we briefly introduce the unstructured pruning counterpart to emphasize their differences. Furthermore, we provide insights into potential research opportunities in the field of structured pruning. A curated list of neural network pruning papers can be found at: https://github.com/he-y/Awesome-Pruning. A dedicated website offering a more interactive comparison of structured pruning methods can be found at: https://huggingface.co/spaces/he-yang/Structured-Pruning-Survey.

Mechanism of lightning damage to glass fiber composite wind turbine blades

Wind turbine blades (WTBs) are susceptible to lightning damage, resulting in significant costs for repair and replacement, which poses a considerable economic burden on wind farms. Therefore, this study investigates the mechanism of lightning damage of glass fiber composite (GFC)-WTBs to reduce the risk of such damage. The damage of GFC-WTBs caused by lightning strikes was analyzed using a numerical simulation method. The lightning pilot was simulated using high-voltage rod electrodes, and subsequently, the electric field strength and damage area on the blade surface under different conditions were measured. Additionally, a simulation model based on finite element analysis was developed to further predict and validate the experimental findings. The results reveal that the maximum electric field intensity was observed in the blade tip. Notably, the electric field intensity was found to be over 798,000 V/m when the rod electrode was positioned at a 0-degree angle with the blade tip. Further investigation revealed a non-linear and positive correlation between the damage area of GFC and lightning current amplitude, charge, and specific energy. In conclusion, the study provides a comprehensive examination of the relationship between lightning current and the damage to GFC-WTBs, as well as elucidates the mechanism of lightning damage to GFC-WTBs.

A systematic review of in-memory database over multi-tenancy

The significant cost and time are essential to obtain a comprehensive response, the response time to a query across a peer-to-peer database is one of the most challenging issues. This is particularly exact when dealing with large-scale data processing, where the traditional approach of processing data on a single machine may not be sufficient. The need for a scalable, reliable, and secure data processing system is becoming increasingly important. Managing a single in-memory database instance for multiple tenants is often easier than managing separate databases for each tenant. The research work is focused on scalability with multi-tenancy and more efficiency with a faster querying performance using in-memory database approach. We compare the performance of a row-oriented approach and column-oriented approach on our benchmark human resources (HR) schema using Oracle TimesTen in-memory database. Also, we captured some of the key advantages on optimization dimension(s) are the traditional approach, late-materialization, compression and invisible join on column-store (c-store) and row-base. When compression and late materialization are enabled in a query set; it improves the overall performance of query sets. In particular, the paper aims to elucidate the motivations behind multi-tenant application requirements concerning the database engine and highlight major designs over in-memory database for the tenancy approach on cloud.

Early prediction of end-stage kidney disease using electronic health record data: a machine learning approach with a 2-year horizon.

In the United States, end-stage kidney disease (ESKD) is responsible for high mortality and significant healthcare costs, with the number of cases sharply increasing in the past 2 decades. In this study, we aimed to reduce these impacts by developing an ESKD model for predicting its occurrence in a 2-year period. We developed a machine learning (ML) pipeline to test different models for the prediction of ESKD. The electronic health record was used to capture several kidney disease-related variables. Various imputation methods, feature selection, and sampling approaches were tested. We compared the performance of multiple ML models using area under the ROC curve (AUCROC), area under the Precision-Recall curve (PR-AUC), and Brier scores for discrimination, precision, and calibration, respectively. Explainability methods were applied to the final model. Our best model was a gradient-boosting machine with feature selection and imputation methods as additional components. The model exhibited an AUCROC of 0.97, a PR-AUC of 0.33, and a Brier score of 0.002 on a holdout test set. A chart review analysis by expert physicians indicated clinical utility. An ESKD prediction model can identify individuals at risk for ESKD and has been successfully deployed within our health system.

Do accent and input modality modulate processing of language switches in bilingual language comprehension?

We examined how bilinguals process language switches between their first (L1) and second language (L2). Language switching costs (slower responses to language switch than nonswitch trials) appear to arise more systematically in production than in comprehension, possibly because the latter context might sometimes elicit less language coactivation (Declerck et al., 2019). This might reduce language competition and in turn the need for bilinguals to apply language control when processing language switches. Yet even in comprehension, language coactivation may vary depending on variables such as the accent of the speaker (e.g., whether the L2 words are pronounced with an L1 or L2 accent) and input modality (spoken or written). In three experiments conducted during 2021-2022, we tested how unbalanced Mandarin-English bilinguals processed language switches during comprehension and the potential influence of a speaker's accent and input modality. Overall, across settings, participants experienced significant language switching costs. In some conditions, switching costs were larger to L1-Mandarin than to L2-English, an asymmetry consistent with the participants' dominance in L1-Mandarin and the application of language control. However, manipulating accent and input modality did not influence language switches, suggesting they did not impact language coactivation sufficiently to modulate language control. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Testing nanometer memories: a review of architectures, applications, and challenges

Newer defects in memories arising from shrinking manufacturing technologies demand improved memory testing methodologies. The percentage of memories on chips continues to rise. With shrinking technologies (10 nm up to 1.8 nm), the structure of memories is becoming denser. Due to the dense structure and significant portion of a chip, the nanometer memories are highly susceptible to defects. High-frequency specifications, the complexity of internal connections, and the process variation due to newer manufacturing technology further increased the probability of the physical failure of memories to a great extent. Memories need to be defect-free for the chip to operate successfully. Therefore, testing embedded memories has become crucial and is taking significant test costs. Researchers have proposed multiple approaches considering these factors to test the nanometer memories. They include using new fault models, march algorithms, memory built-in self-test (MBIST) architectures, and validation strategies. This paper surveys the methodologies presented in recent times. It discusses the core principles used in them, along with benefits. Finally, it discusses key opens in each and offers the scope for future research.

Concept paper: Innovative approaches to food quality control: AI and machine learning for predictive analysis

The concept paper explores the potential of artificial intelligence (AI) and machine learning (ML) in revolutionizing food quality control processes. In response to the growing challenges faced by the food industry in ensuring consistent quality and safety standards, this paper proposes leveraging advanced technologies to enhance predictive analysis. The traditional methods of food quality control are often reactive and time-consuming, leading to inefficiencies and increased risks of contamination or spoilage. By harnessing AI and ML algorithms, businesses can shift towards proactive strategies, predicting potential issues before they arise and implementing preventive measures accordingly. Key components of the proposed approach include data collection from various sources such as sensors, supply chain records, and historical quality data. Through sophisticated data analysis techniques, AI systems can identify patterns, anomalies, and correlations that might indicate deviations from expected quality standards. Moreover, ML models can continuously learn and adapt based on new data, improving prediction accuracy over time. Implementation of AI-driven predictive analysis in food quality control offers several benefits. Automation of quality control processes reduces manual effort and enables real-time monitoring, enabling timely interventions to maintain product quality. By minimizing the likelihood of product recalls, waste, and rework, businesses can achieve significant cost savings associated with quality control measures. Consistently delivering high-quality products strengthens consumer trust and loyalty, leading to increased market competitiveness and brand reputation. AI-powered systems can assist in ensuring compliance with stringent food safety regulations by providing comprehensive documentation of quality control measures and outcomes. However, successful adoption of AI and ML technologies in food quality control requires overcoming various challenges, including data privacy concerns, integration with existing systems, and ensuring the reliability and interpretability of AI-driven insights. the integration of AI and ML for predictive analysis represents a transformative opportunity for the food industry to modernize quality control practices and uphold the highest standards of safety and excellence. Embracing innovation in this domain is essential for staying competitive in a rapidly evolving market landscape and meeting the evolving expectations of consumers and regulatory bodies alike.

SasWOT: Real-Time Semantic Segmentation Architecture Search WithOut Training

In this paper, we present SasWOT, the first training-free Semantic segmentation Architecture Search (SAS) framework via an auto-discovery proxy. Semantic segmentation is widely used in many real-time applications. For fast inference and memory efficiency, Previous SAS seeks the optimal segmenter by differentiable or RL Search. However, the significant computational costs of these training-based SAS limit their practical usage. To improve the search efficiency, we explore the training-free route but empirically observe that the existing zero-cost proxies designed on the classification task are sub-optimal on the segmentation benchmark. To address this challenge, we develop a customized proxy search framework for SAS tasks to augment its predictive capabilities. Specifically, we design the proxy search space based on the some observations: (1) different inputs of segmenter statistics can be well combined; (2) some basic operators can effectively improve the correlation. Thus, we build computational graphs with multiple statistics as inputs and different advanced basis arithmetic as the primary operations to represent candidate proxies. Then, we employ an evolutionary algorithm to crossover and mutate the superior candidates in the population based on correlation evaluation. Finally, based on the searched proxy, we perform the segmenter search without candidate training. In this way, SasWOT not only enables automated proxy optimization for SAS tasks but also achieves significant search acceleration before the retrain stage. Extensive experiments on Cityscapes and CamVid datasets demonstrate that SasWOT achieves superior trade-off between accuracy and speed over several state-of-the-art techniques. More remarkably, on Cityscapes dataset, SasWOT achieves the performance of 71.3% mIoU with the speed of 162 FPS.

Analyzing determinants of whistleblowing intention with the gamification method

Whistleblowing plays a critical role in building healthy transparent and responsible organizations. Whistleblowing can be challenging, however, as it may result in significant costs for the whistleblower. Facilitating whistleblowing that resists illegal and unethical behaviors within organizations requires management to enhance behavior while protecting whistleblowers. From the perspective of risk-taking, the current research examines which factors are associated with whistleblowing intention using an original approach, the gamification method. Accordingly, in addition to the effects of respondents’ characteristics including Big Five personalities, this research tests how the immediate supervisor’s sex and organizational size, which are randomly assigned by interactive webcomics using hypothetical situations and experimental questionnaires, affect whistleblowing intention. Logistic regression analysis shows openness is significantly associated with whistleblowing intention. In the hypothetical situation, whistleblowers are more likely to come forward if they have a supervisor of a different sex and/or are in a larger organization.

Application of aggregation operators for forecasting [formula omitted] fluctuations: From available Caribbean data sites to unequipped ones

Air pollution is a substantial issue for public health. Predicting the levels of airborne particles, especially those originating from natural phenomena like sand mists from the African dust belt, enables more effective warnings for affected populations. Currently, there are only three islands that continuously measure PM10 concentrations in the Caribbean area. Thus, there is a main issue for the forecast of these particulate pollutants for unequipped sites. In this article, a new approach based on the use of aggregation operators in a non-fuzzy framework allows to predict the quantity of PM10 for certain sites in this geographical area. It is the characteristics of non-linearity and downward reinforcement of the fuzzy operators used (triple Pi and t-norms) which make it possible to model the evolution of a phenomenon as complex as the sand mists from African dust belt. The time series having been normalized, the tests were carried out on two of the three sites (Martinique and Guadeloupe) to predict the values of the third site (Puerto Rico) and assess the quality of the prediction. The mean absolute error (MAE), the mean bias error (MBE), the root mean square error (RMSE) and the index of agreement (IOA) values show that this approach provides significant results. The African dust seasonality strongly impacts these performances metrics. These initial promising findings emphasize that the utilization of aggregation operators is a robust method for forecasting PM10 fluctuations, eliminating the need for complex parameterization or learning phase that would otherwise entail significant computational costs.

“HOT” ChatGPT: The Promise of ChatGPT in Detecting and Discriminating Hateful, Offensive, and Toxic Comments on Social Media

Harmful textual content is pervasive on social media, poisoning online communities and negatively impacting participation. A common approach to this issue is developing detection models that rely on human annotations. However, the tasks required to build such models expose annotators to harmful and offensive content and may require significant time and cost to complete. Generative AI models have the potential to understand and detect harmful textual content. We used ChatGPT to investigate this potential and compared its performance with MTurker annotations for three frequently discussed concepts related to harmful textual content on social media: Hateful, Offensive, and Toxic (HOT). We designed five prompts to interact with ChatGPT and conducted four experiments eliciting HOT classifications. Our results show that ChatGPT can achieve an accuracy of approximately 80% when compared to MTurker annotations. Specifically, the model displays a more consistent classification for non-HOT comments than HOT comments compared to human annotations. Our findings also suggest that ChatGPT classifications align with the provided HOT definitions. However, ChatGPT classifies “hateful” and “offensive” as subsets of “toxic.” Moreover, the choice of prompts used to interact with ChatGPT impacts its performance. Based on these insights, our study provides several meaningful implications for employing ChatGPT to detect HOT content, particularly regarding the reliability and consistency of its performance, its understanding and reasoning of the HOT concept, and the impact of prompts on its performance. Overall, our study provides guidance on the potential of using generative AI models for moderating large volumes of user-generated textual content on social media.

How current law and policy supports providers of NHS healthcare in England to respond to patient harm: A scoping review protocol.

Harm arising from National Health Service (NHS) healthcare results in significant human cost for the patient, those who care for them, and the medical staff involved. Furthermore, patient harm results in substantial financial costs to the public purse. Improving how NHS providers in England respond to patient harm could reduce the number of claims for clinical negligence brought against NHS. Doing so will ensure those affected receive the justice they deserve and protect the public purse. Law and policy are key to supporting providers of NHS healthcare to respond to patient harm but are not necessarily understood and therefore can be challenging to apply to practice. Research exploring how law and policy supports this understanding is limited. The purpose of this scoping review is to address this knowledge gap and improve understanding by critically evaluating how law and policy supports providers of NHS healthcare in England to respond to patient harm. The review will use the methodological framework proposed by Arskey and O'Malley, Levac et al and the Joanna Briggs Institute. Search strategies will be developed using selected key words and index terms. MEDLINE, CINAHL, and Westlaw and reference lists of relevant publications will be searched to identify relevant grey literature. Two reviewers will independently assess the extracted data against the eligibility criteria. All studies identified will be charted and the results presented as a narrative synthesis.

On the Importance of Precise Positioning in Robotised Agriculture

The precision of agro-technical operations is one of the main hallmarks of a modern approach to agriculture. However, ensuring the precise application of plant protection products or the performance of mechanical field operations entails significant costs for sophisticated positioning systems. This paper explores the integration of precision positioning based on the global navigation satellite system (GNSS) in agriculture, particularly in fieldwork operations, seeking solutions of moderate cost with sufficient precision. This study examines the impact of GNSSs on automation and robotisation in agriculture, with a focus on intelligent agricultural guidance. It also discusses commercial devices that enable the automatic guidance of self-propelled machinery and the benefits that they provide. This paper investigates GNSS-based precision localisation devices under real field conditions. A comparison of commercial and low-cost GNSS solutions, along with the integration of satellite navigation with advanced visual odometry for improved positioning accuracy, is presented. The research demonstrates that affordable solutions based on the common differential GNSS infrastructure can be applied for accurate localisation under real field conditions. It also underscores the potential of GNSS-based automation and robotisation in transforming agriculture into a more efficient and sustainable industry.

Cost-Effectiveness of Novel Agent Regimens for Transplant-Eligible Newly Diagnosed Multiple Myeloma Patients in India.

Survival outcomes for multiple myeloma have improved dramatically since the introduction of novel therapeutic agents. While these drugs are highly effective in improving survival outcomes and quality of life in patients with multiple myeloma, they come at a significant cost. We assessed the cost-effectiveness of bortezomib-based triplet or quadruplet drug regimens in isolation and followed by autologous hematopoietic stem cell transplantation (AHSCT) for the treatment of newly diagnosed multiple myeloma (NDMM) in the Indian context. A Markov model was developed to assess the health and economic outcomes of novel drug regimens with and without AHSCT for the treatment of NDMM in India. We estimated the lifetime quality-adjusted life-years (QALYs) and costs in each scenario. The incremental cost-effectiveness ratios (ICERs) were computed and compared against the current willingness-to-pay threshold of a one-time per capita gross domestic product of ₹146,890 (US$1,927.70) for India. Parameter uncertainty was assessed through Monte Carlo probabilistic sensitivity analysis. Among seven treatment sequences, the VCd (bortezomib, cyclophosphamide, dexamethasone)alone arm has the lowest cost and health benefits as compared to four treatment sequences, namely VTd (bortezomib, thalidomide, dexamethasone) alone, VRd (bortezomib, lenalidomide, dexamethasone) alone, VRd plus AHSCT and DVRd (Daratumumab, bortezomib, lenalidomide, dexamethasone) plus AHSCT. It was found that VTd plus AHSCT and VCd plus AHSCT arms were extendedly dominated (ED) by combination of two alternative treatments. Among the five non-dominated strategies, VRd has a lowest incremental cost of ₹ 2,20,093 (US$2,888) per QALY gained compared to VTd alone followed by VRd plus AHSCT [₹3,14,530 (US$4,128) per QALY gained] in comparison to VRd alone. None of the novel treatment sequences were found to be cost-effective at the current WTP threshold of ₹1,46,890 (US$1,927.7). At the current WTP threshold of one-time per capita GDP (₹ 146,890) of India, VRd alone and VRd plus AHSCT has 38.1% and 6.9% probability to be cost-effective, respectively. Reduction in current reimbursement rates of novel drugs, namely VRd, lenalidomide, and pomalidomide plus dexamethasone under national insurance program and societal cost of transplant by 50%, would make VRd plus AHSCT and VTd plus AHSCT cost-effective at an incremental cost of ₹40,671 (US$34) and ₹97,639 (US$1,281) per QALY gained, respectively.

ANALISIS PERHITUNGAN BIAYA SATUAN TINDAKAN ORIF (Open Reduction Internal Fixation) FRAKTUR FEMUR MENGGUNAKAN METODE ABC (Activity Based Costing) (STUDI KASUS DI RS PKU MUHAMMADIYAH BANTUL)

Background: The determination of operating room rates at PKU Muhammadiyah Hospital in Bantul is currently using fee for service method where the operating rate is determined by the calculation of the cost components that occur as the cost of medical services, consumables, medicines, and the rent of operating room, but it has not included all the components of the activity-based costs associated with clinical pathways. However, this surgical procedure may result significant costs to the hospital.
 Methods: The study was conducted at PKU Muhammadiyah Hospital in Bantul. This research is descriptive qualitative case study design. The sample were the patients who underwent an operation of ORIF for Femoral Fractures without complications that met the inclusion and exclusion criteria. Cost analysis method used was the Activity-Based Costing (ABC).
 Results and Discussion: The unit cost of ORIF for Femoral Fractures which was calculated by the method of Activity Based Costing (ABC) was 8,157,726 rupiahs, while the real cost spent by the hospital was 9,035,885 rupiahs. The amount of real cost on ORIF for Femoral Fracture specified by RS PKU Muhammadiyah Bantul was higher than the unit cost calculated based on Activity-Based Costing method. The obtained difference was a positive difference, which was 878.159 rupiahs (10,76%). In the Activity-Based Costing (ABC), indirect and direct cost (overhead) are charged at some cost drivers, while the traditional system is only charged on a cost driver.
 Conclusions and Recommendations: Based on the calculation of unit costs obtained, it can be concluded that the unit cost of ORIF for Femoral Fracture in Central Surgery Installation PKU Muhammadiyah Hospital in Bantul with Activity- Based Costing method is lower and more proportionate with the activity. However, the hospital should also apply the calculation of the unit cost to all the types of service especially for surgery in order to identify and examine the cost components which are subject to change at any time and cause some disadvantages to the hospital accounting.

Residential Prosumer Energy Management System with Renewable Integration Considering Multi-Energy Storage and Demand Response

Rising energy demands, economic challenges, and the urgent need to address climate change have led to the emergence of a market wherein consumers can both purchase and sell electricity to the grid. This market leverages diverse energy sources and energy storage systems to achieve significant cost savings for consumers while providing critical grid support for utilities. In this study, an energy management system has been employed to tackle the optimization problem associated with various energy sources. This approach relies on mixed-integer linear programming (MILP) to optimize energy utilization while adhering to diverse constraints, yielding a feasible energy solution. This model is applied to real-world energy system consumption data and forecasts the most cost-effective day-ahead energy plans for different types of loads engaged in demand response. Furthermore, time-based charging and discharging strategies for electric vehicles and energy storage systems are considered, conducting a comprehensive analysis of energy costs across various storage devices. Our findings demonstrate that implementing this model can lead to an 18.26% reduction in operational costs when using lithium batteries and a remarkable 14.88% reduction with lead–acid batteries, particularly when integrating solar power and an EV into the system, while GHG is reduced by 36,018 grams/day for a load of 25 kW in one particular scenario. However, the analysis reveals that integrating wind power is not economically viable due to its comparatively higher operational costs.

Assessment of risk factors for nephrolithiasis in Rawat.

Objective: To identify the risk factors for nephrolithiasis among all patients found to have nephrolithiasis on renal sonography. The study will help us report the incidence of renal stones in this area, and as they cause significant morbidity and high costs of healthcare, and guide towards the introduction of public health measures to reduce the incidence of nephrolithiasis. Study Design: Descriptive, Questionnaire Based Cross-sectional study. Setting: Watim General Hospital, Rawalpindi. Period: Jul 1st, 2023 to Sep 30th, 2023. Material & Methods: The data of all adult patients who underwent renal sonography and were documented to have nephrolithiasis was obtained through physician administered questionnaires from the ultrasound department records. Data was then entered and analyzed by using the Statistical Package for the Social Sciences (SPSS), version 16. Results: Data was collected on 115 patients, of these, 57% were males. 48% of the patients were aged between 18-30 years of age. 54% of the patients belonged to lower socio-economic class. 55% of them had low water intake, and 81% consumed non-purified water. 51% patients also had a positive family history. 81% of the patients had less intake of citrus fruits and 37% of them never exercised. Conclusion: Many significant risk factors were assessed. Awareness campaigns and preventive health measures should be implemented addressing the critical risk factors. This will help decrease the burden on patients, and on the health care system.

The Effects of Body-Worn Cameras on Police Efficiency: A Study of Local Police Agencies in the United States

Research on body-worn cameras (BWCs) has burgeoned in recent years. However, much of this research has not examined the effects of this technology on police performance. In this study, we created a novel dataset on police organizational inputs and outputs and assessed the effects that BWCs have on police efficiency. Using a multiple-stage approach, in the first stage, we estimated the efficiency of local police agencies using a robust order- m efficiency model. In the second stage, we assessed the effects of BWCs using a range of matching estimators and an instrumental variable (IV) model. The first-stage results show that police agencies could improve their efficiency by 2% (from a mean efficiency score of 0.98 to 1). The second-stage matching and IV estimates suggest BWC effects on efficiency range between four and six percentage points in our preferred model. The introduction of this significant technological advance has brought many benefits to police agencies. Our findings show that BWCs can help improve police efficiency, yielding significant cost savings to law enforcement.