Expenditure Of Resources Research Articles

Development of an Attention Mechanism for Task-Adaptive Heterogeneous Robot Teaming

The allure of team scale and functional diversity has led to the promising adoption of heterogeneous multi-robot systems (HMRS) in complex, large-scale operations such as disaster search and rescue, site surveillance, and social security. These systems, which coordinate multiple robots of varying functions and quantities, face the significant challenge of accurately assembling robot teams that meet the dynamic needs of tasks with respect to size and functionality, all while maintaining minimal resource expenditure. This paper introduces a pioneering adaptive cooperation method named inner attention (innerATT), crafted to dynamically configure teams of heterogeneous robots in response to evolving task types and environmental conditions. The innerATT method is articulated through the integration of an innovative attention mechanism within a multi-agent actor–critic reinforcement learning framework, enabling the strategic analysis of robot capabilities to efficiently form teams that fulfill specific task demands. To demonstrate the efficacy of innerATT in facilitating cooperation, experimental scenarios encompassing variations in task type (“Single Task”, “Double Task”, and “Mixed Task”) and robot availability are constructed under the themes of “task variety” and “robot availability variety.” The findings affirm that innerATT significantly enhances flexible cooperation, diminishes resource usage, and bolsters robustness in task fulfillment.

Is alcohol and psychoactive medication use associated with excess hospital length-of-stay and admission frequency? A cross-sectional, observational study

BackgroundHospital length-of-stay and admission frequency are commonly used indicators of disease burden and health resource expenditures. However, the impact of psychoactive prescription medication use and harmful alcohol consumption on both the duration and frequency of hospital admissions is under-explored.MethodsWe conducted an analysis of data gathered from 2872 patients admitted to the Emergency Department at Lovisenberg Diaconal Hospital in Oslo, Norway. Psychoactive medicines (benzodiazepines, opioids, and z-hypnotics) were detected via liquid chromatography-mass spectrometry analysis of whole blood, while alcohol consumption was self-reported through the Alcohol Use Disorder Identification Test-4 (AUDIT-4). Using logistic regression, we examined associations with our primary outcomes, which were excess length-of-stay and admission frequency, defined as exceeding the sample median of 3.0 days and 0.2 admissions per year, respectively.ResultsCompared to the absence of psychoactive medication, and after adjusting for age, gender, malignant disease, pre-existing substance use disorder and admission due to intoxication, the detection of two or more psychoactive medicines was associated with both excess length-of-stay (odds ratio [OR], 1.60; 95% confidence interval [CI], 1.20 to 2.14) and yearly hospitalization rate (OR, 3.72; 95% CI, 2.64 to 5.23). This association persisted when increasing the definition for excess length-of-stay to 4 and 5 days and to 1.0 and 1.5 admissions per year for admission frequency. Harmful alcohol consumption (AUDIT-4 scores of 9 to 16) was not associated with excess length-of-stay, but with excess admission frequency when defined as more than 1.0 admission per year when compared to scores of 4 to 6 (OR, 2.68; 95% CI, 1.58 to 4.57).ConclusionsPsychoactive medication use is associated with both excess length-of-stay and increased antecedent admission frequency, while harmful alcohol consumption may be associated with the latter. The utility of our findings as a causal factor should be explored through intervention-based study designs.

Reinforcement Learning Based Metaheuristic Algorithm For Optimized Load Balancing In Cloud Environment

In cloud computing, workload balancing remains a difficult issue, as overburdened or underburdened servers are capable of causing issues with computation speed or even cause an entire network to collapse, subsequently this is a scenario that is never supposed to occur when using the cloud. Therefore, in order to avoid these issues, the system should be load balanced—that is, allocate job across each of the accessible computing assets by taking into account an acceptable schedule of utilization. The Virtual Machines are supposed to be used effectively, in response to the distributed load mechanism. In this work, a multifaceted load balancing optimization based on the Enhanced Firefly algorithm (EFA) with a Partial Order Markov Decision (POMD) process algorithm is proposed as an autonomous job allocation strategy in cloud computing. In order to overcome the limits of concurrent considerations, the suggested solution seeks to maximise VM productivity, streamline task allocation and the use of resources, and provide load balancing amongst virtual machines based on Time to Finish, expenditure, and consumption of resources. Using CloudSim, the performance study of the suggested approach was contrasted with the load balancing algorithms now in use in datasets like Google Cloud Jobs (GoCJ). According to the trial results, the suggested POMD-EFA strategy performed better than the other algorithms in terms of decreasing the Time to Finish, minimizing expenses, and increasing the efficiency in the application of resources.

Abstract LB139: Disparities in oral precancerous lesion surveillance in the United States upper Midwest

Abstract Objective: To determine whether geographic representation and race of patients in a regional oral cancer surveillance program (University of Minnesota Cancer Active Surveillance Population (UMN CASP)) mirror characteristics in the corresponding National Cancer Institute (NCI) designated comprehensive cancer center catchment. Introduction: Oral cancer poses a significant public health concern that may be mitigated through lesion surveillance. Representation of patients for clinical trials throughout an NCI cancer center catchment area is a crucial goal to promote improved health outcomes. This study investigates the demographic landscape of patients with preneoplastic oral lesions seen at the CASP. Methods: Through the UMN Clinical and Translational Sciences Institute, we queried 30 International Classification of Disease (ICD) 9 and 10 codes corresponding to 120 unique mucosal oral precancerous lesion descriptors of CASP patients. Automated extraction of electronic medical record (EMR) data for 4,496 patients treated by the senior author between 2011 and 2023 identified 1,375 patients with preneoplastic lesions. The proportion of patients residing in three-digit zip code regions of Minnesota were compared to 2021 American Community Survey state population data. Additionally, race data were compared between CASP patients and the UMN Masonic Cancer Center (MCC) patient catchment data. Results: CASP patients were over-represented relative to state population distributions within the Minneapolis-St. Paul metropolitan area and surrounding suburbs (region 554 p<0.0001 and region 551 p<0.0001). Under-representation of CASP patients was observed in all western and southern border regions (regions 559 p<0.0001, 560 p<0.0001, 561 p<0.0001, 562 p=0.0037, 565 p<0.0001, and 567 p=0.0006) as well as portions of central (563 p<0.0001), northern (566 p=0.0015), and north-eastern (558 p<0.0001) Minnesota. Proportional representation was observed in several regions of central (553 and 564), eastern (550), and north-eastern (557 and 556) Minnesota. Comparisons of CASP race data revealed a higher proportion of black patients (CASP 8.59% [95% CI 7.17-10.27%] vs. MCC 7.16% [95% CI 7.14-7.17%]) and a lower proportion of Asian/Pacific Islander patients (CASP 3.74% [95% CI 2.82-4.94%] vs. MCC 5.32% [95% CI 5.30-5.34%]) relative to MCC catchment patients. White, American Indian/Alaskan Native, and mixed-race groups were proportionally represented in both data sets. Conclusions: These data show that system wide EMR searches can be implemented to identify patients in an NCI cancer center catchment area with precancerous or other at-risk conditions for cancer. Importantly, Native patients were proportionally represented in the screening program while several rural locations were under-represented. This information can instruct the expenditure of additional resources for under-represented groups and permit streamlined enrollment of patients for cancer prevention clinical trials. Citation Format: Alexander J. Dwyer, Lindsey Mortensen, Beverly R. Wuertz, Frank G. Ondrey. Disparities in oral precancerous lesion surveillance in the United States upper Midwest [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB139.

Atypical case of anterograde amnesia after cerebral infarction and anti-NMDA encephalitis post Covid 19 infection: A complex clinical case

IntroductionOnly a few cases of primary anterograde amnesia with confabulation after severe complex brain damage have been described in the literature.ObjectivesTo describe a case of anterograde amnesia with confabulation in a patient with severe and extensive brain damage.Methodscase reportResultsCase presentation: A 48-year-old male patient with a medical history of diabetes mellitus type II, hypertension, presented to a psychiatric clinic for the first time. He was admitted to the hospital due to the manifestation of disruptive aggressive behaviour, aimless wandering, and excessive, impulsive expenditure of financial resources. At the time of hospitalization and during the hospital stay, the patient exhibited a state of elevated mood and anterograde amnesia compounded by the presence of prominent confabulation, easily irritable mood with a tendency to conflict. No physical limitations.Background: The patient is an active long-distance driver for 15 years. A year before hospitalization in psychiatric clinic, he was travelling to Moscow, he had episode of headache and unconsciousness after which hospitalization. Diagnosed with multiple infarcts of embolic origin in the right frontal lobes, both cortical and subcortical, on the right side at the level of the uncus, in the medial anterior parts of the right occipital lobe, on the left side in the insula and at the level of the capsula externa, in the anterior basal part of the left temporal lobe. After hemodynamic stabilization, he was repatriated to Latvia. Stationary positive SARS-CoV-2 PCR, O2 support therapy required. The patient develops auditory and visual hallucinations, which do not correct on antipsychotic therapy. Lumbar puncture was performed, which showed positive anti-NMDA antibodies, magnetic resonance - autoimmune limbic encephalitis with damage to the gyrus cinguli of the insula cortex of both temporal lobes and the right subfrontal part with spread throughout the right temporal lobe, bilaterally in the mediobasal structures of the temporal lobes and the right thalamus with progressive changes. The patient receives immunomodulatory therapy, plasma exchange and immune globulin. Hallucinations decrease on the background of therapy. At discharge - moderate ataxia in the legs, disorientation in time, severe short-term memory disorders.The patient in his mind lives like nothing has happened and the same life continues.Image:Image 2:ConclusionsThe long-term prognosis for the patient remains uncertain, given the multifaceted nature of the condition and the extent of brain damage. Continuous monitoring, rehabilitation, and ongoing support will be essential to assess cognitive recovery and improve the patient’s quality of life.Disclosure of InterestNone Declared

What Should Prescribers and Policy Makers Know About US Drug Importation?

Drug importation raises several ethical and safety concerns relevant to prescribers and policy makers considering costs and benefits of international medicine importation. This article suggests key points to consider, especially from a policy perspective, when weighing imported medicines' perceived affordability and accessibility against additional resource expenditure needed to assure sufficient regulatory oversight and equitable distribution and to mitigate potential risks of harm to patients.

The economics of deep and machine learning-based algorithms for COVID-19 prediction, detection, and diagnosis shaping the organizational management of hospitals

Research background: Deep and machine learning-based algorithms can assist in COVID-19 image-based medical diagnosis and symptom tracing, optimize intensive care unit admission, and use clinical data to determine patient prioritization and mortality risk, being pivotal in qualitative care provision, reducing medical errors, and increasing patient survival rates, thus diminishing the massive healthcare system burden in relation to severe COVID-19 inpatient stay duration, while increasing operational costs throughout the organizational management of hospitals. Data-driven financial and scenario-based contingency planning, predictive modelling tools, and risk pooling mechanisms should be deployed for additional medical equipment and unforeseen healthcare demand expenses. Purpose of the article: We show that deep and machine learning-based and clinical decision making systems can optimize patient survival likelihood and treatment outcomes with regard to susceptible, infected, and recovered individuals, performing accurate analyses by data modeling based on vital and clinical signs, surveillance data, and infection-related biomarkers, and furthering hospital facility optimization in terms of intensive care unit bed allocation. Methods: The review software systems employed for article screening and quality evaluation were: AMSTAR, AXIS, DistillerSR, Eppi-Reviewer, MMAT, PICO Portal, Rayyan, ROBIS, and SRDR. Findings & value added: Deep and machine learning-based clinical decision support tools can forecast COVID-19 spread, confirmed cases, and infection and mortality rates for data-driven appropriate treatment and resource allocations in effective therapeutic and diagnosis protocol development, by determining suitable measures and regulations and by using symptoms and comorbidities, vital signs, clinical and laboratory data and medical records across intensive care units, impacting the healthcare financing infrastructure. As a result of heightened use of personal protective equipment, hospital pharmacy and medication, outpatient treatment, and medical supplies, revenue loss and financial vulnerability occur, also due to expenses related to hiring additional staff and to critical resource expenditures. Hospital costs for COVID-19 medical care, screening, treatment capacity expansion, and personal protective equipment can lead to further financial losses while affecting COVID-19 frontline hospital workers and patients.

Striatal insights: a cellular and molecular perspective on repetitive behaviors in pathology.

Animals often behave repetitively and predictably. These repetitive behaviors can have a component that is learned and ingrained as habits, which can be evolutionarily advantageous as they reduce cognitive load and the expenditure of attentional resources. Repetitive behaviors can also be conscious and deliberate, and may occur in the absence of habit formation, typically when they are a feature of normal development in children, or neuropsychiatric disorders. They can be considered pathological when they interfere with social relationships and daily activities. For instance, people affected by obsessive-compulsive disorder, autism spectrum disorder, Huntington's disease and Gilles de la Tourette syndrome can display a wide range of symptoms like compulsive, stereotyped and ritualistic behaviors. The striatum nucleus of the basal ganglia is proposed to act as a master regulator of these repetitive behaviors through its circuit connections with sensorimotor, associative, and limbic areas of the cortex. However, the precise mechanisms within the striatum, detailing its compartmental organization, cellular specificity, and the intricacies of its downstream connections, remain an area of active research. In this review, we summarize evidence across multiple scales, including circuit-level, cellular, and molecular dimensions, to elucidate the striatal mechanisms underpinning repetitive behaviors and offer perspectives on the implicated disorders. We consider the close relationship between behavioral output and transcriptional changes, and thereby structural and circuit alterations, including those occurring through epigenetic processes.

Comparison of Electrocardiographic Findings Between Bodybuilding Athletes and Non-Athletes in Herat, Afghanistan

Regular physical exercise induces cardiac remodeling, known as the "athlete's heart," with both electrophysiological and structural changes. Distinguishing physiological from pathological Electrocardiogram (ECG) patterns in athletes is vital for optimal diagnosis and cost-effective healthcare. This study aimed to compare ECG findings between bodybuilder athletes and non-athletes in Herat City, Afghanistan. A prospective cross-sectional study was conducted on 140 male participants in Herat City, Afghanistan, from November 2014 to November 2016. It comprised two cohorts: 70 healthy bodybuilder athletes were taken as cases, and 70 age-matched males who were non-athletes and healthy individuals were taken as a control group. A non-probability convenience sampling technique was used to collect study participants. Data was gathered using a questionnaire-based approach from the respondents and analyzed using the Statistical Package for Social Sciences (SPSS) version 26. Athletes had a significantly higher prevalence of sinus arrhythmia (34.3% vs. 8.6%; p < 0.001), left ventricular hypertrophy (18.6% vs. 4.3%; p = 0.008), incomplete right bundle branch block (32.9% vs. 8.6%; p < 0.001), and T-wave inversions (8.6% vs. 0.0%; p = 0.012) compared to control groups. Notably, the overall ECG interpretation differed significantly (p < 0.001), with athletes exhibiting a higher frequency of abnormal findings. However, no statistically significant differences were observed in heart rate, PR interval, left atrial enlargement, QRS axis, QTc interval, ST segment, U wave, premature atrial contractions, and premature ventricular contractions. These findings indicate that athlete's heart adaptations mainly affect rhythm and repolarization on ECG. Accurately distinguishing between physiological and pathological ECG findings is crucial in sports cardiology. This discernment is key for precise diagnosis and targeted treatment, leading to optimal patient outcomes and minimizing unnecessary resource expenditure.

Continuous and Non-Invasive Lactate Monitoring Techniques in Critical Care Patients.

Lactate, once merely regarded as an indicator of tissue hypoxia and muscular fatigue, has now gained prominence as a pivotal biomarker across various medical disciplines. Recent research has unveiled its critical role as a high-value prognostic marker in critical care medicine. The current practice of lactate detection involves periodic blood sampling. This approach is invasive and confined to measurements at six-hour intervals, leading to resource expenditure, time consumption, and patient discomfort. This review addresses non-invasive sensors that enable continuous monitoring of lactate in critical care patients. After the introduction, it discusses the iontophoresis system, followed by a description of the structural materials that are universally employed to create an interface between the integumentary system and the sensor. Subsequently, each method is detailed according to its physical principle, outlining its advantages, limitations, and pertinent aspects. The study concludes with a discussion and conclusions, aiming at the design of an intelligent sensor (Internet of Medical Things or IoMT) to facilitate continuous lactate monitoring and enhance the clinical decision-making support system in critical care medicine.

Effect of a Co-Located Bridging Recovery Initiative on Hospital Length of Stay Among Patients With Opioid Use Disorder

Co-located bridge clinics aim to facilitate a timely transition to outpatient care for inpatients with opioid use disorder (OUD); however, their effect on hospital length of stay (LOS) and postdischarge outcomes remains unclear. To evaluate the effect of a co-located bridge clinic on hospital LOS among inpatients with OUD. This parallel-group randomized clinical trial recruited 335 adult inpatients with OUD seen by an addiction consultation service and without an existing outpatient clinician to provide medication for OUD (MOUD) between November 25, 2019, and September 28, 2021, at a tertiary care hospital affiliated with a large academic medical center and its bridge clinic. The bridge clinic included enhanced case management before and after hospital discharge, MOUD prescription, and referral to a co-located bridge clinic. Usual care included MOUD prescription and referrals to community health care professionals who provided MOUD. The primary outcome was the index admission LOS. Secondary outcomes, assessed at 16 weeks, were linkage to health care professionals who provided MOUD, MOUD refills, same-center emergency department (ED) and hospital use, recurrent opioid use, quality of life (measured by the Schwartz Outcome Scale-10), overdose, mortality, and cost. Analysis was performed on an intent-to-treat basis. Of 335 participants recruited (167 randomized to the bridge clinic and 168 to usual care), the median age was 38.0 years (IQR, 31.9-45.7 years), and 194 (57.9%) were male. The median LOS did not differ between arms (adjusted odds ratio [AOR], 0.94 [95% CI, 0.65-1.37]; P = .74). At the 16-week follow-up, participants referred to the bridge clinic had fewer hospital-free days (AOR, 0.54 [95% CI, 0.32-0.92]), more readmissions (AOR, 2.17 [95% CI, 1.25-3.76]), and higher care costs (AOR, 2.25 [95% CI, 1.51-3.35]), with no differences in ED visits (AOR, 1.15 [95% CI, 0.68-1.94]) or deaths (AOR, 0.48 [95% CI, 0.08-2.72]) compared with those receiving usual care. Follow-up calls were completed for 88 participants (26.3%). Participants referred to the bridge clinic were more likely to receive linkage to health care professionals who provided MOUD (AOR, 2.37 [95% CI, 1.32-4.26]) and have more MOUD refills (AOR, 6.17 [95% CI, 3.69-10.30]) and less likely to experience an overdose (AOR, 0.11 [95% CI, 0.03-0.41]). This randomized clinical trial found that among inpatients with OUD, bridge clinic referrals did not improve hospital LOS. Referrals may improve outpatient metrics but with higher resource use and expenditure. Bending the cost curve may require broader community and regional partnerships. ClinicalTrials.gov Identifier: NCT04084392.

Catheter ablation for supraventricular tachycardia and health resource utilization and expenditures: A propensity-matched cohort study

BackgroundFew data are available regarding temporal patterns of health resource utilization (HRU) and expenditures among patients undergoing catheter ablation for paroxysmal supraventricular tachycardia (PSVT). This study aimed to describe expenditures and HRU in patients with PSVT who underwent catheter ablation compared to a matched cohort of patients on medical therapy alone. MethodsUsing a large US administrative database, we identified adult patients (age 18 to 65 years) with a new PSVT diagnosis between 2008 and 2016. Propensity-score matching was used to assemble a PSVT cohort treated with ablation or medical therapy alone (N = 2556). Longitudinal trends in HRU and expenditures in the 3-years preceding and following PSVT diagnosis were compared. ResultsThere were no significant differences in expenditures between groups except within the first year after PSVT diagnosis: $48,004 ablation vs. $17,560 medical therapy (p < 0.001). This difference was driven by procedural expenditures, where the mean cost of catheter ablation was $32,057 ± SD 26,737. In Years 2 and 3 post-ablation, HRU and expenditures decreased to the levels associated with the medical therapy group, although fewer ablation patients required any prescription for beta-blockers, calcium channel blockers, or anti-arrhythmic drugs (32% ablation vs. 42% medical therapy group, p < 0.001). ConclusionCatheter ablation reduces medication burden in PSVT, yet health resource use and expenditures were similar beyond 2 years post-ablation when compared to PSVT patients on medical therapy alone. Additional studies are required to better understand drivers of these sustained health expenditures, and barriers to achieving cost-savings for a potentially curative procedure.

Considering collaborative incident management: A study of the COVID-19 response and initial recovery in a rural West Virginia community.

Hierarchical command-style structures are commonplace in the management of disasters, though researchers have begun to recommend the exploration of networked approaches to incident management. Furthermore, few studies are explicitly looking at the rural context of disaster management. This study seeks to contribute to both topics by examining the motivations for emergent collaboration in the direction of the response and initial recovery to the coronavirus disease 2019 pandemic in a rural West Virginia community. Between March 2020 and March 2022, the primary investigator moderated regular briefings of a community task force convened to coordinate the pandemic response and recovery. As regular operations concluded, members completed a survey, and 10 randomly selected members participated in semistructured interviews regarding their experiences in the task force. Survey responses suggest that common motivations for collaboration in nondisaster contexts (as they appear in the scholarly literature) and potential benefits of networked approaches highlighted in the incident management literature could also serve as motivators within the disaster context. Qualitative interview data extend that discussion and identify the need to gain clear information regarding the concerned authorities and specific local information to better inform the expenditure of limited resources as two additional motivators for collaboration.

Enhancement Of A 32-By-32-Bit Signed Digital Multiplier: A Multi-Dimensional Optimization Approach

In the pursuit of enhanced performance in a 32-by-32-bit signed digital multiplier, optimizations were enacted on three critical fronts. Initial efforts focused on the integration of an advanced Booth encoding technique for the generation of three-bit control signals, meticulously crafted in the Verilog programming language. This approach, characterized by its restricted symbol expansion methodology, strategically curtails hardware resource expenditures, and diminishes power utilization during computational tasks. Subsequently, a sophisticated hybrid Wallace tree architecture was employed, offering a notable decrement in the delay of two XOR gates in contrast to the exclusive application of 3-2 and 4-2 compressors. This refinement not only augments the aggregation efficacy of partial products but also substantially accelerates operational velocity. Furthermore, the incorporation of a 64-bit carry-lookahead adder was instrumental in mitigating the latency induced by lower-order carry anticipation. To ascertain the multiplier's computational precision, a Verilog-constructed testbench file facilitated the assembly of 100,000 test vectors, with simulations executed in ModelSim yielding a verifiable accuracy rate of 100%.

Predictive mobility and cost-aware flow placement in SDN-based IoT networks: a Q-learning approach

Software-Defined Networking (SDN) has emerged as an innovative networking method that offers effective management and remarkable flexibility. However, current SDN-based solutions primarily focus on static networks or concentrate on backbone networks, where network dynamics have minimal impact. The existing methods for placing flow entries in Software-Defined Networking (SDN)-based Internet of Things (IoT) systems exhibit shortcomings in accurately predicting outcomes and efficiently reducing table misses and associated performance metrics. This research introduces a new approach, specifically the mobility-aware adaptive flow entry placement scheme for SDN-based Internet of Things (IoT) environments, to address the mobility aspect of networks. The proposed scheme utilizes the Q-learning algorithm to predict the next possible location of end devices, while the cost-sensitive AdaBoost algorithm is employed to select heavy and active flows. As a result, efficient flow rules for incoming flows can be dynamically implemented without controller intervention. Extensive computer simulations demonstrate that this approach significantly enhances match probability and prediction accuracy while concurrently reducing the number of table misses and resource expenditure compared to existing schemes.

Bad Feelings, Best Explanations: In Defence of the Propitiousness Theory of the Low Mood System

AbstractThere are three main accounts of the proper function of the low mood system (LMS): the social risk theory, the disease theory, and the propitiousness theory. Adjudicating between these accounts has proven difficult, as there is little agreement in the literature about what a theory of the LMS’s proper function is supposed to explain. In this article, drawing upon influential work on the evolution of other affective systems, such as the disgust system and the fear system, I argue that a theory of the proper function of the low mood system should: (i) account for the reliable, distal causes and effects of the system’s activation, and (ii) explain how having a system that performed such a function increased fitness in ancestral environments. On this basis, I show that the proper function of the low mood system is to limit resource expenditure in relatively unpropitious circumstances, exactly as hypothesised by the propitiousness theory.

Countering terrorist propaganda: Competitive resource allocation to communities

Increased terrorist propaganda has caused greater violence worldwide. Communities are efficient channels through which terrorist organizations can access target audiences; thus, communities are playing larger roles in terrorist propaganda distribution. However, no previous studies have examined the interactions between terrorists and governments regarding terrorist propaganda in communities from a game-theoretic lens. This article aims to fill this research gap. We first classify the relationships between the communities as independent or interactive. For both cases, we describe the process of terrorist propaganda distribution in communities, highlighting the dependence of the degree of terrorist ideology within a community on the terrorist-organization and government resource allocations; based on this, we build novel sequential game models of the terrorist and the government and obtain the equilibrium solutions. Furthermore, we conduct a comparative study to elucidate the differences in the expected government losses for independent and interactive communities. The outcomes are as follows: (i) the higher the community propagation centrality, the greater the resource expenditure of the government and terrorist organization, and (ii) introducing an interaction network among communities decreases the expected government loss when the network directly destroys the distribution uniformity of the community propagation centralities. Finally, we present a case study to illustrate the usability of the proposed models. This study provides novel insights for governments targeting optimal allocation of intervention resources to communities so as to counter terrorist propaganda distributed by a strategic terrorist.

Data-Tracking in Blockchain Utilizing Hash Chain: A Study of Structured and Adaptive Process

This study presents a series of structured and adaptive processes aimed at tracking and verifying transactions recorded on the blockchain. Permissioned blockchains are employed across diverse enterprises for various purposes, including data recording, management, the utilization of blockchain services, and authentication. However, the processes of data tracking and transactions incur substantial resource and time expenditure. Furthermore, there is potential for information asymmetry within the blockchain ledger due to data breach attacks. Consequently, we propose a contract structured as a hash chain to mitigate resource and time consumption in the tracking and verification processes by organizing transaction hash values and content in a hash chain format based on cryptography. We generate a hash chain for the recorded transactions along the process line and expedite the tracking and verification process by navigating the relevant hash chain. This approach achieves faster and more accurate tracking procedures compared to conventional transaction tracking processes, simultaneously maintaining data symmetry within the blockchain ledger. We conduct a comparative analysis of a contract-based hash-chain-employing structure and two contracts related to tracking in terms of tracking time, CPU usage, and network traffic, among other metrics. The findings suggest that structuring transaction data in the form of a hash chain significantly enhances the efficiency and integrity of the data-tracking and verification processes. Consequently, in this study, we advocate for the adoption of contracts based on the hash chain format when leveraging the blockchain for tracking and verification purposes across various institutions.

Primary Care Support Tools for Digestive Health Care: A Mixed Method Study.

To address the increasing demands for gastroenterology specialty care and increasing wait times, centralized access and triage (CAT) systems, telephone support, and clinical care pathways were implemented to streamline referrals and support management of low-risk gastrointestinal (GI) conditions in the primary care medical home. This study aimed to understand primary care providers (PCPs) and GI specialists' perceptions of these supports, factors that affect support implementation and identify barriers and facilitators for implementing supports from both PCP and GI specialists' perspectives. We conducted a mixed method study including surveys and interviews with PCPs and GI specialists. Online surveys and semistructured qualitative interviews were conducted from July 2022 to September 2022. All interviews were transcribed and coded to perform a thematic analysis. Survey data were analyzed in SPSS version 25. Descriptive statistics were employed to summarize and describe the data collected. Inferential statistics were used to identify associations and relationships within the dataset. T-test and chi-square tests were applied at 95% confidence level, with a p value <0.05 (two-sided) considered statistically significant. A total of 36 PCPs responded to the survey. Most respondents were working full-time (73.5%, n = 25) and were female (73.5%, n = 25). Overall, 42% used the pathways regularly, 48% (n = 16) used them occasionally, and very few (9.1%, n = 3) said they were aware but had not used pathways. Overall, PCPs were satisfied with CAT processes and the use of primary care pathways, recognizing the importance of fair and equitable access to specialty care. Specific processes in CAT for vulnerable populations and patients using walk-in clinics were recognized as a limitation, given the lack of ease in completing the required testing and follow-up needed when utilizing the care pathway. Of the 112 GI specialists who received the survey, 28 (25%) completed it, with males (50.0%, n = 14) and females (39.2%, n = 11), remainder no response. Most participate in CAT (73.9%, n = 17) and were remunerated by an alternative relationship plan (ARP) (53.6%, n = 15). Overall, GIs were satisfied with central triaging and primary care pathways, reducing unnecessary time and resource expenditure for referrals. There were statistically significant differences in perceptions among fee for service and alternative relationship plan GI specialists regarding the effectiveness of CAT in improving access and use of health system resources. Overall, PCPs and GI specialists believe utilizing CAT and primary care pathways improves referral quality, reduces resource expenditure, and provides fair and equitable access to GI specialty services. Improvement in CAT processes with improved pathway awareness may reduce unnecessary referrals.

Effects of parliamentary amendments on municipal financing of primary health care in the Brazilian Unified National Health System

This study aims to analyze the effects of the expansion of the federal transfer of parliamentary amendments for municipal financing of primary health care (PHC) in the Brazilian Unified National Health System (SUS), from 2015 to 2020. A longitudinal study was conducted using secondary data on transfers of parliamentary amendments from the Brazilian Ministry of Health and expenditure of municipalities' own resources on public health actions and services and PHC. The effect of the transfer of parliamentary amendments on municipal financing was verified in a stratified way by population size of the municipalities, using generalized estimating equation models. The transfer of parliamentary amendments for PHC showed a large discrepancy in per capita values among municipalities of different population sizes. No correlation with municipal spending on public health actions and services was observed in municipalities with more than 10,000 inhabitants, and the association with spending on PHC (p < 0.050) was inverse in all municipalities. Therefore, the increase in the transfer of parliamentary amendments by the Brazilian Ministry of Health favored a reduction in the allocation of municipal revenues to PHC, which may have been directed to other spending purposes in the SUS. These changes seem to represent priorities established for municipal budget expenditure, which have repercussions on local conditions for guaranteeing stable funding for PHC in Brazil.