Search Results (105)

Optimizing ambulance deployment is a critical task in emergency medical services (EMS), as it directly affects patient outcomes and system efficiency. This study proposes a cyber-secure, machine learning-based framework for predicting region-specific ambulance allocation and response times across South Korea. The model integrates heterogeneous datasets—including demographic profiles, transportation indices, medical infrastructure, and dispatch records from 229 EMS centers—and incorporates real-time IoT streams such as traffic flow and geolocation data to enhance temporal responsiveness. Supervised regression algorithms—Random Forest, XGBoost, and LightGBM—were trained on 2061 center-month observations. Among these, Random Forest achieved the best balance of accuracy and interpretability (MSE = 0.05, RMSE = 0.224). Feature importance analysis revealed that monthly patient transfers, dispatch variability, and high-acuity case frequencies were the most influential predictors, underscoring the temporal and contextual complexity of EMS demand. To support policy decisions, a Lasso-based simulation tool was developed, enabling dynamic scenario testing for optimal ambulance counts and dispatch time estimates. The model also incorporates the coefficient of variation (CV) of workload intensity as a performance metric to guide long-term capacity planning and equity assessment. All components operate within a cyber-secure architecture that ensures end-to-end encryption of sensitive EMS and IoT data, maintaining compliance with privacy regulations such as GDPR and HIPAA. By integrating predictive analytics, real-time data, and operational simulation within a secure framework, this study offers a scalable and resilient solution for data-driven EMS resource planning. Full article

Background: Glioblastoma (GBM) resists current therapies due to its rapid proliferation, diffuse invasion, and heterogeneous cell populations. We previously showed that a single cold atmospheric plasma discharge tube (DT) reduces GBM viability via broad-spectrum electromagnetic (EM) emissions. Here, we tested whether two DTs arranged in a helmet configuration could generate overlapping EM fields to amplify the anti-tumor effects without thermal injury. Methods: The physical outputs of the single- and dual-DT setups were characterized by infrared thermography, broadband EM field probes, and oscilloscope analysis. Human U87-MG cells were exposed under the single or dual configurations. The viability was quantified with WST-8 assays mapped across 96-well plates; the intracellular reactive oxygen species (ROS), membrane integrity, apoptosis, and mitochondrial potential were assessed by multiparametric flow cytometry. Our additivity models compared the predicted versus observed dual-DT cytotoxicity. Results: The dual-DT operation produced constructive EM interference, elevating electric and magnetic field amplitudes over a broader area than either tube alone, while temperatures remained <39 °C. The single-DT exposure lowered the cell viability by ~40%; the dual-DT treatment reduced the viability by ~60%, exceeding the additive predictions. The regions of greatest cytotoxicity co-localized with the zones of highest EM field overlap. The dual-DT exposure doubled the intracellular ROS compared with single-DT and Annexin V positivity, confirming oxidative stress-driven cell death. The out-of-phase operation of the discharge tubes enabled the localized control of the treatment regions, which can guide future treatment planning. Conclusions: Two synchronously operated plasma discharge tubes synergistically enhanced GBM cell killing through non-thermal mechanisms that coupled intensified overlapping EM fields with elevated oxidative stress. This positions modular multi-DT arrays as a potential non-invasive adjunct or alternative to existing electric-field-based therapies for glioblastoma. Full article

Organizations worldwide are moving towards sustainability in the supply chains (SCs). Ergonomics management (EM) in SCs can contribute to their social sustainability (SS) by providing a fair, safe, and healthy environment. The literature recognizes the lack of an ergonomics management evaluation model (EMEM) for SCs contributing to SS. This research aims to propose an EMEM applicable to SCs. A continuous improvement approach with five constructs: Plan, Do, Check, Act, and Leadership and Worker participation (L&WP) was conducted, including nineteen domains, and the axiomatic design methodology was deployed. Design ranges (DRs) were defined by 34 experts from Latin America. System ranges (SRs) were assessed by self-assessments of EM practices to obtain the information content axiom in one case study of the Mexican salt industry. A new ergonomics management index for the supply chain (EMISC) and a corresponding scale were implemented. According to this scale, the index was found to be low, indicating a poor ergonomics management index (EMI) for the supplier link across the nineteen domains. The proposed EMEM effectively obtains an EMI of the supply chain (SC) by link and entirely. The model identifies opportunities to improve ergonomics practices for companies participating in sustainable supply chains (SSC). Full article

Background: Professional knowledge and experience of healthcare organization went through continuous change and development with the progression of COVID-19 pandemic waves. However, carefully developed guidelines for cardiopulmonary resuscitation (CPR) remained largely unchanged regardless of the epidemic situation, with the largest change being a more prominent bioethical approach. It would be possible to further improve the quality of CPR by systematic data collection, the facilitation of prospective studies, and further development of the methodology based on this evidence, as well as by providing information and developing provisions on interventions with expected poor outcomes, and ultimately by refusing resuscitation. Methods: This study involved the critical collection and analysis of literary data originating from the Web of Science and PubMed databases concerning bioethical aspects and the efficacy of CPR during the COVID-19 pandemic. Results: According to the current professional recommendation of the European Resuscitation Council (ERC), CPR should be initiated immediately in case of cardiac arrest in the absence of an exclusionary circumstance. One such circumstance is explicit refusal of CPR by a well-informed patient, which in practice takes the form of a prior declaration. ERC prescribes the following conjunctive conditions for do-not-attempt CPR (DNACPR) declarations: present, real, and applicable. It is recommended to take the declaration as a part of complex end-of-life planning, with the corresponding documentation available in an electronic database. The pandemic has brought significant changes in resuscitation practice at both lay and professional levels as well. Incidence of out-of-hospital resuscitation (OHCA) did not differ compared to the previous period, while cardiac deaths in public places almost halved during the epidemic (p < 0.001) as did the use of AEDs (p = 0.037). The number of resuscitations performed by bystanders and by the emergency medical service (EMS) also showed a significant decrease (p = 0.001), and the most important interventions (defibrillation, first adrenaline time) suffered a significant delay. Secondary survival until hospital discharge thus decreased by 50% during the pandemic period. Conclusions: The COVID-19 pandemic provided a significant impetus to the revision of guidelines. While detailed methodology has changed only slightly compared to the previous procedures, the DNACPR declaration regarding self-determination is mentioned in the context of complex end-of-life planning. The issue of safe environment has come to the fore for both lay and trained resuscitators. Future Directions: Prospective evaluation of standardized methods can further improve the patient’s autonomy and quality of life. Since clinical data are controversial, further prospective controlled studies are needed to evaluate the real hazards of aerosol-generating procedures. Full article

Background: Dental radiographs play a crucial role in diagnosis and treatment planning. With the rise in digital imaging, there is growing interest in leveraging artificial intelligence (AI) to support clinical decision-making. AI technologies can enhance diagnostic accuracy by automating tasks like identifying and locating dental structures. The aim of the current study was to assess and validate the accuracy of an AI-powered application in the detection and numbering of primary teeth on panoramic radiographs. Methods: This study examined 598 archived panoramic radiographs of subjects aged 4–14 years old. Images with poor diagnostic quality were excluded. Three experienced clinicians independently assessed each image to establish the ground truth for primary teeth identification. The same radiographs were then evaluated using EM2AI, an AI-based diagnostic software for the automatic detection and numbering of primary teeth. The AI’s performance was assessed by comparing its output to the ground truth using sensitivity, specificity, predictive values, accuracy, and the Kappa coefficient. Results: EM2AI demonstrated high overall performance in detecting and numbering primary teeth in mixed dentition, with an accuracy of 0.98, a sensitivity of 0.97, a specificity of 0.99, and a Kappa coefficient of 0.96. Detection accuracy for individual teeth ranged from 0.96 to 0.99. The highest sensitivity (0.99) was observed in detecting upper right canines and primary molars, while the lowest sensitivity (0.79–0.85) occurred in detecting lower incisors and the upper left first molar. Conclusions: The AI module demonstrated high accuracy in the automatic detection of primary teeth presence and numbering in panoramic images, with performance metrics exceeding 90%. With further validation, such systems could support automated dental charting, improve electronic dental records, and aid clinical decision-making. Full article

To address energy management challenges for intelligent connected automated range-extended electric vehicles under vehicle-road cooperative environments, a hierarchical energy management strategy (EMS) based on speed prediction is proposed from the perspective of multi-objective optimization (MOO), with comprehensive system performance being significantly enhanced. Focusing on connected car-following scenarios, acceleration sequence prediction is performed based on Kalman filtering and preceding vehicle acceleration. A dual-layer optimization strategy is subsequently developed: in the upper layer, optimal speed curves are planned based on road network topology and preceding vehicle trajectories, while in the lower layer, coordinated multi-power source allocation is achieved through EMS_MPC-P, a Bayesian-optimized model predictive EMS based on Pontryagin’ s minimum principle (PMP). A MOO model is ultimately formulated to enhance comprehensive system performance. Simulation and bench test results demonstrate that with SoC₀ = 0.4, 7.69% and 5.13% improvement in fuel economy is achieved by EMS_MPC-P compared to the charge depleting-charge sustaining (CD-CS) method and the charge depleting-blend (CD-Blend) method. Travel time reductions of 62.2% and 58.7% are observed versus CD-CS and CD-Blend. Battery lifespan degradation is mitigated by 16.18% and 5.89% relative to CD-CS and CD-Blend, demonstrating the method’s marked advantages in improving traffic efficiency, safety, battery life maintenance, and fuel economy. This study not only establishes a technical paradigm with theoretical depth and engineering applicability for EMS, but also quantitatively reveals intrinsic mechanisms underlying long-term prediction accuracy enhancement through data analysis, providing critical guidance for future vehicle–road–cloud collaborative system development. Full article

Natural disasters and emergencies continue to increase in frequency and severity worldwide, necessitating robust emergency management (EM) systems and evaluation methodologies. This study addresses critical gaps in current emergency response capacity (ERC) evaluation frameworks by developing a comprehensive quantitative decision-making model to assess ERC more effectively. This research constructs a systematic ERC assessment framework based on the four phases of the disaster management cycle (DMC): prevention, preparedness, response, and recovery. The methodology employs multi-criteria decision analysis to evaluate ERC using three distinct information representation environments: intuitionistic fuzzy (IF) sets, linguistic variables (LV), and a novel mixed IF-LV environment. For each environment, we derive appropriate aggregation operators, weight determination methods, and information fusion mechanisms. The proposed model was empirically validated through a case application to emergency plan selection in Shenzhen, China. A statistical analysis of results demonstrates high consistency across all three decision environments (IF, LV, and mixed IF-LV), confirming the model’s robustness and reliability. A sensitivity analysis of key parameters further validates the model’s stability. Results indicate that the proposed decision-making approach provides significant value for EM by enabling more objective, comprehensive, and flexible ERC assessment. The indicator system and evaluation methodology offer decision-makers (DMs) tools to quantitatively analyze ERC using various information expressions, accommodating both subjective judgments and objective metrics. This framework represents an important advancement in emergency preparedness assessment, supporting more informed decision-making in emergency planning and response capabilities. Full article

Background/Objectives: Emergency Medical Services (EMS) clinicians in the US have high COVID-19 vaccine hesitancy rates and often do not receive primary vaccinations or subsequent boosters. The extent of booster attrition following initial vaccination and first booster dose in EMS clinicians is unknown. Our objective was to evaluate the prevalence and drivers of COVID-19 booster attrition in EMS clinicians. We hypothesized that booster attrition is common among EMS clinicians and associated with various EMS characteristics. Methods: This study was a cross-sectional analysis of nationally certified civilian EMS clinicians aged 18–85 years old. An electronic survey was distributed, which included an evaluation of vaccination status, booster acceptance, willingness to receive future boosters, perceived risk of contracting COVID-19 from the Understanding America Survey (8 items), and mistrust of healthcare organizations using the Medical Mistrust Index (MMI) (7 items). These data were combined with demographic and work-related characteristics from the National Registry of EMTs dataset. A multivariable logistic regression model (OR, 95% CI) was used to describe booster attrition associations between demographics, work-related characteristics, perceived risk, and medical mistrust. Results: A total of 1902 respondents met initial inclusion criteria. Within this group, 78% were COVID-19 vaccinated, and an additional 65% received a booster. Of these, 37% reported not planning to receive any other booster treatments following the first booster. Primary reasons for not continuing with subsequent boosters include confusion among experts on efficacy (59%), severe side effects (38%), the belief that COVID-19 is not a threat (26%), the belief in natural immunity (25%), and the belief that boosters are not required (23%). Odds of planning to receive another booster increased with receiving a flu vaccine (5.03, 3.08–8.22) and urban environment (1.96, 1.19–3.24, referent rural). In comparison, the odds of planning to receive another booster were lower for paramedics (0.56, 0.38–0.83, referent EMT) and fire agencies (0.53, 0.31–0.89, referent hospital). As the perceived risk of COVID-19 and medical mistrust decrease, the odds of planning to receive another booster increase (perceived risk: 1.98, 1.41–2.78; trust: 4.12, 3.21–5.28). Conclusions: The rate of booster attrition following receipt of one booster is high, at 37%. While there are associations with EMS demographic and workforce characteristics, further exploration is necessary to define the drivers and potential consequences of high booster attrition in the EMS clinician community. Full article

Emergency medical services (EMSs) are critical to reducing fatalities and improving patient outcomes in emergencies such as traffic accidents, where response time is a decisive factor. This study proposes a comprehensive and systematic approach to designing and optimizing EMS systems tailored for urban traffic accidents. By integrating Geographic Information Systems (GISs), hypercube queuing models, Economic Value Added (EVA) calculations, and multi-criteria decision-making (MCDM) techniques, we developed a model that balances service efficiency, financial sustainability, and equitable access to emergency care. The hypercube queuing model was applied to estimate key performance metrics, such as response time, coverage, and the GINI index for equity, under varying numbers of ambulances and demand scenarios. In addition, EVA was calculated for different configurations of leased and owned ambulances, offering a financial perspective to assess the viability of public–private partnerships (PPPs) in EMSs. Using the fuzzy Analytic Hierarchy Process (AHP) and CoCoSo (Combined Compromise Solution) methods, this study identified the optimal number of ambulances required to minimize response time, maximize coverage, and ensure financial sustainability. The proposed approach has been applied to a real case in Colombia. Furthermore, integrating leased ambulances offers a financially viable solution with positive EVA values that guarantee the long-term sustainability of the public–private partnership. This paper advances the literature by providing a practical framework for optimizing EMS systems, particularly in developing countries where financial constraints and resource limitations represent significant challenges. The proposed methodology improves service efficiency and economic sustainability and ensures equity in access to life-saving care. Full article

Fuel cell hybrid vehicles offer significant potential in heavy-duty transportation due to their high efficiency, extended range, and zero emissions, making them a key enabler of sustainable transportation. To enhance the energy consumption economy and lifecycle economy of fuel cell hybrid mining trucks (FCHMTs) while reducing total operating costs and promoting environmental sustainability, this paper proposes an approximate globally optimal energy management strategy (EMS) based on a rule-interposing balance cost minimization strategy (AGO-BCMS). First, an FCHMT power system model is established, including degradation models for the fuel cell and battery. Then, the global optimality of dynamic programming (DP) is utilized to extract the fuel cell output characteristics under different battery states and vehicle power demands. Subsequently, optimal rules are designed and embedded into the cost minimization optimization model to plan the fuel cell output range under actual driving conditions. Simultaneously, dynamic threshold updates are performed based on vehicle driving condition phase recognition. Finally, energy distribution optimization is calculated using sequential quadratic programming (SQP). This strategy not only improves the economic viability of FCHMTs but also contributes to the broader goals of advancing sustainable transportation solutions. The proposed strategy was validated under both single round-trip and continuous operational conditions. Simulation results show that, under single round-trip conditions, the proposed strategy reduces the total operational cost by 3.13%, 4.09%, and 10.90% compared to balance cost-minimization strategies (BCMS), equivalent consumption minimization strategy (ECMS), and rule-based strategies, respectively. Under continuous operational conditions, the total cost is reduced by 3.61%, 6.63%, and 15.90%, respectively. Full article

With the rapid aging of the population and increasing demand for elderly care services, ensuring equitable access to emergency medical service (EMS) for nursing homes has become a critical public health challenge. As the first Chinese city to experience an aging society, Shanghai faces compounding pressures from rapid urbanization and recurrent urban flooding, both of which exacerbate disparities in healthcare accessibility. This study investigates the spatial equity of EMS access among nursing homes in Shanghai, with a particular focus on the impacts of urban flooding. Using ordinary least squares and geographically weighted regression models, the study reveals that EMS accessibility is relatively equitable under normal conditions but deteriorates significantly during flood events, particularly in suburban and low-lying areas. The findings show that flood-induced disruptions to road networks disproportionately impact nursing homes in peripheral districts, widening accessibility gaps. Additionally, the study identifies that factors such as road density, emergency center distribution, and flood inundation depth play critical roles in shaping spatial equity. The results underscore the need for strategic interventions to enhance healthcare resilience, including optimized facility allocation and flood-resistant infrastructure. Policymakers should adopt integrated planning approaches to ensure equitable EMS access for vulnerable elderly populations during emergencies. Full article

The layout of emergency medical services (EMS) is of vital importance. A well-planned layout significantly impacts the timeliness of response and operational efficiency, which are crucial for saving lives and mitigating injury severity. This paper presents a novel decision support framework for optimizing EMS station layout. Employing the k-means clustering algorithm in combination with the elbow method and silhouette coefficient method, we conduct a clustering analysis on a patient call record dataset. Comprising 166,161 emergency center call records in the Shanghai area over one year, this dataset serves as the basis for our analysis. The analysis results are applied to determine EMS station locations, with the average ambulance patient pickup time as the evaluation criterion. A simulation model is utilized to validate the effectiveness and reliability of the decision-making framework. An experimental analysis reveals that compared with the existing EMS station layout, the proposed framework reduces the average patient pickup time from 11.033 min to 9.661 min, marking a 12.441% decrease. Furthermore, a robustness test of the proposed scheme is carried out. The results indicate that even when some first-aid sites fail, the average response time can still be effectively controlled within 9.9 min. Through this robustness analysis, the effectiveness and reliability of the decision framework are demonstrated, offering more efficient and reliable support for the EMS system. Full article

Background and Objectives: Management of second-trimester placenta accreta spectrum (PAS) is currently center-dependent with minimal evidence-based practices. This study aims to analyze outcomes of hysterectomy as second-trimester active management (AM) versus cesarean hysterectomy as expectant management (EM) in cases of PAS with intraoperative and postoperative outcomes. Materials and Methods: This study is a retrospective case-control study of patients with a pathology-confirmed diagnosis of PAS managed at a single center over 16 years (2005–2020). All cases were diagnosed during the first or second trimester by ultrasonography and managed by the same multidisciplinary team with delivery within the second trimester. Results: Thirty-four patients with PAS were diagnosed and delivered by the second trimester. Of these, (41.1%) elected for active management and 20 (58.9%) for expectant management but ultimately required delivery prior to 28 weeks’ gestation. Baseline demographics were similar between groups. Intraoperatively, no differences were noted in operative time (191.5 vs. 203 min, p = 0.85), blood loss (2300 vs. 2600 cc, p = 0.85), or incidental cystotomy (1 vs. 7, p = 0.10). Postoperative length of stay was similar (3 vs. 3.5 days, p = 0.28), and ICU admission was not statistically different (6 vs. 12, p = 0.48). Conclusions: This retrospective study suggests that when hysterectomy is planned, there is no difference in maternal outcomes and morbidity with an expectant management with cesarean hysterectomy in the second trimester compared to proactive cesarean hysterectomy. Full article

Background/Objectives: While most studies on the postoperative condition of patients with spinal cord tumors describe long-term outcomes, data are needed on immediate surgical outcomes demanding rehabilitation to make informed assessments for postoperative planning. The aim of this study was to identify factors predicting function and rehabilitative needs after intradural spinal tumor surgery. Methods: Eighty-five prospectively recruited patients underwent surgery for intradural intramedullary (ID-IM; n = 23) and extramedullary (ID-EM; n = 62) tumors. Neurological and functional status were assessed before surgery, after surgery, and at discharge using the modified McCormick scale (MMS), Karnofsky performance status (KPS) scale, Barthel index (BI), and the gait index (GI). Results: There were no significant predictors of early postoperative rehabilitation in the ID-IM group. In the ID-EM group, age, thoracic level, subtotal resection (STR), repeat surgery, and functional scale scores predicted the need for rehabilitation. In multivariable analysis, MMS (odds ratio (OR) 8.7; 95% confidence interval (CI): 2.37–32.44) and STR (OR 13.00; 95%CI: 1.56–107.87) remained independent predictors of rehabilitation need (area under curve, 92%). Despite their younger age, most patients with ID-IM tumors, especially ependymomas, required rehabilitation but improved quickly (KPS, BI, p < 0.001). Among ID-EM tumors, meningiomas were characterized by poorer preoperative function and low gross total resection (GTR) rates, but did not deteriorate neurologically after surgery. Patients with schwannoma and ID-EM ependymomas achieved the highest GTR rate and had the best function both before and after surgery. Conclusions: These results may be useful for estimating early rehabilitation needs after intradural tumor surgery and counseling patients before surgery about the expected postoperative course. Full article

Autonomous Underwater Vehicles (AUVs) operate independently using onboard batteries and data storage, necessitating periodic recovery for battery recharging and data transfer. Traditional surface-based launch and recovery (L&R) operations pose significant risks to personnel and equipment, particularly in adverse weather conditions. Subsurface docking stations provide a safer alternative but often involve complex fixed installations and costly acoustic positioning systems. This work introduces a comprehensive docking solution featuring the following two key innovations: (1) a novel deployable docking station (DDS) designed for rapid deployment from vessels of opportunity, operating without active acoustic transmitters; and (2) an innovative sensor fusion approach that combines the AUV’s onboard forward-looking sonar and camera data. The DDS comprises a semi-submersible protective frame and a subsurface, heave-compensated docking component equipped with backlit visual markers, an electromagnetic (EM) beacon, and an EM lifting device. This adaptable design is suitable for temporary installations and in acoustically sensitive or covert operations. The positioning and guidance system employs a multi-sensor approach, integrating range and azimuth data from the sonar with elevation data from the vision camera to achieve precise 3D positioning and robust navigation in varying underwater conditions. This paper details the design considerations and integration of the AUV system and the docking station, highlighting their innovative features. The proposed method was validated through software-in-the-loop simulations, controlled seawater pool experiments, and preliminary open-sea trials, including several docking attempts. While further sea trials are planned, current results demonstrate the potential of this solution to enhance AUV operational capabilities in challenging underwater environments while reducing deployment complexity and operational costs. Full article