Reduce Resource Utilization Research Articles

10 TIPS on how to use dynamic risk assessment and alerts for AKI

Abstract Acute kidney injury (AKI) is a common syndrome in hospitalized patients and is associated with increased morbidity and mortality. The focus of AKI care requires a shift away from strictly supportive management of established injury to the early identification and timely prevention of worsening renal injury. Identifying patients at risk for developing or progression of severe AKI is crucial for improving patient outcomes, reducing length of hospitalization and minimizing resource utilization. Implementation of dynamic risk scores and incorporation of novel biomarkers show promise for early detection and minimizing progression of AKI. Like any risk assessment tools, these require further external validation in a variety of clinical settings prior to widespread implementation. Additionally, alerts which may minimize exposure to a variety of nephrotoxic medications or prompt early nephrology consultation are shown to reduce the incidence and progression of AKI severity and enhance renal recovery. While dynamic risk scores and alerts are valuable, implementation requires thoughtfulness and should be used in conjunction with the overall clinical picture in certain situations, particularly when considering the initiation of fluid and diuretic administration or renal replacement therapy. Despite the contemporary challenges encountered with alert fatigue, implementing an alert-based bundle to improve AKI care is associated with improved outcomes, even when implementation is incomplete. Lastly, all alert-based interventions should be validated at an institutional level and assessed for their ability to improve institutionally relevant, clinical meaningful outcomes, reduce resource utilization and provide cost effective interventions.

Using Quality Improvement to Optimize Blood Product Utilization in a Pediatric Cardiac Catheterization Laboratory.

Packed red blood cells (PRBC) are frequently ordered for cardiac catheterization procedures, which increases resource utilization and patient charges. We applied the Plan-Do-Study-Act (PDSA) principle in order to optimize the ordering of PRBC for pediatric cardiac procedures and reducing charges. Our primary aim was to increase adherence to ordering guidelines to greater than 97%, with a global aim to reduce resource utilization. The existing PRBC ordering guidelines were revised and procedure reports were updated to include administration of PRBC. The rate of pre-procedure PRBC orders, adherence to the new protocol guidelines, presence of documentation and rate of blood transfusion within 24h post-procedure, frequency of emergency release blood during a procedure were monitored. During our study period, there was an increased adherence from 86 to 100% adherence following implementation of the updated guidelines. With improved adherence, we decreased PRBC ordering and hospital charges to the patient in low-risk cardiac catheterization procedures, without an increase in blood transfusions.

Antenatal Risk of Coarctation for Newborns at Hartford Hospital (ARCH) Pathway: A Predictor of Postnatal Management Strategy.

The diagnosis of coarctation of the aorta (CoA) prior to birth can be challenging due to the physiologic changes during postnatal transition. Prenatal risk stratification can standardize postnatal management and improve outcome. CT Children's Fetal Cardiology created the Antenatal Risk of CoA in Hartford (ARCH) clinical pathway defining four distinct postnatal order sets based on degree of suspicion for ductal dependency on fetal evaluation: low, low-moderate, moderate-high, and high risk. This study aims to evaluate safety and efficacy of the ARCH pathway in neonates with suspected CoA. This study was a single-center, retrospective chart review evaluating maternal-infant dyads with findings concerning for CoA between July 2004 and July 2021, before and after ARCH pathway implementation. Neonates were evaluated for the presence or absence of critical CoA and postnatal clinical data were collected. Statistical analysis was performed using chi square and Fisher's exact test. There were 108 maternal-infant dyads studied, comprising 53 non-pathway patients and 55 ARCH pathway participants. Thirty-three neonates had critical CoA, comprising 23 non-pathway and 10 ARCH pathway subjects.Patients categorized in the high-risk group were highly associated with critical CoA (P = 0.003). Non-pathway neonates with CoA demonstrated higher likelihood of hospital transfer compared to ARCH pathway neonates (56.5% vs. 10.0%, P = 0.021).NICU admission, prostaglandin administration, and intubation were not significantly different between before and after ARCH implementation (P < 0.05). More echocardiograms were performed in ARCH pathway neonates without CoA than their non-pathway counterparts (1.586 vs. 2.133, P = 0.049). The ARCH pathway is a safe, reliable prenatal risk stratification system to help guide management of patients with critical CoA. These results identify effective targets of modification to the pathway to reduce resource utilization without compromising safety.

Enhancing the security in IoT and IIoT networks: An intrusion detection scheme leveraging deep transfer learning

The Internet of Things (IoT) networks, which are defined by their interconnected devices and data streams are an expanding attack surface for cyber adversaries. Industrial Internet of Things (IIoT) is a subset of IoT and has significant importance in-terms of security. Robust intrusion detection systems (IDS) are essential for protecting these critical infrastructures. Our research suggests a novel approach to the detection of anomalies in IoT and IIoT networks that leverages the capabilities of deep transfer learning. Our methodology begins with the EdgeIIoT dataset, which serves as the basis for our data analysis. We convert the data into an appropriate image format to enable Convolutional Neural Network (CNN)-based processing. The hyper-parameters of individual machine learning models are subsequently optimized using a Random Search algorithm. This optimization phase optimizes the performance of each model by modifying the hyper-parameters that are unique to the learning algorithms. The performance of each model is meticulously assessed subsequent to hyper-parameter optimization. The top-performing models are subsequently, strategically selected and combined using the ensemble technique. The IDS scheme’s overall detection accuracy and generalizability are improved by the integration of strengths from multiple models. The proposed scheme demonstrates significant effectiveness in identifying a broad spectrum of attacks, encompassing a total of 14 distinct attack types. This comprehensive detection capability contributes to a more secure and resilient IoT ecosystem. Furthermore, application of quantization to our best models reduces resource utilization significantly without compromising accuracy.

Parcel-Locker-Sharing Model for E-Commerce Logistics Service Providers

In the e-commerce last-mile delivery process, the asset operators (logistics service providers who own parcel locker facilities) support their delivery service with parcel lockers, while the non-asset operators (logistics service providers without parcel lockers) perform door-to-door delivery. Due to demand fluctuation, asset operators’ parcel-locker slots may be left vacant, while non-asset operators are stuck with the high-cost door-to-door service. The exclusiveness of parcel-locker usage reduces resource utilization and service efficiency in last-mile delivery. Therefore, this paper proposes a parcel-locker-sharing model in which these two parties share the parcel-locker capacity in last-mile delivery. The asset operator rents the unused parcel lockers to the non-asset operator by charging a rental fee,while the non-asset operator rents the parcel lockers for delivery to save logistics costs. The motivation of this alliance is to increase the profits of both parties and that of the total supply chain. This study establishes the supply-chain profit model for the parcel-locker-sharing framework and finds that the profit or loss depends on the comparison of the operation cost savings and delivery-cost savings. A numerical analysis is conducted to validate the final result. The research further suggests the optimal rental quantity and price interval. This paper is the first to study the operational mechanism of sharing the parcel locker between two distinct types of logistics service providers and to offer recommendations for industrial application.

Initiation or switch to insulin degludec/insulin aspart in adults with type 2 diabetes in India: Results from a prospective, non-interventional, real-world study.

To investigate clinical outcomes in adults with type 2 diabetes (T2D) after insulin degludec/insulin aspart (IDegAsp) treatment in a real-world setting. The 26 weeks study involved 1102 adults with T2D who were either initiated with or switched to IDegAsp according to local practice in six countries. It was an open-label, non-interventional study. The primary endpoint was the change in glycosylated haemoglobin (HbA1c) levels from baseline to the end of study (EOS). From India, 185 adults participated in this study with mean age of 58.1 (10.3) years and 14.4 (8.1) years of mean duration of T2D. Mean HbA1c decreased from 9.8% (1.8) at baseline to 8.2% (0.1) at the EOS; change in HbA1c from baseline [95% CI]: -1.6% (0.1) [-1.8; -1.4], P < 0.0001. There was a significant reduction in mean fasting plasma glucose (FPG) level from 190.0 (65.8) mg/dl at baseline to 141.9 (4.3) mg/dl at EOS; change in FPG from baseline [95% CI]: -52.2 (4.3) mg/dl [-60.7; -43.7], P < 0.0001. There was a numerical reduction in resource utilization related to diabetes and its complications and hypoglycaemic episodes. From baseline to EOS, the participants with outpatient visits (72 to 32) and workdays missed (2 to 0) decreased. Additionally, the number of patient-reported non-severe hypoglycaemic (47 to 8) and severe hypoglycaemic (4 to 1) episodes decreased as well. Initiation or switching to IDegAsp led to improvement in glycaemic control in real-world population of Indian adults with T2D. This was accompanied by a numerical reduction in resource utilization and patient-reported hypoglycaemia. Clinical trial registration: NCT04042441.

Optimized power and speed of Split-Radix, Radix-4 and Radix-2 FFT structures

Fast Fourier transform (FFT) is a fundamental building block for digital signal processing applications where high processing speed is crucial. Resource utilization in implementing FFT structures can be minimized by optimizing the performance of multipliers and adders used within the design. FFTs are also widely used in various machine learning algorithms. To achieve increased processor efficiency and reduced resource utilization, we propose a hardware design for Radix-2, Radix-4, and Split-Radix FFT architectures that utilizes a novel parallel prefix adder. This design offers lower power consumption, smaller chip area, and faster operation compared to existing architectures. Our performance analysis focuses on metrics such as power consumption, clock speed, and hardware complexity for Radix-2, Radix-4, and Split-Radix FFT algorithms implemented with the proposed adder. We compare these metrics using our proposed arithmetic structure against existing adder designs. The results indicate that the Split-Radix FFT architecture achieves lower power consumption and smaller chip area compared to Radix-4 and Radix-2 methods. Additionally, the Split-Radix FFT exhibits a higher clock speed. Therefore, based on these findings, the Split-Radix algorithm appears to be a compelling choice for implementation on field-programmable gate arrays due to its high speed and lower power consumption.

Experience Curve With the Cone Procedure for Ebstein’s Anomaly: Effect on Cost and Resource Utilization

BackgroundThe volume-outcome relationship is well-known in health care. The Experience Curve, initially developed by the Boston Consulting Group for manufacturing, offers insight on this relationship and has never before been applied to health care. ObjectivesThe purpose of this study was to determine the effect of experience on cost and resource utilization for the Cone procedure. MethodsWe performed a retrospective review of patients who underwent Cone reconstruction for Ebstein’s anomaly at Boston Children’s Hospital between October 2010 and October 2021. Cardiopulmonary bypass time and aortic cross clamp time over time were evaluated using exponential regression to assess the surgeon-level learning curve. At the hospital level, length of stay and cost over time were assessed using exponential regression. ResultsThere were 115 patients included in the study. Median hospital length of stay was 7.9 days (IQR: 6.4-10.2 days) with a decline of 3.3% per year. Median intensive care unit [ICU] length of stay was 3.2 days (IQR: 2-5.7 days) with a decline of 10.5% per year. Adjusted direct costs indexed to 2020 prices fell by 4% per year. There was no statistically significant change in cardiopulmonary bypass or aortic cross clamp time. In mediation analysis, the reduction in cost was completely accounted for in the decline in ICU length of stay. ICU length of stay was correlated with duration of intubation. ConclusionsIncreasing familiarity with Ebstein’s anomaly and Cone reconstruction led to a reduction in resource utilization.

Cost-Minimization Analysis for Subcutaneous Daratumumab in the Treatment of Newly Diagnosed Multiple Myeloma in Three Gulf Countries

Background: The second most common hematologic cancer worldwide is multiple myeloma (MM), with incidence and mortality rates that have more than doubled over the past 30 years. The safety and efficacy of daratumumab regimens in the treatment of newly diagnosed MM (NDMM) is demonstrated in clinical trials. Objective: To assess the financial effects of the adoption of subcutaneous daratumumab (dara-SC) rather than intravenous daratumumab (dara-IV) for the treatment of NDMM in three Gulf countries (Qatar, Oman and the United Arab Emirates; UAE), a cost-minimization model was constructed. Methods: We performed static cost minimization analyses from a societal perspective to evaluate the costs and possible reductions in resource utilization associated with a shift from dara-IV infusion to dara-SC injection for NDMM patients over a 5-year time horizon. The model included 2 scenarios: the current scenario in which 100% of patients with NDMM are treated with dara-IV infusion and a future scenario in which dara-SC injection is gradually adopted over the modeled time horizon. The model differentiated precisely between autologous stem cell transplantation (ASCT)–eligible and ASCT-ineligible NDMM patients in terms of their number in each group and the associated therapeutic regimens. One-way sensitivity analyses were also conducted. Results: The model showed that the use of dara-SC in NDMM patients who were eligible or ineligible for ASCT resulted in lower non-drug costs, including premedication drug costs, adverse-effect costs, administration costs, medical staff costs, and indirect costs. The resulting total savings over the 5-year time horizon of the model for Hamad Medical Corporation, Sultan Qaboos University Hospital/Royal Hospital, Sheikh Shakhbout Medical City (SSMC), and Tawam Hospital were QAR −2 522 686, OMR −143 214, AED −30 010 627, and AED −5 003 471, respectively. Conclusion: The introduction of dara-SC as a front-line treatment for NDMM patients in Qatar (Hamad Medical Corporation), Oman (Sultan Qaboos University Hospital, Royal Hospital-MOH), and the UAE (SSMC and Tawam Hospital) can help save resources and minimize constraints on the healthcare system.

Toward efficient resource utilization at edge nodes in federated learning

Federated learning (FL) enables edge nodes to collaboratively contribute to constructing a global model without sharing their data. This is accomplished by devices computing local, private model updates that are then aggregated by a server. However, computational resource constraints and network communication can become a severe bottleneck for larger model sizes typical for deep learning (DL) applications. Edge nodes tend to have limited hardware resources (RAM, CPU), and the network bandwidth and reliability at the edge is a concern for scaling federated fleet applications. In this paper, we propose and evaluate a FL strategy inspired by transfer learning in order to reduce resource utilization on devices, as well as the load on the server and network in each global training round. For each local model update, we randomly select layers to train, freezing the remaining part of the model. In doing so, we can reduce both server load and communication costs per round by excluding all untrained layer weights from being transferred to the server. The goal of this study is to empirically explore the potential trade-off between resource utilization on devices and global model convergence under the proposed strategy. We implement the approach using the FL framework FEDn. A number of experiments were carried out over different datasets (CIFAR-10, CASA, and IMDB), performing different tasks using different DL model architectures. Our results show that training the model partially can accelerate the training process, efficiently utilizes resources on-device, and reduce the data transmission by around 75% and 53% when we train 25%, and 50% of the model layers, respectively, without harming the resulting global model accuracy. Furthermore, our results demonstrate a negative correlation between the number of participating clients in the training process and the number of layers that need to be trained on each client’s side. As the number of clients increases, there is a decrease in the required number of layers. This observation highlights the potential of the approach, particularly in cross-device use cases.

Effect of heart failure on mortality and clinical outcomes in patients hospitalized with pancreatic cancer.

e16367 Background: Pancreatic cancer (PC) is the fourth leading cause of cancer-related death globally. The impact of HF on clinical outcomes in hospitalized patients with PC remains unknown. Considering the poor prognosis associated with PC, we aim to compare clinical outcomes in PC patients with or without HF. Methods: The research utilized the National Inpatient Sample (NIS) database, covering data from 2019 and 2020. Through this dataset, patients hospitalized with a primary discharge diagnosis of PC and a concurrent secondary diagnosis of HF were identified and a multivariate regression analysis was applied. Our primary outcome was mortality in patients with comorbid heart failure in patients diagnosed with pancreatic cancer compared to pancreatic cancer alone and our secondary outcomes were the length of hospitalization, associated hospitalization cost, and the risk of clinical complications or outcomes such as major depressive disorder, atrial fibrillation, acute coronary syndrome, etc. Baseline patient characteristics were found to be comparable in either group and data hence was not specifically adjusted in this regard. Results: A total of 74,585 patients were hospitalized with PC, of which 5,755 had a concurrent diagnosis of HF. The mean age of patients with HF was 74.43 years, while those without heart failure was 67.97 years. Primary and secondary outcomes were calculated. HF was not found to be an independent predictor of mortality (OR 1.17, 95% CI 0.90-1.53, p=0.22), and there was no significant difference in the length of stay in the hospital between the two groups of patients (1.09, 95% CI 0.59-1.60, p&lt;0.001). However, patients with heart failure had an increased total cost of hospitalization ($18,045, 95% CI 5,697-30,392, p=0.00) and increased odds of worse clinical outcomes such as major depressive disorder (OR 1.26, 95% CI 1.02-1.55, p=0.03), p&lt;0.001), atrial fibrillation (OR 4.59, 95% CI 3.85-5.47), acute coronary syndrome (OR 9.96, 95% CI 4.31-23.01, p&lt;0.001), acute respiratory failure (OR 2.69, 95% CI 2.08-3.47, pneumonia (OR 1.66, 95% CI 1.17-2.36 p=0.00), septic shock (OR 1.54, 95% CI 1.09-2.17, p=0.01), p&lt;0.001), invasive mechanical ventilation (OR 2.70, 95% CI 1.96-3.72, p&lt;0.001), ICU admission (OR 2.18, 95% CI 1.67-2.85, p&lt;0.001), acute kidney injury (OR 1.84, 95% CI 1.56-2.18, p&lt;0.001), and cardiovascular accident (CVA) (OR 2.74, 95% CI 1.37-5.47, p=0.004). Conclusions: HF is not an independent predictor of mortality or prolonged length in PC patients, but it is associated with increased hospitalization costs and adverse clinical outcomes. Implementing careful and aggressive management strategies for addressing HF is necessary to optimize patient quality of life, minimize adverse events, and reduce resource utilization to minimize the costs of hospitalization and improve quality of life.

#1094 Hemodiafiltration improves solute clearance without increasing water consumption: an educational bench model for the dialysis ward

Abstract Background and Aims Recently, the positive results of the CONVICE trial have raised the question whether the improved outcome of postdilution high volume HDF (oHDF-p) compared to high flux HD (hfHD) requires increased water consumption. From mathematical modelling it can be shown that oHDF-p always leads to higher low molecular clearance than hfHD at equal total dialysate consumption Qd_tot = Qd_dial + Qs (Qd_dial: dialysate flow through the dialyzer, Qs: substitution flow). This has also been shown in a clinical trial (Mesic et al, HDI 15, p. 522-529). However, mathematical modelling might not be intuitive for everyone, and clinical trials are always influenced by multiple confounders. To add a more intuitive way of reasoning, a simple educational bench model was designed based only on equipment easily available in a standard dialysis clinic which allows to compare dialysis efficiencies based on the urea concentrations as surrogate parameter. Since the fundamental task of dialysis is to cleanse blood from uremic solutes, the treatment modality which at equal resource utilization reduces solute concentration most is the more efficient one! Method Dialysis treatments were simulated in single pass setup using a Fresenius 5008S machine (Fig. 1). Blood inlet was simulated as a fixed urea concentration of 170 mg/dL in dialysate from a 110 L reservoir “cbi”. Treatment parameters were set such that they correspond to effective values in in-vivo treatments: As the mass transfer coefficient is always much higher in vitro with water than in vivo with blood, a small high-flux dialyzer (Fresenius FX40) was used. Blood flow Qb was set to 300 mL/min, corresponding to the in vivo blood water flow for Qb_set ∼ 350 mL/min. Alternating experiments in oHDF-p and hfHD were carried out in triplicate with Qd_tot=400 mL/min and 300 mL/min. This resulted in the settings HDF400, HD400, HDF300 and HD300 (cf. Fig. 1). Qs was set to 100 ml/min in oHDF-p. In each setting the fluid from blood and dialysate outlet were collected (recipients “cbo” and “cdo”, resp.) for 8 min. To be independent of the machine accuracy of set flow rates, sample volumes were measured by weighing, and effective flow rates of blood Qb_eff and total dialysate Qd_tot_eff were calculated. Samples were taken into monovettes from “cbi”, “cbo” and “cdo”. Urea concentrations cbi, cbo and cdo were determined by sending the samples to two commercial clinical labs who delivered consistent results. Blood and dialysate side urea clearances were calculated as KUB= Qb_eff*(cbi-cbo)/cbi and KUD=Qd_tot_eff*cdo/cbi. Results The table in Fig. 1 includes the measure values of Qb_eff and Qd_tot_eff for each setting. Blood and dialysate outlet concentrations (cbo and cdo, resp.) and calculated blood and dialysate side clearances are shown in Fig. 2. Lab results in repeated settings were highly reproducible with a SD &amp;lt; 2 mg/dL. Switching from hfHD to oHDF-p at equal Qd_tot always decreased cbo, corresponding to a higher clearance in oHDF-p. Reducing total dialysate flow from 400 to 300 ml/min caused a slight decrease in clearance in both modalities. KUB and KUD were in good agreement, showing that urea mass balance was well controlled in the experiment. Conclusion The educational bench model demonstrates the superior low molecular clearance of oHDF-p compared to hfHD at equal Qd_tot, reflected by lower urea cbo values in oHDF-p. Although the clinical outcome advantage of oHDF-p is rather related to middle molecular clearance, the model shows that the improved treatment quality of oHDF-p can be provided with no extra resource utilization compared to hfHD, and that dialysis adequacy as measured by urea clearance is even increased despite of the fact that a part of Qd_tot is deviated as substitution fluid. As moderately reducing Qd_tot in many cases only results in acceptable clearance reductions, the automated adjustment of both dialysate and substitution flow rates for oHDF-p could improve patient outcome at even reduced resource utilization.

The influence of neighborhood income on healthcare utilization in pediatric liver transplant.

Neighborhood contextual factors are associated with liver transplant outcomes. We analyzed associations between neighborhood-level socioeconomic status and healthcare utilization for pediatric liver transplant recipients. We merged the Pediatric Health Information System and Scientific Registry of Transplant Recipients databases and included liver transplant recipients ≤21 years hospitalized between January 2004 and May 2022. Outcomes were annual inpatient bed-days, risk of hospitalizations, and risk of liver biopsies. The primary exposure was zip code-based neighborhood income at transplant. We applied causal inference for variable selection in multivariable analysis. We modeled annual inpatient bed-days with mixed-effect zero-inflated Poisson regression, and rates of hospitalization and liver biopsy with a Cox-type proportional rate model. We included 1006 participants from 29 institutions. Children from low-income neighborhoods were more likely to be publicly insured (67% vs. 46%), Black (20% vs. 12%), Hispanic (30% vs. 17%), and have higher model for end-stage liver disease/pediatric end-stage liver disease model scores at transplant (17 vs. 13) than the remaining cohort. We found no differences in inpatient bed-days or rates of hospitalization across neighborhood groups. In univariable analysis, low-income neighborhoods were associated with increased rates of liver biopsy (rate ratio [RR]: 1.57, 95% confidence interval [CI]: 1.04-2.34, p = 0.03). These findings persisted after adjusting for insurance, race, and ethnicity (RR: 1.86, 95% CI:1.23-2.83, p < 0.01). Children from low-income neighborhoods undergo more liver biopsies than other children. These procedures are invasive and potentially preventable. In addition to improving outcomes, interventions to mitigate health inequities among liver transplant recipients may reduce resource utilization.

FATE: A Flexible FPGA-Based Automatic Test Equipment for Digital ICs

The limits of chip technology are constantly being pushed with the continuous development of integrated circuit manufacturing processes and equipment. Currently, chips contain several billion, and even tens of billions, of transistors, making chip testing increasingly challenging. The verification of very large-scale integrated circuits (VLSI) requires testing on specialized automatic test equipment (ATE), but their cost and size significantly limit their applicability. The current FPGA-based ATE is limited in its scalability and support for few test channels and short test vector lengths. As a result, it is only suitable for testing specific chips in small-scale circuits and cannot be used to test VLSI. This paper proposes a low-cost hardware and software solution for testing digital integrated circuits based on design for testability (DFT) on chips, which enables the functional and performance test of the chip. The solution proposed can effectively use the resources within the FPGA to provide additional test channels. Furthermore, the round-robin data transmission mode can also support test vectors of any length and it can satisfy different types of chip test projects through the dynamic configuration of each test channel. The experiment successfully tested a digital signal processor (DSP) chip with 72 scan test pins (theoretically supporting 160 test pins). Compared to our previous work, the work in this paper increases the number of test channels by four times while reducing resource utilization per channel by 37.5%, demonstrating good scalability and versatility.

A reduced complexity rate-matching and channel interleaver/de-interleaver for 5G NR

Rate matching and channel interleavers play a pivotal role in code rate adaptation and minimizing burst errors in communication systems. The data and control channels in the Fifth generation (5G) New Radio (NR) employs distinct channel interleavers/de-interleavers for reducing bit error rate. However, the independent implementation results in a substantial increase in silicon cost. To address this challenge, hardware sharing is pivotal for optimizing resource utilization and improving overall system performance. This study introduces a rate-matching architecture and a simple algorithm for the creation of a new Shared-Resource Channel interleaver/De-interleaver (SRC) architecture. The proposed work ensures low complexity tailored for both data and control channels within the 5G NR framework by incorporating the hardware sharing technique. The proposed SRC architecture, synthesized on the Virtex-7 Field Programmable Gate Array (FPGA), showcases noteworthy advancements over existing 5G NR interleaver architecture, achieving a notable 23% reduction in resource utilization. Through implementation using the Qflow Compiler in 45 nm CMOS technology, the ASIC synthesis of the proposed design demonstrates a substantial decrease in hardware complexity and area by 44.6% and 43%, respectively. Furthermore, the proposed architecture exhibits a 19.2% reduction in power consumption compared to its existing counterpart in 5G NR. The proposed integrated design demonstrates a remarkable resource efficiency by occupying nearly half the resources compared to individual implementations.

929 Outcomes in Pediatric Spinal Cord Injuries Based on Time to Surgery

INTRODUCTION: Early surgery in individuals with traumatic spinal cord injury (SCI) has been associated with improved neurological recovery, lower risk of complications, and reduced resource utilization. Patient and healthcare-related factors may contribute to delay in surgery. This study aimed to determine factors contributing to longer time-to-surgery in pediatric patients with complete and incomplete SCI. METHODS: Patients with SCI were identified in the National Trauma Data Bank based on AIS codes. Patients who died in the ED or were transferred to another hospital were excluded. Procedure codes were used to identify patients who underwent spinal surgery. Patients were grouped by time to surgery less than or greater than 12 hours. RESULTS: 549 patients were identified with 235 incomplete and 314 complete SCI. 399 patients underwent surgery (177 incomplete; 222 complete). Of patients who underwent surgery, 55% of incomplete and 52% of complete SCI went to surgery after 12 hours. SCI patients with greater than 12-hour time-to-surgery had worse injury severity scores (32 vs 28, p &lt; 0.05). Additionally, ICU LOS, total ventilation days, and total LOS were longer. Time-to-surgery greater than 12 hours was statistically associated with unplanned ICU stay (incomplete), unplanned OR (complete), ventilator associated pneumonia (complete), and delirium (incomplete). Time-to-surgery was not significantly associated with race, gender, or mechanism of injury. pneumonia (complete), and delirium (incomplete). Time-to-surgery was not significantly associated with race, gender, or mechanism of injury. CONCLUSIONS: Greater time-to-surgery is associated with higher risk of complications and longer hospital stays in patients with complete and incomplete SCI in the pediatric population. This data raises the question as to whether earlier surgery could reduce complications.

An efficient cloud-integrated distributed deep neural network framework for IoT malware classification

The proliferation of interconnected devices in the Internet of Things (IoT) landscape has introduced significant security concerns. With the integration of android devices, the potential for attackers to exploit vulnerabilities becomes a crucial issue. Timely detection of malware attacks has emerged as a critical challenge, especially in industrial IoT (IIoT), to prevent permanent damage. The slow inference speed of the current algorithms causes limited performance and a significant delay in the detection and mitigation of malware, especially in large-scale environments such as IIoT. High resource utilization can limit the size of the model (number of parameters), which could be implemented in embedded devices, which causes lower accuracy of the model. Hence, we proposed a HierarchicalCloudDNN, a distributed malware detection framework that used modified state-of-the-art deep learning algorithms in a boosting ensemble architecture. The proposed method could scale efficiently from IIoT devices up to the edge and cloud to speed up malware detection, simultaneously reduce resource utilization, and maintain a controllable level of accuracy compared to rival methods in malware detection. The objective is to minimize both run-time and resource consumption while maintaining performance characteristics. The performance analysis of the HierarchicalCloudDNN method was evaluated using standard performance measures on the frequently used BIG 2015 dataset. Based on evaluations, the proposed approach has demonstrated an impressive accuracy rate of 98.90%, outperforming many state-of-the-art models in performance, response time, and resource utilization. These findings suggest a notable decrease in both false positives and false negatives while maintaining accuracy. The proposed distributed malware analysis framework is scalable, robust, and helpful for the timely detection of malicious activity, especially in geographically distributed environments such as the IIoT.

Multi-Attribute Decision-Making Method in Preventive Maintenance of Asphalt Pavement Based on Optimized Triangular Fuzzy Number

By choosing the right pavement maintenance plan, we can reduce resource utilization, reduce environmental pollution, and extend road life, which is important for improving social sustainability. At present, the selection of road maintenance programs mostly adopts multiple attribute decision-making (MADA), in particular, the analytic hierarchy process (AHP) is often used. However, this method needs to use expert scoring data, which leads to strong subjectivity and poor reliability. Therefore, it reduces the science of road maintenance scheme selection. In order to reduce the subjectivity of the score and obtain a more suitable road maintenance scheme, this paper applies a multi-criteria decision-making method that characterizes attribute information by triangular fuzzy numbers (TFN) in the discrete decision space. Firstly, we invite experts to score the importance of the selection of pavement preventive maintenance technical solutions with respect to the indicators affecting the selection of solutions. Secondly, the two indicators of similarity and reliability are used to quantitatively evaluate the indicators and programs, respectively. Finally, we compare the weighted programs according to the overall possibility degree of each program. In actual cases, the overall possibility degree of each scheme obtained by this method is 1.0002–0.0477, and the optimal solution is fog sealing technology. The decision-making model applied in this paper can be considered in multiple dimensions, which can scientifically reduce the subjectivity of expert scoring. The best maintenance plan can also be quickly obtained through the simple calculation method in this paper.

Analysing the impact on groundwater quality using dynamic programming and vision transformer

The demand for sustainable water sources has intensified the focus on ground water harvesting, necessitating rigorous quality analysis for diverse applications. The existing methods for ground water quality analysis are briefly discussed, revealing a research gap in optimizing the sampling and testing process. To address this gap, we employ Dynamic Programming, which is utilized to streamline the sampling and testing process by selecting optimal locations and times. This study proposes a new methodology that combines Dynamic Programming (DP) and the Vision Transformer (ViT) model to evaluate the impact on ground water quality efficiently. This optimization not only enhances data comprehensiveness but also minimizes resource utilization, a crucial aspect in resource-constrained environments. The incorporation of the Vision Transformer model into the methodology brings a novel dimension to ground water quality analysis. ViT, a cutting-edge deep learning model for image analysis, is adapted to process visual data from ground water samples. This includes identifying contaminants, suspended solids, and microbial content, providing a more holistic understanding of ground water quality than traditional methods. Results from our integrated approach demonstrate its efficiency and accuracy in assessing ground water quality. It achieves a 10–15% boost in sampling optimization efficiency, 5–10% improvement in visual data accuracy, and 5–10% increase in precision. DP-ViT reduces resource utilization by 15–20% and consistently offers 5–10% higher data coverage.

SC-IZ: A Low-Cost Biologically Plausible Izhikevich Neuron for Large-Scale Neuromorphic Systems Using Stochastic Computing

Neurons are crucial components of neural networks, but implementing biologically accurate neuron models in hardware is challenging due to their nonlinearity and time variance. This paper introduces the SC-IZ neuron model, a low-cost digital implementation of the Izhikevich neuron model designed for large-scale neuromorphic systems using stochastic computing (SC). Simulation results show that SC-IZ can reproduce the behaviors of the original Izhikevich neuron. The model is synthesized and implemented on an FPGA. Comparative analysis shows improved hardware efficiency; reduced resource utilization, which is a 56.25% reduction in slices, 57.61% reduction in Look-Up Table (LUT) usage, and a 58.80% reduction in Flip-Flop (FF) utilization; and a higher operating frequency compared to state-of-the-art Izhikevich implementation.