Efficiency Metrics Research Articles

Performance improvement of a biotechnology vaccine cleanroom for contamination control

Cleanrooms play an important role in various industries, such as pharmaceuticals, specifically for vaccine cleanrooms by strictly regulating contaminants, temperature, humidity, and pressure to ensure product reliability, particularly in critical areas. This study investigates a biotechnology vaccine cleanroom using a comprehensive field measurement test and numerical simulation analysis. Field measurements were conducted during the at-rest and operational occupancy states. As a result, it was discovered that the operational condition has a higher concentration than the at-rest condition. To mitigate this issue, the numerical simulation improvement strategy employed in this study aimed to reduce the concentration under operational conditions. The strategy proposed in this simulation is determining the ventilation rate and arranging the outlet air grilles. To assess and evaluate the performance of the cleanroom, we utilized ventilation and removal efficiency metrics. These indices served as important metrics for evaluating the cleanroom's efficiency in controlling contaminants. In at-rest conditions, ventilation and removal efficiency were 90.0 % and 86.2 %, respectively. However, under operational conditions, these metrics dropped to 81.3 % and 80.1 %, respectively, in the baseline case. After the proposed improvement design, the ventilation and removal efficiency in the operational condition could improve by up to 6.8 % and 5.2 %, respectively. A proper arrangement of outlet air grilles could control the decrease in concentration. In addition, a higher ventilation rate also could decrease the concentration. However, it caused high energy consumption. Therefore, the ventilation rate must be designed optimally to compromise contamination control and energy saving.

SE Analysis and GEE Optimization of Hardware-Impaired Multi-Cell Massive MIMO Systems With Rician Fading and Phase Shifts

We consider a practical hardware-impaired multi-cell massive multi-input multi-output (mMIMO) system with spatially-correlated Rician fading channels, whose line-of-sight component experiences a random phase shift. The multi-cell mMIMO system employs two-layer large-scale fading decoding (LSFD) to mitigate the interference due to pilot contamination. We consider a dynamic analog-to-digital converter (ADC) architecture at the base stations (BS), which enables us to independently vary the resolution of ADC connected to its each antenna, and thus properly choose them to achieve a high spectral efficiency (SE). Both BS and user equipments (UEs) are also equipped with low cost radio frequency chains, which introduce additional hardware impairments. We first derive a closed-form SE expression for this system, and then optimize the UEs uplink data power and LSFD vectors to maximize the global energy efficiency metric. The proposed optimization, which is practically implementable due to its closed-form updates, is derived by combining the quadratic and Dinkelbach transforms, and generalized Rayleigh quotient. We investigate the ADC resolution and hardware impairments values for which LSFD is highly effective, and the values for which its effectiveness reduces.

Energy, Exergy, Economic, and Environmental Prospects of Solar Distiller with Three-Vertical Stages and Thermo-Storing Material

Solar distillation emerges as a viable remedy for addressing water scarcity in both remote and urban locales. However, its operational efficiency remains a limiting factor. Consequently, this study undertakes a comprehensive approach by introducing design modifications to enhance a distiller’s overall productivity. The pivotal adjustment involves configuring the distiller into a three-tiered structure, thus designating it as a multi-stage solar still (MSSS). Notably, the solar stills are crafted entirely from glass to optimize consistent solar tracking, eschewing the conventional sun-tracking rotation mechanism. Furthermore, the three-stage distiller undergoes refinement through the incorporation of a thermo-storing material (PCM) comprising paraffin infused with graphene nanocomposites at the base of the solar still (SS). Subsequent to these design enhancements, a comprehensive evaluation encompassing exergy, economic viability, environmental impact, and thermal considerations is conducted for both the conventional solar still (CSS) and MSSS. The outcomes elucidate that the upper stage of the MSSS outperforms its counterparts, producing superior results. Comparative analysis indicates a remarkable 160% enhancement in productivity for the MSSS over the CSS. Cumulative water productivities for the CSS and MSSS with PCM are recorded at 2840 and 7980 mL/m2 during the daytime, reflecting an improvement of 181%. The energy efficiency metrics reveal values of 31%, 49.8%, and 53% for the CSS, MSSS, and MSSS with PCM, respectively. Moreover, the MSSS with PCM demonstrates an exergy efficiency of 5.8%. The environmental implications are quantified at 12 tons of CO2 emissions per year for the MSSS with PCM. Finally, the cost considerations illustrate a reduction in the cost of freshwater for the MSSS with PCM (0.10 $/L) and the MSSS (0.13 $/L), as compared to the conventional SS (0.24 $/L).

Measurement on University Websites: A Perspective of Effectiveness

This paper highlights the importance of evaluating the performance of university websites and how this can affect the reputation of universities. Different quality evaluation models are analyzed and emphasized in the context of efficiency and how factors such as response time, processing capacity, efficient use of resources, scalability, data transfer rate, concurrency capacity, and fault tolerance can positively or negatively affect websites. In addition, the importance of applying specific techniques to increase efficiency in loading speed is pointed out, such as image optimization, responsiveness on desktop and mobile devices, and content caching, among others, which allow to improve the website's efficiency. To conduct this process, a case study was applied where the university websites were selected, efficiency metrics were defined, and the data provided by the performance measurement tools that provide metrics and quantitative data for the evaluation were collected and analyzed. from the website. The results of the study revealed that there is room for improvement in page load time and page size optimization. In addition, the need to upgrade the performance of mobile devices was identified, given the increasing use of smartphones and tablets to access websites. As a final recommendation, it is advised to implement a comprehensive strategy to improve website performance. This strategy should include optimization of page load time and page size as well as user experience considerations. By achieving optimal performance, universities can offer their users a more satisfying online experience, thus strengthening their reputation and their ability to attract new users.

Effects of Aging on Taxi Service Performance: A Comparative Study Based on Different Age Groups

Rapid population aging has significantly impacted labor supply and posed substantial challenges for the taxi industry, which have not yet been fully comprehended. Here, for the first time, we employ a large-scale dataset of taxi driver operations from China, establishing a comprehensive indicator system of taxi service performance, encompassing economic, environmental, and safety aspects. Through the application of multivariate regression models and other statistical analysis techniques, we have thoroughly investigated the mechanisms through which aging influences taxi service performance. Our research reveals that older drivers, despite exhibiting higher operational efficiency and greater inclination towards stability, underperform in time efficiency metrics, such as income per hour worked, owing to a more conservative working style. Furthermore, aging manifests negative effects on safety and environmental performance. Adjusting the driving strategies of older taxi drivers, such as regulating daily working hours and refining passenger-searching area preferences, can help mitigate these adverse impacts.

Efficiency Optimization in Multi-Branch Converters through Dynamic Control

As the global emphasis on solar energy intensifies, optimizing the efficiency of photovoltaic panels becomes crucial in meeting energy demands sustainably. Addressing this, our research delves deeply into advancing maximum power point tracking (MPPT), a pivotal component in perfecting the energy conversion process. Leveraging state-of-the-art mathematical modeling, in-depth simulations, and comprehensive experimental validation, we set out to markedly refine the performance of non-isolated multi-branch buck DC–DC converters. In this pursuit, we introduce an innovative algorithm meticulously designed to adjust the number of active branches. This adjustment is rooted in robust efficiency metrics, ensuring optimal power delivery even under dynamic and fluctuating conditions. We place a distinct emphasis on the transformative role of current in determining converter efficiency. Drawing from our findings, we advocate for an adaptive control strategy, precisely engineered to thrive in a spectrum of operational contexts. With this study, we not only present pivotal contributions to the domain of photovoltaic technology but also chart out clear expectations for future endeavors. Our hope is that these advancements serve as foundational steps, guiding the evolution of sustainable energy generation.

An Efficient Cooperative Routing with ML based Energy Efficiency Model for Distributed Underwater WSN Electricity Meter Warning System

Underwater wireless sensor network that operates underwater, typically in oceans, lakes, and rivers. UWSNs are composed of a large number of small sensor nodes that are equipped with various sensing and communication capabilities. These nodes are deployed in the underwater environment to collect and transmit data, which can be used for a variety of applications such as environmental monitoring, oceanography, and marine biology. The Underwater WSN (UWSN) consists of sensor nodes to sense the data and transmit it to the sink node. These sensor nodes (SN) are equipped with limited batteries, which is the central issue. Therefore, the routing protocols were developed for researchers to save energy. However, the increment of network lifetime remains an open challenge. Forwarding the data to the nearest SN to the sink will reduce the network reliability and stability, draining SN's energy early. To overcome these issues, this paper focused on developing an efficient Cooperative based routing (CR) with a machine learning (ML) model to improve the network's lifetime. The cooperative routing discovers the route path from the sender to the destination. The best possible way from the sender to the receiver has been selected using the ML approach called the Self-organizing network (SON). By identifying congestion-free multi-hop transmission using CRSON, the data packet is transmitted from sender to receiver with reduced energy, increasing the network's lifetime and reliability. This model is simulated and experimented with energy efficiency, packet delivery, loss rate, latency, and throughput metrics.

Determining quality of hire, the holy grail of recruitment: A structuration perspective

Purpose: This research paper aims to justify the need for the Quality of Hire (QOH) construct as a value-adding focus for strategic human resource management (SHRM). The traditional focus on efficiency and cost-oriented recruitment metrics overlooks the importance of QOH in providing a competitive advantage and delivering long-term value. The study expands the economic theory of human resource development and develops a profit-building concept relevant to SHRM by exploring the practices that enable QOH in organizations. Design: The study utilizes a case-study method to examine a target firm’s mechanisms to build QOH in its recruitment process. It applies a structuration theory lens to analyze the behavior of various actors, their agencies, and the continuous interplay between structure and action in enabling QOH. Findings: The findings suggest that assessing and building measures for getting QOH is a complex task for organizations due to the inherent reliance on lag measures such as performance and tenure. The study highlights that QOH can be enabled through changes in the firm’s recruitment practices. Originality: This paper contributes to recruitment research in two significant ways. First, it expands on the under-researched construct of QOH, providing clarity on its definition and importance. Second, it identifies lead practices that organizations can incorporate into their recruitment and selection processes to enable QOH. By using a structuration theory lens, the study explores how actors in the recruitment process adapt and align with new structural rules to enable QOH. Research implications: The research builds on the structuration theory in recruitment and selection and exhorts practitioners in organizations to move beyond efficiency-oriented recruitment practices and focus on practices that contribute to QOH. By considering post-hire outcomes, such as job performance and long-term retention, organizations can improve their talent acquisition and retention strategies, creating long-term value for the organizations.

Relationship of Credit Risk and Profitability of Microfinance Institutions in Kenya

Microfinance institutions (MFIs) play a crucial role in offering financial services to marginalized and economically disadvantaged populations. However, these institutions face the challenge of credit risk, which has the potential to greatly impact their profitability and long-term viability. This study examined the relationship between credit risk and the financial performance of MFIs in Kenya. The study aims to investigate the connection between Capital Adequacy, Operational Efficiency, Interest Rate Spread, and the profitability of microfinance institutions in Kenya. The research utilizes a comprehensive dataset from 14 licensed and regulated MFIs in Kenya, covering the period from 2020 to 2022.The study utilized a research design based on census data. Secondary data was utilized. The Study examines the previous financial reports to find secondary data on performance. The data collected underwent analysis using both descriptive and inferential statistical tools. Given that the current study focused on the relationship study, a regression model was used as the analysis tool. The results obtained were then presented in the form of tables. The results of this study will benefit policy makers, managers, administrators, entrepreneurs, researchers, consultants, scholars, and trainers involved in strategic Microfinance Institutions. Results from the analysis suggest that the Kenyan microfinance sector encountered significant financial difficulties in the past. The profitability metrics, specifically return on assets (ROA) and return on equity (ROE), showed negative mean values of -8.89% and -8.72%, respectively. Based on the findings, it appears that MFIs incurred losses, as their assets and equity were not fully utilized. The study also discovered There is a strong positive relationship between Capital Adequacy and Operational Efficiency, as indicated by a correlation coefficient of 0.811. When Capital Adequacy increases, Operational Efficiency also increases, The correlation was highly significant, indicating a robust relationship. However, it is worth noting that there is a strong positive relationship between Capital Adequacy and Profitability Metrics, as indicated by a correlation coefficient of 0.961. Capital Adequacy has improved, leading to a significant increase in Profitability Metrics. The relationship between Operational Efficiency and Profitability Metrics was found to be highly positive, with a correlation coefficient of 0.875. The correlation between improved operational efficiency and higher profitability metrics is incredibly important. The relationship between Interest Rate Spread and Profitability Metrics has shown a strong positive correlation, with a correlation coefficient of 0.943. The profitability metrics were greatly impacted by the increased interest rate spread. The study found that it is recommended for Microfinance institutions in Kenya to diversify their income streams and give priority to improving credit risk management practices in order to effectively navigate the complexities of the sector. Additional research is needed to understand the factors that influence credit risk and profitability in the microfinance sector in Kenya.

Organic Rankine cycle-ejector heat pump hybrid system using low GWP zeotropic mixtures for trigeneration application

This paper presents a comprehensive study focused on improving the thermal efficiency and performance of an organic Rankine cycle (ORC) combined with an ejector heat pump (EHP) for trigeneration application. The utilization of zeotropic mixtures with low global warming potential (GWP) was proposed as a solution to address the inherent inefficiencies of the ORC at low-temperature heat sources and to enhance the coefficient of performance (COP) of the ejector refrigeration cycle. A thermodynamic model was developed to predict system performance, utilizing low-temperature heat sources such as solar or geothermal energy, along with zeotropic mixtures. The model was validated with the available literature data, demonstrating very good agreement. Five zeotropic fluid mixtures were preliminary studied, and the optimal mass fractions identified. Detailed investigations were then carried out for two of these mixtures, R1233zd(E)/propane and butane/propane, which exhibit low GWP and ozone depletion potential (ODP). The influence of generator temperature and entrainment ratio on system efficiency metrics, exergy, and COP were evaluated for these mixtures. Conclusions are drawn based on the two optimized zeotropic mixtures for maximum thermal efficiency. For R1233zd(E)/propane mixture with mass fractions 0.75/0.25 and butane/propane mixture with mass fractions 0.5/0.5 at generator temperature of 75 °C and entrainment ratio of 0.5, the systems achieved a heating capacity of 1607.9 kW and 2847.9 kW, a cooling capacity of 1037.5 kW and 1280.3 kW, and a net power output of 59.2 kW and 136.3 kW. Other related performance and efficiency parameters are included in the paper. These findings indicate the feasibility of utilizing these systems for trigeneration application. Additionally, a comparative analysis with a regenerative ORC highlighted the ORC-EHP hybrid system's advantages, including enhanced heating efficiency, cooling capacity, and overall COP.This research contributes valuable insights to advance the efficiency and sustainability of combined cooling, heat, and power systems, fostering the progression of innovative energy solutions.

Machine Learning-Enhanced Decision Support for Neurological Disorders Management in Healthcare

In a nutshell: Neurological illnesses, which include a wide range of ailments from Parkinson's disease to epilepsy, present considerable problems to the administration of healthcare. The purpose of this abstract is to present an overview of a unique technique called Machine Learning-Enhanced Decision Support (ML-EDS) for Neurological Disorders Management, which aims to improve patient treatment as well as patient outcomes. Within the scope of this investigation, we have suggested a complete ML-EDS framework that integrates cutting-edge machine learning strategies with more conventional approaches to medical treatment. The framework provides a comprehensive solution that makes use of several types of patient data, such as clinical records, neuroimaging data, genetic information, and data from wearable sensors. The Random Forest, Long Short-Term Memory (LSTM), and k-Nearest Neighbours (k-NN) algorithms are integrated into our methodology in order to improve illness progression prediction, seizure event detection, and individualized medication suggestion. In comparison to more conventional approaches, the suggested framework reveals noteworthy advantages. When compared to conventional algorithms, it demonstrates superior accuracy, sensitivity, and specificity in the prediction of illness development. Additionally, its F1 score is significantly higher. In addition, the application of LSTM for the identification of seizure episodes demonstrates the effectiveness of the method in determining crucial occurrences, which in turn enables prompt intervention. The possibility of tailored medical treatment based on k-means clustering is also highlighted in this research. Patients with comparable clinical characteristics can be grouped together to facilitate the development of personalized treatment methods, which can then provide individualized care pathways. The performance evaluation demonstrates that k-NN is capable of making therapy recommendations with high precision, hence contributing to an overall improvement in the health of the patient. Additionally, the proposed framework performs very well in terms of computational efficiency, reducing the amount of time required for both training and prediction, which positions it as a viable option for healthcare applications that are used in the real world. In summation, the use of machine learning in decision support for the management of neurological disorders in healthcare is a ground-breaking strategy that is reshaping the therapeutic landscape. It does this by using the power of machine learning, which enables it to provide more accurate illness progression predictions, rapid seizure event detection, and highly tailored medical care. The findings and efficiency metrics conclusively indicate that this strategy is superior to existing procedures, suggesting a paradigm change in the therapy of neurological disorders.

Development of an agent-based model incorporating Function–Behavior–Structure framework to enable systems engineering design process evaluation

An important step in the development of any process is evaluation. Evaluation ensures that the process affects effectiveness, efficiency, or other metrics as designed. Without evaluation, processes must stand solely on the theories, axioms, and/or heuristics that formed them. Outcome or objectives-based evaluations are especially useful in seeing if the expected impact is realized. Systems and design engineering present problems for outcome evaluations due to system design efforts being long in duration, expensive, organization/team specific, and environment/context specific. These characteristics make repeating the same effort under the same conditions using different processes difficult, if not impossible. While a comparison of processes for evaluation in a real application may not be feasible, a simulation of the processes with agents that capture system design behaviors may produce findings to support hypotheses. This paper examines the use of agent-based modeling and simulation to compare pseudo-waterfall and pseudo-agile engineering processes for a simple design problem. The Function–Behavior–Structure (FBS) model of design is used along with empirical data from FBS studies to examine the performance of a two-person design team using pseudo-waterfall and pseudo-agile engineering processes. The results of this exercise show possible advantages of agile-like processes in total time to complete the design.

Improved modeling of Congo's hydrology for floods and droughts analysis and ENSO teleconnections

Study regionThe Congo River basin (CRB), the world's second-largest river system, is subject to extreme hydrological events that strongly impact its ecosystems and population. Study focusHere we present an improved 40-year (1981–2020) hydrological reanalysis of daily CRB discharge and analyze the spatiotemporal dynamics of recent major CRB floods and droughts, and their teleconnection with El Niño-Southern Oscillation (ENSO), the dominant driver of tropical precipitation. We employ a large-scale hydrologic-hydrodynamic model (MGB) with lake storage dynamics representation and a data assimilation (DA) technique using in-situ and remote sensing observations. New Hydrological InsightsThe MGB model demonstrates satisfactory performance, with Kling-Gupta efficiency metric of 0.84 and 0.71 for calibration and validation, respectively. Incorporating lake representation substantially enhances simulations, increasing the Pearson correlation coefficient from 0.3 to 0.63. Additionally, DA yields a ∼13% reduction in discharge errors via cross-validation. We find that the 1997–1998 flood impacting the south and central CRB is statistically linked to a major El Niño event during that period. However, no such association is found for the 2019–2020 flood. Severe droughts in 1983–1984 and 2011–2012, affecting northern and southern CRB respectively, exhibit strong correlation with preceding El Niño and La Niña events, with a ∼10–12 months lag. This study advances understanding of the intricate interplay between spatiotemporal hydrological variability in CRB and large-scale climate phenomena like ENSO.

A Novel Communication Pathway Metric Evaluation using Throughput and Energy Improvements over Wireless Sensor Networks

Lifetime connectivity and low power consumption are two requirements for WSNs. Additionally, increasing WSN commercialization, application monitoring for higher QoS. Maintaining the improvement of the wireless sensor networks effectiveness requires the establishment of the energy-efficient and consistent connection. In order to increase the efficiency of routing algorithms in latest technology assisted WSNs, a novel method is developed. The gadgets with the lowest power consumption are carefully selected for the appropriate use and carefully managed. The goal of this research is to develop low-power routing algorithms for use in WSNs. Numerous sectors, including the military, the medical profession, surveillance of observing, public transportation sectors etc., can benefit from wireless sensor networks deployed with a large number of mobile nodes in a communications system. To examine data from nodes that are freely moving about the zone, we developed and simulated a wireless network system based on the Distributed Internet of Things. The proposed method of Intelligent Route Metric Analyzer (IRMA) improves upon the efficiency of the standard algorithm with regards to energy consumption. In addition, the ground wireless sensing design's ideal trajectory is identified using a realistic tempering approach. In the end, this concept is compared to similar ones like AODV and the findings reveal that it performs better with regard to of energy consumption, delay and other important metrics. According to the paper we analyzed, the primary objective was to devise a routing method that would sustain network operation for as long as feasible by decreasing the energy needed for different operations at each individual sensor node while keeping the total energy consumption of the nodes constant over their lifetimes. Finally, the study makes a contribution to the ongoing discussion of the difficulties in developing a routing protocol for WSN, taking into account the interdependency of different network factors. Median throughput, median network latency, and standardized route load are only few of the efficiency metrics addressed in this article.

Implementation of a hospital-wide massive transfusion protocol at a tertiary cancer center.

440 Background: Massive Transfusion Protocol (MTP) is a term used to describe a process to deliver blood products rapidly to treat life-threatening hemorrhage. MTPs follow a prescribed algorithm to efficiently replace blood products in set ratios. Three components of MTP include immediate availability of blood products, set types and ratios of blood products, and standardized roles for health care providers. MTP is associated with decreased morbidity and mortality compared to provider-driven decisions on blood transfusion. Methods: The Quality Improvement Assessment Board approved this initiative to standardize and create an MTP process outside of the Operating Room (OR). We assembled a multidisciplinary team to review and adopt evidence-based practices for MTP. A core group of content experts developed the relevant policy and protocol for activation and use of MTP in the Intensive Care Unit (ICU), Pediatric Intensive Care Unit (PICU), Emergency Room (ER), Interventional Radiology (IR) and inpatient floor. Simulations were held in these areas and Plan Do Study Act (PDSA) cycles used to improve the processes. Improvements included adjustment to the quantity and type of emergency release products immediately available, the timing of when platelets were delivered to providers and development of a ‘pull’ process for the Blood Bank to prepare additional blood products minimizing wastage. Following these changes, the MTP process became consistent and could be activated from all of the targeted locations. Each MTP episode was reviewed for outcomes and efficiency metrics. We held multidisciplinary care debriefings to review hemorrhagic events for areas of improvement. The OR had an existing process that was separate from this initiative, but will be aligned June 2023 and included in future analysis. Results: Baseline, from January 2022 to September 2022, 32 massive transfusion events occurred (3.5 per month). After implementation of the MTP protocol, from October 2022 to May 2023, the MTP protocol was activated 36 times (4.5 per month). There were 7 (22%) deaths at baseline and 5 (14%) after implementation. The average length of time from activation to receipt of first blood product was 26 minutes at baseline and 25 minutes with MTP. The average number of units administered at baseline included 7.7 units of red blood cells (RBC), 7.6 units fresh frozen plasma (FFP), 1 unit single donor platelets, and 2 units cryoprecipitate compared to after implementation of 6.9 units RBC, 6 units FFP, 1.6 units single donor platelets, and 3 units cryoprecipitate. The ICU was the most common MTP activation location (44%), followed by ER (19%), PICU (17%), the floor (14%) and IR (6%). Conclusions: It is feasible and safe to deploy an MTP throughout an institution. Preliminary data show a decrease in the percentage of patient deaths with use of MTP. Additional analysis are planned to evaluate the incremental improvements made with each PDSA cycle.

Improving the Efficiency of Electric Rolling Stock Operation Through the Use of Adaptive Filtering Methods for High Harmonic Current Components in Traction Drive Systems

The aim of this work is to investigate the efficiency of various adaptive filtering methods for higher harmonic components of stator currents in a vector-controlled traction drive system to improve the operation efficiency of electric rolling stock. The set goal was achieved by implementing an adaptive filtering block into the basic vector control system, which sequentially employs various adaptive filtering methods. The first harmonic component of stator currents was proposed to be used as the desired signal. An algorithm for determining the first harmonic component under conditions of varying supply voltage frequency was developed. Based on the simulation results, filtration efficiency metrics were established, indicating that the Wiener filter is the most effective for filtering higher harmonic components of stator currents. The most significant results include the algorithm for determining the first harmonic component of the stator phase current and the determination of an adaptive filtering algorithm with the highest convergence rate. The importance of these results lies in identifying the most effective adaptive filtering algorithm for traction drive operation, taking into account the operating modes of electric rolling stock. This will help reduce losses from higher harmonic components in the traction drive and, consequently, increase its energy efficiency.

1 Network Efficiency as Structural Reserve: Pre- And Post-Operative Associations Between Network Organization and Memory in Temporal Lobe Epilepsy

Objective:Memory impairment is a common comorbidity in individuals with temporal lobe epilepsy (TLE). Further, in medication-resistant epilepsy the frontline option, neurosurgical epileptogenic zone destruction, places memory at significant risk. Research has highlighted that TLE causes whole-brain network efficiency disruption, but it is not established how this may explain pre- and post-surgical cognition. Here we examine whether white matter structural network organization predicts pre-operative memory function and/or risk for post-operative memory decline.Participants and Methods:Patients with drug-resistant TLE were recruited from two epilepsy centers in a prospective longitudinal study. The pre-operative sample included 51 individuals with left TLE (L-TLE), 52 with right TLE (R-TLE), and 57 healthy controls who underwent T1- and diffusion-weighted MRI (dMRI), and neuropsychological tests of verbal and visual memory. Forty-four patients (n=21 L-TLE) subsequently underwent temporal lobe surgery (36 anterior temporal lobectomy; 7 stereotactic laser amygdalohippocampectomy; 1 amygdalohippocampectomy) and completed post-operative memory testing. Whole-brain connectomes were generated via diffusion tractography and analyzed using graph theory, focusing on network integration (path length) and specialization (transitivity). In the preoperative dataset, first we compared TLE versus controls with analysis of covariance (ANCOVAs) controlling for age. Next, linear regressions examined the association between memory scores and network efficiency between L-TLE, R-TLE and controls. In the post-operative sample, bivariate correlations examined the association between pre- to post-operative memory change and 1) global network efficiency and 2) asymmetry of mesial temporal efficiency (i.e., local efficiency of the hippocampal, parahippocampal, and entorhinal nodes). Finally, efficiency metrics were entered into stepwise regressions along with established predictors of memory decline.Results:Compared to controls, TLE showed longer path length (p < .05; ηp2 = .03) and lower transitivity (p = .01; ηp2 = .04). Pre-operatively, better verbal learning and memory were associated with both shorter path length (β = -0.23 to -0.32; psadjusted < .05) and increased transitivity (β = 0.20 to 0.31; psadjusted < .05). These associations were greater in L-TLE than R-TLE (i.e., a significant interaction; β = -0.29 to 0.25; psadjusted <.05). Post-operatively, global metrics predicted decline on list learning for LTLEs (rs = -.57 to .58; ps < .01), and were marginal on list recall (rs = -.42 to .40; ps < .10). Leftward asymmetry of mesial temporal local efficiency predicted greater decline across most verbal memory measures for L-TLE (rs -.47 to -59; psadjusted <.05), but not R-TLE. Asymmetry of mesial network efficiency uniquely explained at least 20 to 43% of the variance in list learning, recall, and story learning for L-TLE, outperforming hippocampal asymmetry and preoperative score (psadjusted <.05).Conclusions:Our findings suggest that global white matter network abnormalities contribute to verbal memory impairment pre-operatively and vulnerability to decline post-operatively in L-TLE. Asymmetry of a predefined mesial temporal sub-network may help predict post-operative memory function following left temporal lobe surgery, such that greater efficiency in the to-beresected mesial temporal network may be an important risk factor for decline. Our findings extend the importance of network approaches in TLE to include the relationships between neurobiological networks and memory function.

Effects of Alkoxy Chain Length and 1-Hydroxy Group on Anticolorectal Cancer Activity of 2-Bromoalkoxyanthraquinones.

KRAS and p53 are two of the most common genetic alterations associated with colorectal cancer. New drug development targeting these mutated genes in colorectal cancer may serve as a potential treatment avenue to the current regimen. The objective of the present study was to investigate the effects of alkoxy chain length and 1-hydroxy group on anticolorectal cancer activity of a series of 2-bromoalkoxyanthraquinones and corroborate it with their in silico properties. In vitro anticancer activity of 2-bromoalkoxyanthraquinones was evaluated against HCT116, HT29, and CCD841 CoN cell lines, respectively. Molecular docking was performed to understand the interactions of these compounds with putative p53 and KRAS targets (7B4N and 6P0Z). 2-Bromoalkoxyanthraquinones with the 1-hydroxy group were proven to be more active than the corresponding counterparts in anticancer activity. Among the tested compounds, compound 6b with a C3 alkoxy chain exhibited the most promising antiproliferation activity against HCT116 cells (IC50 = 3.828 ± 0.05 µM) and showed high selectivity for HCT116 over CCD841 CoN cells (SI = 45.47). The molecular docking reveals additional hydrogen bonds between the 1-hydroxy group of 6b and the proteins. Compound 6b has adequate lipophilicity (cLogP = 3.27) and ligand efficiency metrics (LE = 0.34; LLE = 2.15) close to the proposed acceptable range for an initial hit. This work highlights the potential of the 1-hydroxy group and short alkoxy chain on anticolorectal cancer activity of 2-bromoalkoxyanthraquinones. Further optimisation may be warranted for compound 6b as a therapeutic agent against colorectal cancer.

Characterization and Mitigation of Dimensional Effects on Core Loss in High-Power High-Frequency Converters

To date, LLC Resonant converters are deployed in many applications for improved efficiency, density, and reliability. With the introduction of WBG devices coupled with the soft switching feature, the switching frequency can be extended beyond MHz. With the significant increase in operating frequency, complicated magnetic components can be broken down into a cellular structure, each with a few number of turns. They can be easily implemented using 4-6 layers of PCB windings. Moreover, integrating the cellular cores using flux cancellation can further improve the power density. The proposed integrated magnetics can be automated in the manufacturing process. With the recently developed high-frequency core material, phenomena referred to as the dimensional effects on core loss are observed. The dimensional effects were discussed in the literature when using an unusually large core structure; however, at high excitation frequencies, it can be observed more frequently, particularly with integrated magnetic components. In this paper, we give an overview of the dimensional effects on core loss, namely flux crowding, dimensional resonance, and eddy loss, with the latest being a dominant factor in the 1 MHz transformer loss. We also introduce a practical method for the characterization of the dimensional effects to help engineers better their design of the high frequency converter. This includes measuring the ferrite material characteristics and evaluating the dimensional effects using Finite-Element Analysis. Finally, a case study of 1MHz 400-to-48 V converter design is presented, showing superiority in both power density and efficiency metrics compared to other reported literature from the same category. This shows the importance of including the dimensional effects and, specifically, the core eddy loss in the design procedure.

Seismic fragility functions for earthquake-induced landslide risk assessment using identified optimal earthquake intensity measures

Seismic fragility analysis can relate the probability of unsatisfactory performance of slopes with increasing intensity of ground motions, which is the basic component of landslide hazard risk assessment. Determination of the optimal earthquake intensity measure (IM) is one of important prerequisite for seismic fragility analysis. Although a small number of studies have already performed seismic fragility analysis of slopes, the optimal IM for seismic fragility analysis of slopes have not been discussed and researched. In the present study, 15 IMs and two different engineering demand parameters (EDPs), including factor of safety (FOS) and Newmark permanent displacement (NPD), are selected for probabilistic seismic demand and seismic fragility analysis of slopes. The metrics of correlation, practicality, efficiency, and proficiency are adopted to identify the optimal earthquake IMs among the studied 15 IMs for two EDPs in seismic fragility analysis of slopes respectively. The results have found that the optimal IM is different for slope fragility analysis when different EDPs are selected. Acceleration spectrum intensity is the most optimal IM among the examined 15 IMs for seismic fragility analysis of slopes when FOS is adopted as the EDP. When NPD is selected as the EDP, root mean square of acceleration and velocity spectrum intensity are in general more appropriate than other IMs. In the last, the seismic fragility curves with regard to two EDPs and corresponding optimal IMs are derived for interpreting more properly slope seismic performance and earthquake-induced landslide risk.