Cost Efficiency Research Articles

The effect of particle geometry on squirming in a heterogeneous medium

Biological microorganisms and artificial micro-swimmers often locomote in heterogeneous viscous environments consisting of networks of obstacles embedded into viscous fluid media. In this work, we use the squirmer model and present a numerical investigation of the effects of shape on swimming in a heterogeneous medium. Specifically, we analyse the microorganism's propulsion speed as well as its energetic cost and swimming efficiency. The analysis allows us to probe the general characteristics of swimming in a heterogeneous viscous environment in comparison with the case of a purely viscous fluid. We found that a spheroidal microorganism always propels faster, expends less energy and is more efficient than a spherical microorganism in either a homogeneous fluid or a heterogeneous medium. Moreover, we determined that above a critical eccentricity, a spheroidal microorganism in a heterogeneous medium can swim faster than a spherical microorganism in a homogeneous fluid. Based on an analysis of the forces acting on the squirmer, we offer an explanation for the decrease in the squirmer's speed observed in heterogeneous media compared with homogeneous fluids.

Pengendalian Biaya Tenaga Kerja dalam Organisasi Sektor Publik: Studi tentang Pengelolaan Pegawai Tidak Tetap (PTT) dan Alternatif Outsourcing

This article discusses the management of labor costs in the public sector with a focus on the management of non-permanent employees (PTT) and outsourcing alternatives. This research highlights problems in personnel management, especially employee expenditure costs that must be borne by government agencies. This study reviews cost control systems, labor costs, and methods for managing honorary staff costs in government. The findings show that outsourcing is more efficient than recruiting PTT or temporary workers as a solution to control labor costs. Additionally, this paper recommends Activity Based Costing (ABC) method for public sector organizations to provide valuable information related to detailed cost data. This research shows that decentralizing recruitment in local governments can improve efficiency, effectiveness and fairness, but can also lead to corruption and disparities in the distribution of human resources. Cost control, outsourcing, and the use of Activity-Based Costing (ABC) can help public sector organizations manage costs and increase operational efficiency. To achieve better governance, operational efficiency and effective cost control, public sector organizations need to consider the various strategies and methods described in this research.

Characterization and performance evaluation of in-house ultrafiltration membrane coupled with photocatalysis for 17α-methyltestosterone hormone removal.

17α-methyltestosterone (MT) hormone is a synthetic androgenic steroid hormone utilized to induce Nile tilapia transitioning for enhanced production yield. This study specifically focuses on the removal of MT through the utilization of photocatalytic membrane reactor (PMR), which employs an in-house polyvinylidene fluoride (PVDF) ultrafiltration membrane modified with 1% nanomaterials (either TiO2 or α-Fe2O3). The molecular weight cut-off (MWCO) of the in-house membrane falls within the ultrafiltration range. Under UV95W radiation, the PMR with PVDF/TiO2 and PVDF/α-Fe2O3 membranes achieved 100% MT removal at 140 and 160 min, respectively. The MT removal by the commercial NF03 membrane was only at 50%. In contrast, without light irradiation, the MT removal by all the membranes remained unchanged after 180 min, exhibiting lower performance. The incorporation of TiO2 and α-Fe2O3 enhanced water flux and MT removal of the membrane. Notably, the catalytic activity was limited by the distribution and concentration of the catalyst at the membrane surface. The water contact angle did not correlate with the water flux for the composited membrane. The degradation of MT aligned well with Pseudo-first-order kinetic models. Thus, the in-house ultrafiltration PMR demonstrated superior removal efficiency and lower operational costs than the commercial nanofiltration membrane, attributable to its photocatalytic activities.

Adsorption of the Antibiotic Roxithromycin on Low-Cost Food Waste Materials: Batch and Column Studies

Antibiotics are widely utilized for a variety of medical conditions. Antibiotic residues in wastewater are dangerous to all living beings. Antibiotics remain in the wastewater environment when general treatment plant technologies are employed. The literature has numerous techniques for getting rid of antibiotics. Compared to other techniques for removing contaminants in the solution environment, the adsorption method is preferred due to its benefits, such as ease of use, high efficiency, and low cost. The study investigated using the green walnut shell (GWS), a natural sorbent, as an adsorbent to discharge roxithromycin (ROX) antibiotics from the solution medium. Adsorption conditions were studied in batch and continuous systems. pH, adsorbent amount, interaction time, sorbate concentration, and salt effect parameters were investigated in the batch system. The data obtained were calculated with kinetic and isotherm models. The adsorption process has been based on the so-called pseudo-second-order kinetic model. GWS was characterized using SEM and FTIR techniques. The amount of absorbent, flow rate, and breakdown in the continuous system were explored. In the batch system, the adsorption equilibrium was set up at the solution's original pH with 0.1 g of adsorbent in 40 minutes, and 78% ROX removal was achieved. The optimum flow rate and adsorbent amount in the continuous system were determined as 0.1 mL/min and 0.3 g, respectively.

The Power of Artificial Intelligence in Digital Marketing

In the dynamic landscape of digital marketing, businesses are constantly challenged to engage their target audience effectively amidst a plethora of information and choices. Artificial Intelligence (AI) emerges as a pivotal solution in addressing these challenges, revolutionizing traditional marketing approaches through its diverse applications and capabilities. This paper explores the multifaceted role of AI in digital marketing, drawing upon insights from existing literature and practical examples. AI empowers marketers with enhanced customer insights, predictive analytics, and automated campaign optimization, enabling them to tailor marketing strategies to individual preferences and behaviors. Moreover, AI-driven tools such as chatbots and virtual assistants streamline customer interactions, while also offering cost efficiencies and improved return on investment (ROI). Despite the undeniable benefits, the integration of AI in digital marketing raises concerns regarding data privacy, algorithmic bias, and ethical considerations. Nevertheless, by navigating these challenges responsibly and adopting ethical frameworks, businesses can leverage AI to unlock new opportunities for growth, differentiation, and sustainable success in the digital era

AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning

Abstract Large pretrained language models are widely used in downstream NLP tasks via task- specific fine-tuning, but such procedures can be costly. Recently, Parameter-Efficient Fine-Tuning (PEFT) methods have achieved strong task performance while updating much fewer parameters than full model fine-tuning (FFT). However, it is non-trivial to make informed design choices on the PEFT configurations, such as their architecture, the number of tunable parameters, and even the layers in which the PEFT modules are inserted. Consequently, it is highly likely that the current, manually designed configurations are suboptimal in terms of their performance-efficiency trade-off. Inspired by advances in neural architecture search, we propose AutoPEFT for automatic PEFT configuration selection: We first design an expressive configuration search space with multiple representative PEFT modules as building blocks. Using multi-objective Bayesian optimization in a low-cost setup, we then discover a Pareto-optimal set of configurations with strong performance-cost trade-offs across different numbers of parameters that are also highly transferable across different tasks. Empirically, on GLUE and SuperGLUE tasks, we show that AutoPEFT-discovered configurations significantly outperform existing PEFT methods and are on par or better than FFT without incurring substantial training efficiency costs.

New Frontiers in Breast Cancer Imaging: The Rise of AI

Artificial intelligence (AI) has been implemented in multiple fields of medicine to assist in the diagnosis and treatment of patients. AI implementation in radiology, more specifically for breast imaging, has advanced considerably. Breast cancer is one of the most important causes of cancer mortality among women, and there has been increased attention towards creating more efficacious methods for breast cancer detection utilizing AI to improve radiologist accuracy and efficiency to meet the increasing demand of our patients. AI can be applied to imaging studies to improve image quality, increase interpretation accuracy, and improve time efficiency and cost efficiency. AI applied to mammography, ultrasound, and MRI allows for improved cancer detection and diagnosis while decreasing intra- and interobserver variability. The synergistic effect between a radiologist and AI has the potential to improve patient care in underserved populations with the intention of providing quality and equitable care for all. Additionally, AI has allowed for improved risk stratification. Further, AI application can have treatment implications as well by identifying upstage risk of ductal carcinoma in situ (DCIS) to invasive carcinoma and by better predicting individualized patient response to neoadjuvant chemotherapy. AI has potential for advancement in pre-operative 3-dimensional models of the breast as well as improved viability of reconstructive grafts.

EnTdecker - A Machine Learning-Based Platform for Guiding Substrate Discovery in Energy Transfer Catalysis.

Due to the magnitude of chemical space, the discovery of novel substrates in energy transfer (EnT) catalysis remains a daunting task. Experimental and computational strategies to identify compounds that successfully undergo EnT-mediated reactions are limited by their time and cost efficiency. To accelerate the discovery process in EnT catalysis, we herein present the EnTdecker platform, which facilitates the large-scale virtual screening of potential substrates using machine-learning (ML) based predictions of their excited state properties. To achieve this, a data set is created containing more than 34,000 molecules aiming to cover a vast fraction of synthetically relevant compound space for EnT catalysis. Using this data predictive models are trained, and their aptitude for an in-lab application is demonstrated by rediscovering successful substrates from literature as well as experimental validation through luminescence-based screening. By reducing the computational effort needed to obtain excited state properties, the EnTdecker platform represents a tool to efficiently guide substrate selection and increase the experimental success rate for EnT catalysis. Moreover, through an easy-to-use web application, EnTdecker is made publicly accessible under entdecker.uni-muenster.de.

Research on Hybrid Rectifier for High Power Electrolytic Hydrogen Production Based on Modular Multilevel Converter

Aiming at the problem that silicon-controlled rectifiers (SCR) and pulse width modulation (PWM) rectifiers cannot balance high power levels, high hydrogen production efficiency, and high grid connected quality in the current research on rectifier power supplies for electrolytic hydrogen production, a new hybrid rectifier topology based on a modular multilevel converter (MMC) is proposed. The hybrid topology integrates a silicon-controlled rectifier (SCR) with an auxiliary power converter, wherein the SCR is designated as the primary power source for electrolytic hydrogen production. The auxiliary converter employs a cascaded modular multilevel converter (MMC) and an input-series-output-parallel (ISOP) phase-shifted full-bridge (PSFB) arrangement. This configuration allows the auxiliary converter to effectively mitigate AC-side harmonics and minimize DC-side ripple, concurrently transmitting a small amount of power. The effectiveness of the hybrid rectifier in achieving low ripple and harmonic distortion outputs was substantiated through hardware-in-the-loop experiments. Notably, the hybrid topology is characterized by its enhanced electric-to-hydrogen conversion efficiency, elevated power density, cost efficiency, and improved grid compatibility.

Evolutionary game analysis on collaborative logistics diffusion considering diminishing marginal utility under dynamic productivity

Reducing carbon emissions in the transportation sector is crucial for sustainable practices, with collaborative logistics diffusion playing a significant role in promoting energy-saving and emission-reduction strategies. This paper introduces an evolutionary game model to analyze collaboration between manufacturers and logistics service providers (LSPs), using the Hotelling quadratic curve model to measure the diminishing marginal utility of basic profits. Initially, a static strategy leads to a cyclic evolution without reaching the evolutionary stable strategy (ESS), reflecting competitive dynamics under static productivity conditions where neither party gains a lasting advantage, leading to periodic adjustments in strategies. The Cobb-Douglas function models a dynamic productivity strategy that regulates the extra benefits of collaborative logistics and penalties for 'free-riding.' This strategy demonstrates an inward spiral convergence towards the ESS, showing that adaptive mechanisms enable manufacturers and LSPs to align interests and achieve equilibrium. Enhanced benefits from collaborative logistics promote a stable and cooperative ecosystem, discouraging free-riding and enhancing cost efficiency. Moreover, increased investment by manufacturers not only strengthens their position but also fosters an environment conducive for LSP participation, expediting collaborative logistics diffusion and minimizing potential losses.

Identification of Surgeon Outliers to Improve Cost Efficiency: A Novel Use of the Centers for Medicare and Medicaid Quality Payment Program

BackgroundBundled payment programs for total joint arthroplasty (TJA) have become popular among both private and public payers. Because these programs provide surgeons with financial incentives to decrease costs through reconciliation payments, there is an advantage to identifying and emulating cost-efficient surgeons. The objective of this study was to utilize the Centers for Medicare and Medicaid Services (CMS) Quality Payment Program (QPP) in combination with institutional data to identify cost-efficient surgeons within our region and, subsequently, identify cost-saving practice patterns. MethodsData was obtained from the CMS QPP for total knee (TKA) and total hip (THA) surgeons within a large metropolitan area from January 2019 to December 2021. A simple linear regression determined the relationship between surgical volume and cost-efficiency. Internal practice financial data determined whether patients of identified surgeons differed with respect to x-ray visits, physical therapy visits, out-of-pocket payments to the practice, and whether surgery was done in hospital or surgical center settings. ResultsThere were four TKA and three THA surgeons who were cost-efficiency outliers within our area. Outliers and non-outlier surgeons had patients who had similar BMI, ASA, and age-adjusted CCI scores. Patients of these surgeons had fewer x-ray visits for both TKA and THA (1.06 versus 1.11, P < 0.001; 0.94 versus 1.15, P < 0.001) and lower out-of-pocket costs ($86.10 versus $135.46, P < 0.001; $116.10 versus $177.40, P < 0.001). If all surgeons performing > 30 CMS cases annually within our practice achieved similar cost-efficiency, the savings to CMS would be $17.2 million for TKA alone ($75,802,705 versus $93,028,477). ConclusionThe CMS QPP can be used to identify surgeons who perform cost-efficient surgeries. Practice patterns that result in cost savings can be emulated to decrease the cost curve, resulting in reconciliation payments to surgeons and institutions and cost savings to CMS.

The interaction between corruption, bank cost efficiency and economic development in Italy

In this paper, we assess the interaction between corruption, bank cost efficiency, and economic development. Existing literature on the subject normally focuses separately on the link between corruption, financial development, and growth, and finds ambiguous results, not distinguishing between different types of credit institutions. We use Italian disaggregated data from 2004 to 2012, considering cooperative and non-cooperative banks, to determine whether controlling corruption affects banks’ cost efficiency in Italian provinces and promotes economic development. Utilizing a panel autoregressive model, we find that controlling corruption magnifies the positive impact of banks’ cost efficiency on economic development. Evidence of a positive effect of the interaction is observed, particularly in the case of non-cooperative banks and for higher values of the Control of Corruption Index.

CRAMP: Clustering-based RANs association and MEC placement for delay-sensitive applications

With advancements in networking technology and ubiquitous computing, there has been a significant increase in the number of edge devices and delay-sensitive applications. To facilitate efficient processing, mobile edge computing (MEC) technology provides resources through MEC servers, which are deployed at the radio access networks (RANs) of 5G networks. However, these MEC servers possess a limited amount of resources, making their effective management of these resources a critical challenge. This is due to the uneven distribution of resource utilization, where some resources become overutilized while others remain underutilized. Addressing the issue above while simultaneously satisfying user requirements for delay-sensitive applications poses a significant challenge at the edge. In this paper, we propose a clustering-based efficient RANs association and MEC server placement model to tackle this challenge. Our primary objective is to minimize MEC server deployment costs while ensuring that the delays of these applications are effectively managed. We propose a greedy algorithm called the clustering-based radio access networks association and mobile edge computing placement (CRAMP) algorithm, which determines the optimal location of MEC servers to associate with RANs. Simulation results demonstrate that our proposed algorithm outperforms existing approaches regarding cost efficiency and delay management.

Azithromycin removal using pine bark, oak ash and mussel shell

Adsorption is considered an interesting option for removing antibiotics from the environment because of its simple design, low cost, and potential efficiency. In this work we evaluated three by-products (pine bark, oak ash, and mussel shell) as bio-adsorbents for the antibiotic azithromycin (AZM). Furthermore, they were added at doses of 48 t ha-1 to four different soils, then comparing AZM removal for soils with and without bio-adsorbents. Batch-type experiments were used, adding AZM concentrations between 2.5 and 600 μmol L-1 to the different bio-adsorbents and soil + bio-adsorbent mixtures. Regarding the bio-adsorbents, oak ash showed the best adsorption scores (9600 μmol kg-1, meaning >80% retention), followed by pine bark (8280 μmol kg-1, 69%) and mussel shell (between 3000-6000 μmol kg-1, 25-50% retention). Adsorption data were adjusted to different models (Linear, Freundlich and Langmuir), showing that just mussel shell presented an acceptable fitting to the Freundlich equation, while pine bark and oak ash did not present a good adjustment to any of the three models. Regarding desorption, the values were always below the detection limit, indicating a rather irreversible adsorption of AZM onto these three by-products. Furthermore, the results showed that when the lowest concentrations of AZM were added to the not amended soils they adsorbed 100% of the antibiotic, whereas when the highest concentrations of AZM were spread, the adsorption decreased to 55%. However, when any of the three bio-adsorbents was added to the soils, AZM adsorption reached 100% for all the antibiotic concentrations used. Desorption was null in all cases for both soils with and without bio-adsorbents. These results, corresponding to an investigation carried out for the first time for the antibiotic AZM, can be seen as relevant in the search of low-cost alternative treatments to face environmental pollution caused by this emerging contaminant.

Experimental study on a compact solar driven two-stage humidification-dehumidification desalination system with shared dehumidifier

The low-cost and low-grade solar driven humidification-dehumidification desalination is considered a promising process for small and medium capacity freshwater production. Nevertheless, the enhancement of the unit volume water production rate is hindered by the disparity in mass transfer coefficient and the incongruity between heat and mass flow rate in the humidification and dehumidification process. This paper proposes a compact solar driven two-stage humidification-dehumidification desalination system with shared dehumidifier. It contains two humidifiers and a dehumidifier, wherein the dehumidifier is stacked between the vertically placed humidifiers. The circulating air is humidified by hot seawater in the top and bottom humidifiers respectively, and then goes to the middle dehumidifier to condense via a freshwater absorption process. Its design scheme and operating principle are introduced and a mathematical model describing its internal energy and mass flow process is established. An experimental setup with compound parabolic concentrator areas of 6 m2 is developed, and a sequence of experiments is carried out. The results indicate that its freshwater productivity can reach 29.6 kg/h with a gained output ratio of 0.84 when the feed seawater temperature is 80 ℃. Due to its compact structure, the freshwater production per volume unit per day reaches up to 224.1 kg/(day⋅m3). This work may promote the development of solar humidification and dehumidification systems towards low cost and high efficiency.

The Promises and Challenges toward Mass Customization of Healthcare Services

The healthcare industry is confronted with the challenge to offer an increasing variety of healthcare services while in the meantime controlling rapidly increasing healthcare costs. Mass customization has been proven to be an effective strategy to fulfill customers’ individual specific needs with high efficiency and low cost in the manufacturing industry. This paper investigates the theoretical feasibility and practical applicability of adopting mass customization as a conceptual framework for designing a healthcare service delivery system. The nature of healthcare delivery systems and their evolution are discussed relative to those of manufacturing systems. Recent research in personalized medicine, consumer-driven healthcare, consumer healthcare informatics, and integrated healthcare delivery is reviewed as enabling technologies towards mass customization of healthcare services. By synthesizing these scattered efforts in different streams of literature, this paper concludes that mass customization can contribute to the redesign of healthcare service systems, and delineates a roadmap for future research.

Size-controlled synthesis of copper oxide nanoparticles with a supercritical hydrothermal synthesis method

Supercritical hydrothermal synthesis (SCHS) is a promising technique for the preparation of copper oxide nanoparticles with fast reaction rate, green efficiency and low cost. In this paper, nanoparticles of copper oxide ranging from 53.0 to 94.0 nm were synthesized by supercritical hydrothermal method. The effects of different precursor concentrations, alkali addition, reaction pressure, reaction temperature and reaction time on the particle size, morphology and particle uniformity of copper oxide nanoparticles were also investigated, and the mechanism of each parameter was analyzed. The growth kinetic equation of copper oxide nanoparticles under supercritical hydrothermal conditions was established by analyzing the influence laws of temperature and reaction time on the grain size of the products. Further, the growth activation energy and the growth mechanism controlled by the grain surface process was obtained. The formation mechanism of copper oxide nanoparticles in rod form was analyzed by TEM results.

Simulation and reconstruction for 3D elastic wave using multi-GPU and CUDA-aware MPI

3D finite-difference time-domain numerical simulation and reconstruction based on the domain decomposition technique are essential parts of high-performance computation for reverse-time migration and full-waveform inversion. However, the low GPU utilization in computing for small-sized models and the tremendous memory consumption for large-sized models may result in low computational efficiency and high memory costs. This paper proposes a contiguous memory management (CMM) method and a variable-order wavefield reconstruction (VWR) method. The CMM allocates the memory of many small-sized arrays used for MPI communications on a larger-sized contiguous memory block, which aims to reduce the number of MPI communications between subdomains and improve the communication bandwidth, thus reducing the MPI time overhead and improving the GPU utilization. Meanwhile, the VWR can flexibly set the number of layers of boundary wavefield used for source wavefield reconstruction according to the host memory capacity and accuracy requirements. Since one layer of boundary wavefield could be stored using the VWR, the memory consumption of host memory can be significantly alleviated. Numerical experiments show that GPU utilization in computing for the model with a size of 1213 can be improved from 25% to 90% using the CMM method, and the VWR method can reduce memory consumption by about 86% while maintaining good accuracy in wavefield reconstruction. In addition, the issue of how to obtain a domain decomposition scheme with optimal performance is discussed in this paper.

Iranian Traditional Medicine (ITM) and Natural Remedies for Treatment of the Common Cold and Flu.

Traditional Iranian medicine is usually used for both prevention and relief of cold and flu symptoms in China, Iran, and many other Asian countries all over the world. There are 4 kinds of influenza viruses. Unlike type B, which may cause seasonal epidemics, type A viruses can cause pandemics, and influenza C may lead to mild human infection with little public health effects. A literature review was done by using multiple databases such as ISI Web of knowledge, PubMed, Science Direct and Google Scholar. The most notable antiviral medicinal plants for flu and cold are honeysuckle flowers, thyme leaf, green chiretta, andrographis, peppermint oil and leaf and calendula. The most important expectorant medicinal plants for cold and flu are snake root, tulsi, licorice root, slippery elm, clove, and sage leaf. Recommended immunostimulant medicinal plants for cold and flu are eucalyptus, Echinacea root, ginseng, garlic, slippery elm, marshmallow, Usnea lichen, Isatis root, ginger root, and myrrh resin. Iranian traditional medicine, which is one of the oldest schools of traditional medicine, is one of the main concepts of disease and health, and it can be considered as an important complementary and alternative medicine, as in some cases, modern medicine has many side effects, low efficiency, and high costs. Medicinal plants and herbs, which are included in many traditional systems, have significant and promising bioactive components in organic life.

Vision detection and path planning of mobile robots for rebar binding

AbstractFocused on the problems of cumbersome operation, low efficiency, and high cost in the traditional manual rebar binding process, we propose a mobile robot vision detection and path‐planning method for rebar binding to realize automated rebar binding by combining deep learning and path‐planning technology. A MobileNetV3‐SSD rebar binding crosspoints recognition model is built based on TensorFlow deep learning framework, and a crosspoints localization method combining control factor α and feature projection curve is introduced to achieve the localization of unbound crosspoints. In addition, A back‐and‐forth path‐planning algorithm with priority constraints combined with dead zone escape algorithm based on improved A* is proposed to achieve complete coverage path planning of the working area and path transfer of the dead zone. In the field test of the robot prototype, the classification accuracy and localization accuracy reached 94.40% and 90.49%, and the robot was able to reach complete coverage path planning successfully. The experimental results show that the visual detection method can achieve fast, noncontact and intelligent recognition of rebar binding crosspoints, which has good robustness and application value. At the same time, the proposed path‐planning method has higher efficiency in the execution of robot complete coverage path planning, and meets the basic requirements of path planning for rebar binding process.