Low Cost Operation Research Articles

The downtrending cost of robotic bariatric surgery: a cost analysis of 47,788 bariatric patients.

The surgical robot is assumed to be a fixed, indirect cost. We hypothesized rising volume of robotic bariatric procedures would decrease cost per patient over time. Patients who underwent elective, initial gastric bypass (GB) or sleeve gastrectomy (SG) for morbid obesity were selected from Florida Agency for Health Care Administration database from 2017 to 2021. Inflation-adjusted cost per patient was collected. Cost-over-time ($/patientyear) and change in cost-over-time were calculated for open, laparoscopic, and robotic cases. Linear regression on cost generated predictive parameters. Density plots utilizing area under the curve demonstrated cost overlap. Among 76 hospitals, 11,472 bypasses (223 open, 6885 laparoscopic, 4364 robotic) and 36,316 sleeves (26,596 laparoscopic, 9724 robotic) were included. Total cost for robotic was approximately 1.5-fold higher (p<0.001) than laparoscopic for both procedures. For GB, laparoscopic had lower total ($15,520) and operative ($6497) average cost compared to open (total $17,779; operative $9273) and robotic (total $21,756; operative $10,896). For SG, laparoscopic total cost was significantly less than robotic ($10,691 vs. $16,393). Robotic GB cost-over-time increased until 2021, when there was a large decrease in cost (-$944, compared with 2020). Robotic SG total cost-over time fluctuated, but decreased significantly in 2021 (-$490 compared with 2020). While surgical costs rose significantly in 2020 for bariatric procedures, our study suggests a possible downward trend in robotic bariatric surgery as total and operative costs are decreasing at a higher rate than laparoscopic costs.

Thermodynamic Modeling of a Supercritical Steam Rankine Cycle for an Integrated CSP-Nuclear System

Renewable technologies using solar input have varied electrical power production during periods of low solar irradiance caused by cloud coverage, seasonal changes, and time of day. Nuclear power plants can load follow, but due to low operating costs and high fixed costs, this capability is often not financially appealing. Implementing thermal energy storage (TES) within a synergistic solar and nuclear power cycle allows for storage during low demand periods and increased power production during high demand periods, effectively increasing dispatchability. In this paper, we examine the thermodynamic performance of an integrated system that includes concentrating solar power (CSP) and a lead-cooled fast reactor (LFR). These technologies are selected due to their similar operating temperatures, allowing for utilization of established TES technologies. Both the CSP and LFR system produce thermal power that is sent to a supercritical steam-Rankine cycle (SSRC). The SSRC model is designed to be implemented into a larger integrated energy system (IES) which contains multiple communicating models. The IES generates CSP and LFR heat profiles then utilizes the SSRC to output calculated metrics including power generation, heat rejection, and cycle efficiency.

Ensuring cybersecurity for industrial networks: A solution for ARP-based MITM attacks

The increased adoption of the Internet Protocol (IP) in ICSs has made these systems vulnerable to the same security risks that are present in traditional IT environments. The legacy nature of ICSs and their unique operational requirements make them vulnerable to security threats that are different from those in IT environments. In this paper, we describe a protocol, named ArpON, which is able to wipe out in quasi real time any ARP cache poisoning attempt, thus making it ineffective. Contrarily to solutions presented in the literature for contrasting ARP cache poisoning, ArpON incurs in low operational costs, is backward compatible, transparent to the ARP protocol and does not use any HW feature nor cryptography functionality. We also model and validate ArpON in the OMNET + + network simulator. The simulation results show that ArpON is effective in avoiding ARP poisoning, and its communication overhead is negligible with respect to classical ARP protocol.

Is decentralization sustainable in the bitcoin system?

The Bitcoin system is a decentralized monetary system in that any participant can potentially verify and record transactions onto a public ledger. Using a simple model, we show that the number of miners could be negatively (positively) related to the expected rewards if miners with low-operating costs also have less (more) severe financing constraints. We characterize the possibility of miners with low-operating (high-operating) costs facing less (more) severe financing constraints as large-size (small-size) miners. The empirical evidence supports the model's large- and small-size miner characterization prediction. The negative association between the number of miners and expected rewards is attributable to Bitcoin's exchange rate. Collectively, our evidence suggests that even though the Bitcoin system is designed as an egalitarian system, it is likely to evolve into a near-centralized system because of economic forces.

Modeling and Comparing the Total Cost of Ownership of Passenger Automobiles with Conventional, Electric, and Hybrid Powertrains

&lt;div&gt;The global automotive industry’s shift toward electrification hinges on battery electric vehicles (BEV) having a reduced total cost of ownership compared to traditional vehicles. Although BEVs exhibit lower operational costs than internal combustion engine (ICE) vehicles, their initial acquisition expense is higher due to expensive battery packs. This study evaluates total ownership costs for four vehicle types: traditional ICE-based car, BEV, split-power hybrid, and plug-in hybrid. Unlike previous analyses comparing production vehicles, this study employs a hypothetical sedan with different powertrains for a more equitable assessment. The study uses a drive-cycle model grounded in fundamental vehicle dynamics to determine the fuel and electricity consumption for each vehicle in highway and urban conditions. These figures serve a Monte Carlo simulation, projecting a vehicle’s operating cost over a decade based on average daily distance and highway driving percentage. Results show plug-in hybrids generally offer the most economical choice. Due to the BEVs’ heavier weight and battery cost, they only become more cost-effective than plug-in hybrids after 160 km daily travel, associated with only a small percentage of drivers in the United States. Nevertheless, they remain cheaper than conventional vehicles for most distances. The study also investigates the effects of government subsidies, battery cost, and weight on overall expenses for each powertrain. It concludes that opting for less expensive, albeit heavier batteries would generally reduce EV ownership costs for consumers.&lt;/div&gt;

Performance of a Double-Filter-Medium Tandem Membrane Bioreactor with Low Operating Costs in Domestic Wastewater Treatment

To reduce the operating costs of conventional membrane bioreactors (MBRs) and improve the stability and quality of the dynamic membrane bioreactor (DMBR) effluent, a homemade inexpensive filter cloth assembly was connected to an up-flow ultra-lightweight-medium filter (UUF) in lieu of expensive membrane modules to form a double-filter-medium tandem (DT)-MBR. DT-MBR was used to treat domestic wastewater, and its removal efficiencies for chemical oxygen demand, ammonia nitrogen, total nitrogen, and total phosphorus were similar to those of aerobic MBR, with average removal rates of 91.1%, 98.4%, 15.1%, and 50.7%, respectively. The average suspended solid (SS) of the final effluent was 5.6 mg∙L−1, and the filter cloth assembly played a leading role in SS removal, with an average removal rate of 86.0% and a relatively stable removal effect with little impact via backwashing. The activated sludge zeta potential, flocculation and sedimentation properties, particle size distribution, microbial compositions, extracellular polymeric substances (EPS), and filtration resistance of the cake layer were analyzed; it was found that the cake layer, which can also be called the dynamic membrane (DM), had an excellent filtration performance. However, the DM theory could not reasonably explain why the effluent quality of the filter cloth assembly maintained good stability even after backwashing. The real reason must be related to the sieving of cloth pores. Therefore, the concept of an in situ autogenous static membrane (ISASM) was proposed. With low operating costs and good and stable effluent quality, DT-MBR is a desirable alternative to the traditional MBR.

Optimization Design of Closed-Loop Thermosyphons: Experimentation and Computational Fluid Dynamics Modeling

The well-established practice of integrating heat pipes into thermosyphons is recognized for its efficacy in achieving energy savings. This integration facilitates heat transfer and fluid circulation without requiring additional pumps or energy input, resulting in reduced consumption, lowered operational costs, and an overall improvement in system efficiency. This research explores the energy-saving potential of closed-loop thermosyphons, with a specific focus on their integration in latent heat-based heat pipe technologies in industrial settings. The study systematically investigates the influence of thermosyphon orientation on energy efficiency through a combination of experiments and computational fluid dynamics (CFD) simulations. Thereby, it results in superior heat transfer rates in forced convection scenarios. A closed-loop thermosyphon heat exchanger undergoes evaluation in three panel installation configurations relative to the ground, taking into consideration factors including copper diameters, coolants (with or without R410a), and temperature conditions. CFD validation identifies an efficient thermosyphon design—a panel oriented perpendicularly to the ground and filled with R410a refrigerant at 90 °C. It utilizes a 19.05 mm copper tube for forced convection. This optimized design demonstrates a commendable heat transfer rate of 1485 W and a heat transfer coefficient of 1252 W/(m2·K), significantly enhancing thermal process efficiency and resulting in notable energy savings.

Design and evaluation of architectural framework for a secured local energy market model based on distributed ledger technologies

AbstractBlockchain‐based local energy markets have been proposed in recent years to provide a market platform for local prosumers and consumers to exchange their energy in a secured, transparent and tamper‐proof manner. However, there are still some challenges regarding the scalability of blockchain to handle high computational models/algorithms/contracts as this may result in the extension of the block size of the blockchain network and very high gas costs. Also, there is still the problem of transparency as regards General Data Protection Regulation because the full visibility of data in the blockchain may collide with privacy in some settings. A framework is presented that combines the on‐chain features of blockchain with trusted execution environments to develop a transparent, tamper‐resistant, low operation cost, scalable and resilient hybrid model architecture for local electricity trading. The model architecture was simulated in German community case scenarios for a varying number of prosumers and consumers to show its applicability. The simulation results show that the model was able to solve the scalability problem of blockchain for the local energy market application as the market model is run in a trusted environment where the integrity of the model can be verified by the participants.

Stochastic energy management of a microgrid incorporating two-point estimation method, mobile storage, and fuzzy multi-objective enhanced grey wolf optimizer

In this study, the stochastic energy management, and scheduling of a renewable microgrid involving energy sources and dynamic storage is performed considering energy resource and demand uncertainties and demand response (DR) using the two-point estimation method (2 m + 1 PEM). The three-dimensional objective function is defined as maximizing the renewable hosting capacity and minimizing the operation cost, and emission cost minimization. The decision variables include installation location and size of the renewable resources and mobile energy storage system (MESS), determined using a multi-objective enhanced grey wolf optimizer (MOEGWO) improved based on the logistic chaotic mapping integrated with fuzzy decision-making approach. The simulations are implemented for several cases of employing MESS, DR, and uncertainties to investigate the proposed approach's efficacy. The MOEGWO performance is confirmed to solve the ZDT and CEC'09 functions according to some well-known algorithms. Then, the performance of the MOEGWO is evaluated on the stochastic energy management and scheduling of the renewable microgrid. The results indicate that considering the dynamic MESS causes reducing the operation and emission costs by 23.34% and 34.78%, respectively, and increasing the renewable hosting capacity by 7.62% in contrast to using the static MESS. Also, the stochastic problem-solving considering uncertainties showed that operation and emission costs are raised, the renewable hosting capacity is decreased, and the uncertainty impact is reduced in the condition of DR application. So, the results validated the proposed methodology's effectiveness for minimizing the operation and emission costs and maximizing the renewable hosting capacity. Moreover, the superior capability of the MOEGWO is confirmed in comparison with the multi-objective particle swarm optimization to obtain lower operation and emission costs and higher renewable hosting capacity.

Impact of Technological Progress on Energy Consumption in the Hotel Industry in North-East Europe (Latvia)

The impact of technology on energy consumption in the hotel business is significant (Demir &amp; Bahar, 2021). Technological advancements in the hotel industry have led to the development of energy-efficient systems and devices, such as smart thermostats, LED lighting, and occupancy sensors (Toscano et al., 2019). These technologies allow hotels to better monitor and control their energy usage, leading to reduced energy consumption and lower operational costs. By implementing these energy-efficient technologies, hotels can not only reduce their carbon footprint but also improve their overall sustainability and attract environmentally conscious guests. Additionally, technology has also enabled hotels to collect and analyze data on energy usage, allowing them to identify areas of high consumption and implement targeted strategies for energy optimization. Furthermore, technology has also enhanced guest experiences by allowing guests to have better control over their room's energy usage through features like smart room controls and mobile apps. These technological advancements not only benefit the environment but also contribute to cost savings for hotels. The integration of energy-saving technology in hotels has been proven to be beneficial not only for the environment but also for the financial sustainability of hotel businesses. By implementing energy-efficient technologies, hotels can reduce their operational costs and minimize their environmental impact (Wang, 2016). In conclusion, technology plays a crucial role in reducing energy consumption in the hotel business (Toscano et al., 2019). Keywords Energy Consumption, Hotel Business, Hospitality Industry, Gas Energy, Electric Energy, Advanced Technology, Energy Efficient Systems.

Construction of test strips for lung cancer detection based on aptamers

Lung cancer is the most common malignancy worldwide. Early diagnosis helps to reduce mortality and improve survival. Aptamers are widely used in cancer screening because of their high specificity, good stability and low cost. In this study, using the specific aptamer of lung cancer serum, the sandwich method colloidal gold test strip was prepared by isothermal amplification technique and the principle of nucleic acid hybridisation for the early diagnosis of lung cancer. The results showed that the test strip was positive in 8 patients with lung cancer, which was consistent with the actual cases. The test strip can accurately identify lung cancer patients. The concentration range of nucleic acid detection is 1 × 10−4 - 7 × 10−4 mol/L, and the detection limit is 0.67 mM. The test strip detection method has low cost and simple operation, and provides a reference for the development of home portable tumor early detection.

Fast Rock Detection in Visually Contaminated Mining Environments Using Machine Learning and Deep Learning Techniques

Advances in machine learning algorithms have allowed object detection and classification to become booming areas. The detection of objects, such as rocks, in mining operations is affected by fog, snow, suspended particles, and high lighting. These environmental conditions can stop the development of mining work, which entails a considerable increase in operating costs. It is vital to select a machine learning algorithm that is accurate, fast, and contributes to lower operational costs because of the aforementioned environmental situations. In this study, the Viola-Jones algorithm, Aggregate Channel Features (ACF), Faster Regions with Convolutional Neural Networks (Faster R-CNN), Single-Shot Detector (SSD), and You Only Look Once (YOLO) version 4 were analyzed, considering the precision metrics, recall, AP50, and average detection time. In our preliminary tests, we have observed that the differences between YOLO v4 and the latest versions are not substantial for the specific problem of rock detection addressed in our article. Therefore, YOLO v4 is an appropriate and representative choice for evaluating the effectiveness of existing methods in our study. The YOLO v4 algorithm performed the best overall, whereas the SSD algorithm performed the fastest. The results indicate that the YOLO v4 algorithm is a promising candidate for detecting rocks with visual contamination in mining operations.

The Standard and Reverse Mode Operation of a Hydrocyclone for Microplastic Separation

Harmonization in the analytical framework is needed to detect, define and further categorize plastics released into the environment. In the range of particles smaller than 200 μm, hydrocyclones (HCs) have proven their capacity in removing microplastics efficiently by offering technical advantages at low operational costs. This publication aims to expand scientific knowledge by introducing four commercially available, low-priced microplastics to a pilot-scale HC setting. The physicochemical characteristics of particles as well as the separation efficiency of the test rig were investigated in depth. Particles with a density of &gt;1000 kg/m3 passed the primary vortex and were discharged into the underflow, allowing us to employ standard mode operation. Particles with a density of &lt;1000 kg/m3 entered the secondary vortex and were removed through the overflow. As expected, separation efficiencies were found to be higher for particles revealing a greater density difference when compared with the mobile phase water. Furthermore, an increase in the inlet volume flow revealed significant positive impacts on the separation efficiency for three plastics to a certain threshold. Data on standard and reverse mode operations presented in this publication can lay out an important source for the harmonization and standardization of future HC research, with the goal of overcoming plastic pollution by developing economically competitive separation processes.

Balloon angioplasty as first-choice recanalization strategy for intracranial atherosclerosis-related emergent large vessel occlusion with small clot burden.

The optimal primary recanalization strategy for intracranial atherosclerosis-related emergent large vessel occlusion (ICAS-ELVO) remains controversial. We aimed to explore the safety and efficacy of balloon angioplasty as the first-choice recanalization strategy for ICAS-ELVO with small clot burden. Consecutive ICAS-ELVO patients presenting with microcatheter "first-pass effect" during endovascular treatment (EVT) were retrospectively analyzed. Patients were divided into preferred balloon angioplasty (PBA) and preferred mechanical thrombectomy (PMT) groups based on the first-choice recanalization strategy. The reperfusion and clinical outcomes between the two groups were compared. Seventy-six patients with ICAS-ELVO involving the microcatheter "first-pass effect" during EVT were enrolled. Compared with patients in the PMT group, those in the PBA group were associated with (i) a higher rate of first-pass recanalization (54.0% vs. 28.9%, p = .010) and complete reperfusion (expanded thrombolysis in cerebral ischemia ≥ 2c; 76.0% vs. 53.8%, p = .049), (ii) shorter puncture-to-recanalization time (49.5min vs. 89.0min, p < .001), (iii) lower operation costs (¥48,499.5 vs. ¥ 99,086.0, p < .001), and (iv) better 90-day functional outcomes (modified Rankin scale:0-1; 44.0% vs. 19.2%, p = .032). Logistic regression analysis revealed that balloon angioplasty as the first-choice recanalization strategy was an independent predictor of 90-day excellent functional outcomes for ICAS-ELVO patients with microcatheter "first-pass effect" (adjusted odds ratio = 6.01, 95% confidence interval: 1.15-31.51, p = .034). Direct balloon angioplasty potentially improves 90-day functional outcomes for ICAS-ELVO patients with small clot burden, and may be a more appropriate first-choice recanalization strategy than mechanical thrombectomy for these patients.

Exergoeconomic comparison of a novel to a conventional small-scale power-to-ammonia cycle

Ammonia is a promising carbon-free energy vector with potential applications for low carbon energy storage, transportation, power production, and fertilizer use. Small-scale renewable Power-to-Ammonia (P2A) is particularly suited for remote, agricultural areas. Employing real cost data, this work presents an exergoeconomic comparison of a novel to a conventional prototype containerized P2A system. The novel system is designed for low investment cost and is going to be tested in late 2023. Both systems have an installed 18 kWel electrolyzer capacity, yielding 1.5 kg/h of ammonia. However, the novel system has 90 000 € lower purchased equipment costs of 400 000 € and 1 800 € lower operating and maintenance costs of 20 000 €/y. The exergoeconomic analysis rightly identifies the key component in the novel design, the recycle valve, as the highest operation cost driver. However, the investment cost advantage of the valve compared to a recycle compressor in the conventional layout results in a slightly lower Levelised Cost Of Ammonia (LCOA) of 161 €/kg for the novel compared to 173 €/kg of the conventional cycle, making the novel layout overall cost superior. These values are well above values reported in literature of below 1 €/kg. However, the data presented in this work refer to a research prototype application, whose costs are strongly affected by the high engineering cost and the low number of operating hours. A mass produced prototype with a higher utilization will have much lower costs.

Providing a New Multiobjective Two-Layer Approach for Developing Service Restoration of a Smart Distribution System by Islanding of Faulty Area

One of the essential capabilities of a smart distribution network is to improve network restoration performance using the postfault islanding method. Islanding of the faulty area can be done offline and online. Online islanding will decrease load shedding and operation cost. In this study, a novel two-step mathematical method for system restoration after the fault is presented. A new mathematical model for the optimal arrangement of the system for the faulty area in the first layer is proposed. In this layer, the main objective is to decrease the distribution system’s load shedding and operational costs. In this regard, after the fault event, the boundary of the islanded MGs is determined. Then, in the second layer, the problem of unit commitment in the smart distribution network is addressed. In addition to the load shedding, optimal planning of energy storage systems (ESSs) and nondispatchable distributed generation (DG) resource rescheduling are also determined in this layer. The important advantages of the proposed approach are low execution time and operational costs. A demand response (DR) program has also been used for optimal system restoration. Solving the problem using the multiobjective method with the epsilon-constraint method is another goal of the paper, which simultaneously minimizes the cost and the emissions of the smart distribution network. The proposed model has been tested on an IEEE 33-bus system. Better performance of the proposed model compared to the techniques in the literature has been proven.

Prediction of Maize Yield and Assessment of the Material Circularity of Maize Residues Used in the Dairy Cattle Feed Production

The dairy cattle industry is expanding rapidly to accommodate the growing demand for human consumption. This results in high cattle feed production. This study aims to forecast the amount of maize residue and calculate the circularity level of using maize residue as a feedstock for total mixed ration feed production in dairy cattle feed production. The multiple linear regression analysis is performed to predict maize yields in Thailand so that maize residues can be calculated and used in feed production. The material circularity indicator is also used with biological cycles to assess the circularity level of dairy cattle feed production under the circular economy concept. The results show that the maize yield is expected to increase, resulting in more maize residues. The use of maize residues in daily cattle feed production gives the MCI value of 0.6038, representing a high degree of circularity and explaining the sustainable management of wastes in the cattle feed industry. Moreover, using maize residues as a part of feed ingredients instead of the Napier grass saves the cost by 14.2%. The study results provide a guideline for farmers and related authorities to plan for managing maize residue in daily cattle feed production to lower operation costs and minimize environmental problems. Keywords: Biological cycle, Circular economy, Dairy cattle feed production, Maize residues, Material circularity indicator, Multiple linear regression

Photovoltaic panel and wind turbine as an electric energy source for water evaporation and desalination

The purpose of the article is to study the process of heating and desalination of seawater due to electricity from wind turbines and solar panels. This electric current is collected and transmitted through wires to be used to power electrical devices. The advantages of using solar panels as an alternative energy source include environmental cleanliness, long service life, no noise, low cost of operation and no emissions of harmful substances. Boiling water using wind turbine energy is a great way to use renewable wind energy to support daily activities. By placing a wind turbine in an area with a steady wind, the turbine can collect kinetic energy from the air and convert it into mechanical energy that can be used to rotate the generator. This generator can then convert mechanical energy into electrical energy, which can be used to power an electric boiler. The boiler can then use electrical energy to heat the water, producing steam to boil the water.

An ensemble classification method based on machine learning models for malicious Uniform Resource Locators (URL).

Web applications are important for various online businesses and operations because of their platform stability and low operation cost. The increasing usage of Internet-of-Things (IoT) devices within a network has contributed to the rise of network intrusion issues due to malicious Uniform Resource Locators (URLs). Generally, malicious URLs are initiated to promote scams, attacks, and frauds which can lead to high-risk intrusion. Several methods have been developed to detect malicious URLs in previous works. There has been a good amount of work done to detect malicious URLs using various methods such as random forest, regression, LightGBM, and more as reported in the literature. However, most of the previous works focused on the binary classification of malicious URLs and are tested on limited URL datasets. Nevertheless, the detection of malicious URLs remains a challenging task that remains open to research. Hence, this work proposed a stacking-based ensemble classifier to perform multi-class classification of malicious URLs on larger URL datasets to justify the robustness of the proposed method. This study focuses on obtaining lexical features directly from the URL to identify malicious websites. Then, the proposed stacking-based ensemble classifier is developed by integrating Random Forest, XGBoost, LightGBM, and CatBoost. In addition, hyperparameter tuning was performed using the Randomized Search method to optimize the proposed classifier. The proposed stacking-based ensemble classifier aims to take advantage of the performance of each machine learning model and aggregate the output to improve prediction accuracy. The classification accuracies of the machine learning model when applied individually are 93.6%, 95.2%, 95.7% and 94.8% for random forest, XGBoost, LightGBM, and CatBoost respectively. The proposed stacking-based ensemble classifier has shown significant results in classifying four classes of malicious URLs (phishing, malware, defacement, and benign) with an average accuracy of 96.8% when benchmarked with previous works.

Analysis of Platinum (Pt) Ion Biosorption Performance Using Chitosan

Platinum Group Metal has a continuous increase in demand. The demand for platinum is divided into four segments: automotive, jewelry, industry, and investment. However, the availability of PGM is limited, resulting in the potential scarcity of the metal and the necessity for developing more effective and environmentally friendly methods. Biosorption serves as an alternative method for capturing metal ions using biological materials. Biosorption is chosen due to its low operational costs, environmentally friendly, and utilization of abundant natural biomaterials. This study aims to analyze the influence of chitosan concentration and adsorbent surface area on the biosorption process of Pt metal from H2PtCl6 solution. The biosorption method utilizes chitosan membranes produced by mixing chitosan with PVA and using glutaraldehyde as a crosslinker to stabilize chitosan in solution. The research involves testing using UV-VIS, SEM-EDX, and FTIR techniques to analyze the concentration of adsorbed Pt metal, morphology, and functional groups on the membrane. The results indicate that chitosan is an effective adsorbent for absorbing Pt metal ions, particularly in the CS/PVA adsorbent with a surface area of 352 mm2 and a chitosan concentration ratio of 2 gram, resulting in percentage adsorption of 47.948%.