Low Cost Operation Research Articles

IMPROVEMENT OF THE METHODOLOGY FOR CALCULATING THE HYDRODYNAMICS OF COILED HEAT EXCHANGERS FOR CRYOGENIC PLANTS OPERATING ON THE J-T CYCLE

The widespread use of cryogenic plants operating on the J-T (Joule-Thomson) cycle is associated with low operating costs, reliability, and long service life. One of the main elements of the plant is a recuperative heat exchanger made in the form of a single- or multi-layer coiled heat exchange surface. The ability to compensate for temperature and mechanical stresses due to the twisted design ensures long-term and trouble-free operation of the heat exchange equipment. The importance of determining the characteristics of the hydrodynamics of the flow of liquids or gases is primarily associated with a conscious choice of methods for solving heat and mass transfer problems, the use of certain methods of process intensification, and optimization of equipment design. The purpose of creating efficient equipment is to determine the maximum heat transfer rate at moderate values of hydraulic resistance. The analysis of known empirical dependencies does not provide a definitive answer regarding the development of a generalized methodology for calculating Hampson-type microheat exchangers used in cryogenic installations. The aim of this work is to improve the methodology for calculating the hydrodynamics of coiled heat exchangers by modifying the calculated correlations. This is possible by introducing appropriate corrections to them that take into account the influence of the geometric characteristics of the tube bundle on its resistance. The experimental study of hydrodynamic processes during forced gas convection in a coiled heat exchanger made it possible to establish the dependence of the Euler number Eu on the main geometric characteristics of the heat exchanger: the relative coil pitch, the gap between the heat exchanger tube and the outer and inner surfaces of the body. Based on the research results, the corrections in the dimensionless form eкр and eз, which are used to perform variational calculations of the structures of coiled heat exchangers located in annular channels, were determined in order to optimize their geometric characteristics. Bibl. 19, Fig. 5.

Microbial osmotic pressure desalination system for desulfurization wastewater from coal-fired power plants: A pilot study

The treatment of desulfurization wastewater has emerged as a critical area of research due to the significant water consumption and drainage volume associated with thermal power plants, which account for 30 %–40 % of industrial water usage. The desalination and treatment of this wastewater generate desulfurization byproducts, posing environmental risks and incurring high operational costs. This study established a pilot-scale desalination system based on the principle of microbial osmotic pressure for the treatment of desulfurization wastewater from power plants. The desulfurization wastewater primarily contains Cl−, SO42−, Mg2+, Ca2+, and Na+ ions. The microbial osmotic pressure device reduced the water conductivity from 26,000 μs/cm to below 8000 μs/cm, achieving a total salt removal rate of 70 %. The removal rates for Cl−, SO42−, Mg2+, Ca2+ and Na+ were 69.10 %, 76.55 %, 73.0 %, 35.34 % and 52.80 %, respectively. The desalinated water from the osmotic pressure device then undergoes aerobic removal of organic compounds, followed by membrane filtration and reverse osmosis for further desalination. The effluent conductivity is <1000 μs/cm, meeting the standards for reuse water. The microbial osmotic pressure device enriched salt-absorbing halobacteria and was dominated by norank_f__Bacteroidetes_vadinHA17, Trichococcus, Acidovorax, Soehngenia, and norank_f__Hungateiclostridiaceae. Compared to traditional processes, the biological desalination process demonstrates lower operational and annual maintenance costs under conditions of regular addition of microbial agents and liquid activators to sustain the desalination bacterial community. Future research could focus on desalination microbial cultivation methods to improve stability and reduce costs.

Operation and maintenance cost comparison between 15 MW direct-drive and medium-speed offshore wind turbines

Abstract. Determining offshore wind energy operational expenditures relies on acquiring reliability data, particularly as turbine power ratings increase. The uncertainty surrounding operational costs and optimal drivetrain configurations for larger turbines persists. While previous research has addressed reliability data for 3 MW offshore wind turbines, this study reviews and collates updated failure data for 15 MW turbines, comparing direct-drive and medium-speed configurations. It employs an operation and maintenance (O&amp;amp;M) modelling tool to calculate total operational costs. The study concurs with existing literature, showing that direct-drive turbines have lower operational costs than medium-speed turbines in three case studies. However, the cost and availability differences between configurations are smaller than previously suggested. For 15 MW turbines, the analysis reveals that the cost disparity between direct-drive and medium-speed turbines is significantly smaller than for smaller-rated turbines, with percentages of 1.59 %, 1.58 %, and 5.78 % for the three ScotWind sites selected. Previously, the absolute percentage difference in cost between direct-drive and medium-speed turbines was estimated to be 29.79 %. Sensitivity analyses explore the influence of three factors – failure rates, accessibility limits, and major replacement times – on total operational costs. These analyses demonstrate that medium-speed configurations exhibit more significant cost fluctuations, and the cost gap between configurations is reduced if failure rates are lowered at the same rate for each configuration, accessibility increases, or the major replacement time is reduced for the larger wind turbine components.

Impact of CCTV on Police Operations Outcomes in Nairobi City County, Kenya

The widespread adoption of Closed-Circuit Television (CCTV) systems in major cities globally aims to improve policing outcomes. However, despite Nairobi’s CCTV system being operational since mid-2015, its specific impact remained unclear. This study investigated how CCTV influenced five key police operation outcomes: crime reduction, response times, road safety, officer safety, and cost reduction. Drawing from Rational Choice Theory and Routine Activity Theory, the study employed a convergent mixed-method design combining a cross-sectional survey with a phenomenological design. Participants included 403 police officers from the CCTV command centre and various police stations across the county, selected via cluster and purposive sampling. Data collection utilised questionnaires, key informant interviews and focus group discussions. Quantitative analysis employed binary logistic regression, while qualitative data underwent thematic analysis. The findings revealed that CCTV has significantly reduced crime, enhanced road safety and officer safety, and lowered operational costs. However, response times saw minimal improvement due to limited CCTV coverage and coordination challenges between officers. The study recommends expanding coverage, strengthening collaboration among officers, and publicising the system's successes to maximise CCTV's potential

Context really matters: why do women artisans in the Peruvian context avoid the sole ownership of their enterprises?

PurposeThe aim of this paper is to find out why women artisans in Peru avoid the sole ownership of their enterprises while preferring to work in associations.Design/methodology/approachThe data were collected through semi-structured interviews with 28 women artisans in Peru during their participation in a fair organized by the Peruvian Ministry of Culture in Lima (Peru). Thematic analysis was subsequently used to develop the main themes and sub-themes of the study.FindingsThe authors of the present study have found that women artisans in Peru choose to work in associations instead of via the sole ownership of their enterprises because of the following three categories of motives: contextual (low operational cost of family-owned associations, more compliance with the surrounding institutional context), cultural (commitment to parenting, experiencing less marginalization, zero responsibility, and greater work flexibility) and marketing-related motives (eliciting more social support, guaranteeing more invitations to participate in artisanal fairs).Originality/valueThis paper contributes by filling a gap in the literature on artisan entrepreneurship in which studies on women artisans in Latin American contexts and why they choose to work in associations have been limited so far.

Optimizing the infoproduct launch process: integrating the communication macrostrategy and paid traffic key performance indicators through the VERSALTO method

The global market, especially in the Digital Marketing area, has experienced enormous growth and a high degree of impact due to advances in technology, the growing number of smartphone users and the daily time spent using smartphones by these users. Within this market, the infoproducts sector has emerged as a specific and high-potential segment, directly connecting end consumers with information products, such as courses, mentoring, training, e-books and much more. The unique potential of this sector lies in its ability to overcome barriers that exist in other markets, making it possible to reach niche audiences and audiences on a global scale, often with low-cost operations and high return on investment (ROI). Despite the enormous potential of this emerging market, it lacks consolidated research on macro performance indicators in infoproduct operations, particularly the interaction between indicators of paid traffic operations and communication strategies, which directly affect the financial results of digital launches. In this context, this study aims to present the VERSALTO method, a framework designed to plan and analyze communication macro strategies and paid traffic indicators for infoproduct launches, demonstrating the interrelationship between these KPIs and the communication macro strategy, in addition to developing knowledge academic on the topic. To develop the method and demonstrate its practical application, this research analyzes two case studies with revenues exceeding 100 thousand Brazilian reais (20 thousand American dollars). Through this analysis, the interrelationship between paid traffic indicators and the communication macro strategy used in infoproduct launches was visualized. In this way, this study provides crucial insights for optimizing strategies and generating more profitable results in the dynamic digital marketing landscape.

Sustainable Geoinformatic Approaches to Insurance for Small-Scale Farmers in Colombia

This article presents a low-cost insurance system developed for smallholder farms in disaster-prone regions, primarily using free Earth observation (EO) data and free open source software’s (FOSS), collectively termed “sustainable geoinformatics.” The study examined 30 farms in Risaralda Department, Colombia. A digital elevation model (12.5 m pixels) from the ALOS PALSAR satellite sensor was used with a geographic information system (GIS) to map the terrain, drainage, and geohazards of each farming district. Google Earth Engine (GEE) was used to carry out time-series analysis of 15 EO and weather datasets for 1998 to 2020. This analysis enabled the levels of risk from hydrometeorological hazards to be determined for each farm of the study, providing key data for the setting of insurance premiums. A parametric insurance product was developed using a proprietary mobile phone app that collected GPS-tagged, time-stamped mobile phone photos to verify crop damage, with further verification of crop health also provided by daily near-real-time satellite imagery (e.g., PlanetScope with 3 m pixels). Machine learning was used for feature identification with the photos and the satellite imagery. Key features of this insurance system are its low operational cost and rapid damage verification relative to conventional approaches to farm insurance. This relatively fast, low-cost, and affordable approach to insurance for small-scale farming enhances sustainable development by enabling policyholder farmers to recover more quickly from disasters.

Simple Method for Calcium Determination in Crude Oil Emulsion Using Digital Image Colorimetry

A portable device for digital image colorimetry (DIC) is reported for the determination of Ca in crude oil emulsions. ASTM D4807 was used for sample preparation for the isolation of saline species containing Ca. DIC was evaluated based on the complexation reactions with Ca using calcein, murexide, and calcon. Partial least squares (PLS) provided the best determination coefficients (higher than 0.990) for all complexing reagents. Significant interference of Mg was observed for the determination of Ca using calcein. PLS models with the lowest root mean square error of cross validation (RMSECV) values were obtained for murexide and calcon for a concentration of 0.250 mmol L−1 at pH of 13. The best conditions for camera focal length were 11 and 2 mm for murexide and calcon. For light intensity, the optimized conditions for murexide and calcon was 180 lux. Stabilities of color complexes over the time were 30 and 15 min for murexide and calcon, respectively. DIC/murexide provided the lowest root mean square error of prediction (RMSEP) and a linear response was observed from 0.5 to 10 mg L−1 Ca and the relative standard deviation (RSD) was lower than 10%. Significant differences were not observed between the results obtained by DIC/murexide and those obtained by microwave-assisted wet digestion (MAWD) with determination by inductively coupled plasma-optical emission spectrometry (ICP-OES). A limit of quantification of 0.50 mg kg−1 Ca were obtained using DIC/murexide. This approach is an alternative and useful method for the determination of Ca in crude oil samples, with advantages of miniaturization, low cost, and ease of operation.

GREEN SCHOOLS: A BRIEF REVIEW

Green schools represent an innovative educational model that seamlessly integrates environmental stewardship principles with teaching methods to create enriching learning environments. In addition to focusing on conserving resources, they prioritize student well-being and instill a deep environmental consciousness, shaping a generation dedicated to sustainability. This article explores the significance, objectives and advantages of green schools, emphasizing their critical role in fostering a sustainable future through comprehensive education and increased environmental awareness. The research specifically underscores the economic and environmental significance of green schools, highlighting their capacity to lower operational costs while simultaneously reducing environmental impact and promoting a more sustainable planet for future generations.

Unmanned aerial system plant protection products spraying performance evaluation on a vineyard

In the context of increasing global food demand and the urgent need for production processes optimization, plant protection products play a key role in safeguarding crops from insects, pests, and fungi, responsible of plant diseases proliferation and yield losses. Despite the inaccurate distribution of conventional aerial spraying performed by airplanes and helicopters, Unmanned Aerial Spraying Systems (UASSs) offer low health risks and operational cost solutions, preserving crops and soil from physical damage. This study explores the impact of UASS flight height (2 m and 2.5 m above ground level), speed (1 m s−1 and 1.5 m s−1), and position (over the canopy and the inter-row) on vineyard aerial spraying efficiency by analysing Water Sensitive Papers droplet coverage, density, and Number Median Diameter using a MATLAB script. Flight position factor, more than others, influenced the application results. The specific configuration of 2 m altitude, 1.5 m s−1 cruising speed, and inter-row positioning yielded the best results in terms of canopy coverage, minimizing off-target and ground dispersion, and represented the best setting to facilitate droplets penetration, reaching the lowest parts generally more affected from disease. Further research is needed to assess UASS aerial PPP distribution effectiveness and environmental impact in agriculture, crucial for technology implementation, especially in countries where aerial treatments are not yet permitted.

Application of deep UV resonance Raman spectroscopy to column liquid chromatography: Development of a low-flow method for the identification of active pharmaceutical ingredients

In this study, deep UV resonance Raman spectroscopy (DUV-RRS) was coupled with high performance liquid chromatography (HPLC) to be applied in the field of pharmaceutical analysis. Naproxen, Metformin and Epirubicin were employed as active pharmaceutical ingredients (APIs) covering different areas of the pharmacological spectrum. Raman signals were successfully generated and attributed to the test substances, even in the presence of the dominant solvent bands of the mobile phase. To increase sensitivity, a low-flow method was developed to extend the exposure time of the sample. This approach enabled the use of a deep UV pulse laser with a low average power of 0.5 mW. Compared to previous studies, where energy-intensive argon ion lasers were commonly used, we were able to achieve similar detection limits with our setup. Using affordable lasers with low operating costs may facilitate the transfer of the results of this study into practical applications.

Unsupervised quality monitoring of metal additive manufacturing using Bayesian adaptive resonance

Metal additive manufacturing is a recent breakthrough technology that promises automated production of complex geometric shapes at low operating costs. However, its potential is not yet fully exploited due to the low reproducibility of quality in mass production. The monitoring of parts quality directly during manufacturing promises to solve this problem, while machine learning showed efficient performance correlating versatile manufacturing measurements with different quality grades. Today, most monitoring algorithms are based on semi- or supervised learning, thus, requiring a collection and ground-truth validation of training sets. This is costly and time consuming in real-life conditions. Our work is a feasibility study of the application of unsupervised machine learning to monitor different manufacturing regimes and quality in metal additive manufacturing. The algorithm combines the kernel Bayes rule for inference and Bayesian adaptive resonance for structuring the incoming data. Airborne acoustic emission from laser powder bed fusion is used as an algorithm input. The recognition of the main manufacturing regimes (conduction mode, stable, and unstable keyholes) are shown on real-life data, while the self-learning accuracy of developed algorithm exceeds 88 %. Our approach promises future development of plug-and-play quality monitoring systems for laser processing technology, requiring minimum modifications of the existing machines, reducing time/cost for algorithm preparation and providing continuous data driven adaptation of the algorithm to changes in manufacturing conditions.

Advancements and 4E + Q performance analyses in solar drying for maize kernels preservation: A comprehensive review

AbstractThe world's population is projected to increase, impacting a 60% rise in food production demand by 2050, including the demand for maize. Currently, maize production is estimated to reach 1510 million tons in 2023, necessitating suitable methods for preserving maize kernels to maintain quality and reduce post‐harvest agricultural losses. Although drying methods present a viable solution to address this issue, the majority of farmers in Indonesia still use conventional drying methods, such as open sun drying, which has several drawbacks. Hence, solar drying systems aim to overcome these limitations by harnessing renewable, clean, sustainable, and eco‐friendly energy to support the sustainable development goals (SDGs). This research comprehensively reviews the progress of solar drying technology, providing insights into its working principles, innovative designs, and operational modes across various types of solar dryers. The 4E + Q analysis provides information regarding the performance of solar drying systems, including energy analysis to determine the quantity of energy utilized and exergy analysis to demonstrate irreversibility in thermodynamic processes. Additionally, this analysis highlights environmental impacts such as CO2 emissions and mitigation efforts, while considering economic aspects through lifecycle assessment and payback period methods. The quality assessment of dried maize kernels according to the standards set by the Indonesian National Standard (SNI) is conducted, including proximate analysis, aflatoxin levels, color measurements, and internal cracking analysis.Practical applicationsDrying technology is essential for the food preservation process, enabling the maintenance of product quality and extending storage life by reducing the moisture content to eliminate the activities of microbial agents. The application of solar drying technology harnesses solar radiation as the primary energy source, making it well‐suited for crops like paddy, maize, cassava, etc. The development of solar dryers continues through modifications and the addition of auxiliary systems to achieve more efficient, economical, and sustainable technologies. The hope is that in the future, these technologies can be adopted by farmers on an industrial scale due to low operational costs, ease of operation, and controllable operating conditions. The methodological approach is necessary to evaluate the performance of solar dryers and the quality of dried products through a 4E + Q analysis, it provides information on energy consumption, exergy flow, economic considerations, environmental aspects, and quality assessments against national quality standards (SNI).

The complexation color development—visible spectrophotometry method for rapid determination of Ca2+ concentration in mine wastewater and its mechanism

Ca2+ is an important cation in coagulants used in the treatment of mine wastewater such as coal slime water, and its concentration directly affects the settling and flotation of coal slime water. The rapid determination of Ca2+ concentration is of great significance to guiding industrial production. Herein, the complexation color development—visible spectrophotometry (CCDVS) method was proposed to obtain the concentration information of Ca2+, wherein 1-(1-Hydroxy-4-methyl-2-phenylazo)-2-naphthol-4-sulfonic acid (Calmagite) was used as an indicator and the mechanism of complexation between the indicator and Ca2+ was explored. The strong correlation between spectral data and Ca2+ concentration (R2 = 0.997) indicated that this method could be used for Ca2+ detection, thereby a quantitative model was proposed to predict Ca2+ concentration by using the complexation color development—visible light absorption data. The chemical element and bonding analysis confirmed that Ca2+ was complexed with the sulfonic acid group on the indicator skeleton in the form of Ca(OH)+ intermediate in an alkaline environment. The generated complexation product (-S-O-Ca(OH)+) could stably absorb 620 nm light energy, and its absorption performance remained 99.8% after standing for 60 min. The relative standard deviation of the detection results of Ca2+ in different calcium source solutions was as low as 1.24%, which confirmed the strong calcium source compatibility of the proposed method. More importantly, the successful application of this method in the determination of Ca2+ concentration at different points in industrial coal slime water system proved its advantages of high accuracy (relative error is as low as 5.01%), low cost and simple operation. The LOD of this method is 0.038 mg/L. Therefore, the complexation color development—visible spectrophotometry method can maximize the potential for cost reduction and efficiency improvement in mine wastewater treatment.

Future of District Heating Systems – Investigation of Various Technologies in Danish Context

In response to the urgent need for decarbonization, district heating (DH) systems must explore emission-reducing investments that simultaneously lower operational costs of the plant. The recent years with wildly fluctuating gas and electricity prices put even more emphasis on finding the optimal combination of generation units for DH systems. This article aims at investigating which of the available technologies are the future of district heating. Solar collectors, photovoltaics (PV) and wind turbines and their combinations with other technologies are considered. The analysis is done based on Assens District Heating located in Funen, Denmark, where the annual heat production is approximately 96 000 MWh. The plant has already invested in an East-West oriented PV plant and a heat pump. However, for the purpose of this paper, an assumption is made that this investment can be redone based on the present investment costs in Denmark. The analysis is made in the energy system analysis tool energyPRO, where different combinations of technologies are analyzed. The study investigates the operational and investments costs, looking into the influence on Net Heat Production Cost (NHPC) and quantifying the investment yearly benefit. The results demonstrate profitability and feasibility of integrating renewable energy resources into district heating systems within the Danish context. The ambition is to showcase the possibilities and provide decision makers with insight into robust investments in renewable energy systems. Furthermore, by highlighting the success and potential of renewable energy integration in district heating systems in Denmark, this study aims to inspire further research and innovation in the field in other European countries, where the DH systems are primarily based on fossil fuels.

Detection of Cavitation in a Centrifugal Pump-as-Turbine Using Time-Domain-Based Analysis of Vibration Signals

Pumps-as-Turbines (PATs) are increasingly used in micro-hydropower applications due to their cost competitiveness that is brought about by lower acquisition, design, operation, and maintenance costs. Despite these, limited research exists that investigates PAT failures. Notably, there is a literature gap concerning cavitation in PATs. As such, this study proposes an improvement to the deviation from the normal distribution (DND) technique to facilitate application in PAT cavitation detection. Probability density functions of vibration signals collected during operation at design speed and various cavitation states are developed and the DND computed using two approaches, i.e., the use of baseline data and the original method, for comparison purposes. Normal probability plots are presented to depict suitability of the two approaches in quantifying the DND. Results show higher deviation when using baseline data, hence, improved detection capabilities with amplification of the slope of the trend line under cavitating conditions when using the proposed DND approach. The proposed method also allows for establishing clear alarm limits for the condition monitoring of PATs in practice. Moreover, the proposed method is validated by application at various PAT operating speeds and cavitation states. The proposed method is found to be responsive, reliable, and independent from operating speed.

Performance Analysis of Multi-Tote Storage and Retrieval Autonomous Mobile Robot Systems

Multi-tote storage and retrieval (MTSR) autonomous mobile robots can carry multiple product totes, store and retrieve them from different shelf rack tiers, and transport them to a workstation where the products are picked to fulfill customer orders. In each robot trip, totes retrieved during the previous trip must be stored. This leads to a mixed storage and retrieval route. We analyze this mixed storage and retrieval route problem and derive the optimal travel route for a multiblock warehouse by a layered graph algorithm, based on storage first-retrieval second and mixed storage and retrieval policies. We also propose an effective heuristic routing policy, the closest retrieval (CR) sequence policy, based on a local shortest path. Numerical results show that the CR policy leads to shorter travel times than the well-known S-shape policy, whereas the gap with the optimal mixed storage and retrieval policy in practical scenarios is small. Based on the CR policy, we model the stochastic behavior of the system using a semiopen queuing network (SOQN). This model can accurately estimate average tote throughput time and system throughput capacity as a function of the number of robots in the system. We use the SOQN and corresponding closed queuing network models to optimize the total annual cost as a function of the warehouse shape, the number of robots, and tote buffer positions on the robots for a given average tote throughput time and throughput capacity. Compared with robots that retrieve a single tote per trip, an MTSR system with at least five buffer positions can achieve lower operational costs while meeting given average tote throughput time and tote throughput capacity constraints. Funding: This work was supported by National Natural Science Foundation of China [Grant 72372088] and the Shenzhen Science and Technology Program [Grant GJHZ20220913143003006]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0397 .

Comparison Of Functional Outcomes of Arthroscopic Bankart Repair Vs Open Bristow-Latarjet Procedure for Shoulder Joint Instability

Background: Anterior shoulder instability predominantly affects collegiate athletes, with a notable incidence of 0.12 per 1000 athletic exposures. Acute dislocations pose an orthopedic emergency requiring immediate repositioning to prevent complications such as brachial plexus or artery compression, avascular necrosis of the humeral head, and chronic disability. Conservative therapy often precedes surgical intervention due to concerns over recovery time, discomfort, decreased range of motion, and high recurrence rates. Objective: This study aims to compare the efficacy of Arthroscopic Bankart Repair and open Bristow-Latarjet Procedure in terms of pain reduction, return to normal activities, and improvement in Western Ontario Shoulder Instability (WOSI) score among patients with anterior shoulder joint instability. Methods: A 12-month comparative study was conducted at Lahore General Hospital from April 2022 to April 2023, following ethical review board approval. Thirty-two patients aged 18–32, with severe unidirectional dislocation, consented and participated. They were divided into two groups: Group A underwent Arthroscopic Bankart Repair, and Group B underwent the open Bristow-Latarjet Procedure. Results: The average age was 23 in Group B and 25 in Group A. Males comprised 80% of Group A and 68% of Group B. WOSI scores averaged 72 for Bankart and 76 for Latarjet. Rowe scores were 75 (Bankart) and 69 (Latarjet), with Quick DASH scores of 11 (Bankart) and 15 (Latarjet). External rotation was 77 degrees in Bankart and 73 degrees in Latarjet. Functional satisfaction was reported by 84.0% of Bankart and 11.7% of Latarjet patients. The cost of Latarjet was significantly lower than that of Bankart. Conclusion: The open Latarjet procedure demonstrated higher functional satisfaction and lower operational costs compared to Arthroscopic Bankart Repair, which exhibited a higher recurrence rate. Given the economic constraints in a developing country like Pakistan, Latarjet may be a more viable option despite the non-invasive appeal of Bankart Repair.

GMAlignNet: multi-scale lightweight brain tumor image segmentation with enhanced semantic information consistency

Although the U-shaped architecture, represented by UNet, has become a major network model for brain tumor segmentation, the repeated convolution and sampling operations can easily lead to the loss of crucial information. Additionally, directly fusing features from different levels without distinction can easily result in feature misalignment, affecting segmentation accuracy. On the other hand, traditional convolutional blocks used for feature extraction cannot capture the abundant multi-scale information present in brain tumor images. This paper proposes a multi-scale feature-aligned segmentation model called GMAlignNet that fully utilizes Ghost convolution to solve these problems. Ghost hierarchical decoupled fusion unit and Ghost hierarchical decoupled unit are used instead of standard convolutions in the encoding and decoding paths. This transformation replaces the holistic learning of volume structures by traditional convolutional blocks with multi-level learning on a specific view, facilitating the acquisition of abundant multi-scale contextual information through low-cost operations. Furthermore, a feature alignment unit is proposed that can utilize semantic information flow to guide the recovery of upsampled features. It performs pixel-level semantic information correction on misaligned features due to feature fusion. The proposed method is also employed to optimize three classic networks, namely DMFNet, HDCNet, and 3D UNet, demonstrating its effectiveness in automatic brain tumor segmentation. The proposed network model was applied to the BraTS 2018 dataset, and the results indicate that the proposed GMAlignNet achieved Dice coefficients of 81.65%, 90.07%, and 85.16% for enhancing tumor, whole tumor, and tumor core segmentation, respectively. Moreover, with only 0.29 M parameters and 26.88G FLOPs, it demonstrates better potential in terms of computational efficiency and possesses the advantages of lightweight. Extensive experiments on the BraTS 2018, BraTS 2019, and BraTS 2020 datasets suggest that the proposed model exhibits better potential in handling edge details and contour recognition.

Frequency-selective acoustic micromanipulation platform

Acoustic micromanipulation methods offer versatile tools ranging from lab-on-chip diagnostic platforms to mobile microrobots. Acoustically oscillating bubble-powered microsystems are gaining increased attention owing to their low cost and convenient operation. To date, no studies have reported on the controllability of the driving frequency on acoustic micromanipulation platforms using Helmholtz resonators. Here, we introduce a Helmholtz resonant cavity into a bubble-powered microsystem to reveal the function of the oscillation modes and frequency selectivity. The Su–Schrieffer–Heeger (SSH) model was utilized to construct a topological interfacial state and bubble-powered working location. We experimentally observed that the microparticles were captured by the oscillation bubble and underwent orbital rotation at the topological frequency. However, the microparticles were tightly bound close to the oscillation bubble area when the driving frequency was outside the topological frequency range. Our acoustic micromanipulation platform implements multiple microparticle manipulation modes based on frequency selectivity that are independent of optical or magnetic properties, which can enrich the micromanipulation toolbox and exhibit enormous application potential in the biomedical field.