Usage Period Research Articles

Examination of Microbial Levels in Students' Bed Linens

Purpose: This paper determined the microbial content of bed linens used by students based on period of usage, gender of user and types of fabrics. Design/Methodology/ Approach: Laboratory tests were conducted on bed linens used by students. Purposive and convenience sampling techniques were employed to select 32 each male and female participants living alone for the study. 32 pieces of bed linens, each 100% cotton and cotton/polyester blend, were used for the experiment. Two categories of gender (male and female) and two different periods of usage (one week and two weeks) were also employed. The statistical software employed to analyse the data collected was the Statistical Package and Service Solution (SPSS) for Windows version 26. Means and standard deviations of microbial load of bed linens were determined. Independent samples t-test and analysis of variance were employed to determine if differences existed between and among the variables studied. Findings: The study found no difference in microbial load in terms of duration and gender of the user. The results also showed more bacteria load on 100% cotton bed linen than on cotton and polyester blends. Research Limitation: The study's sample was confined to a specific group of students at one university, limiting the generalizability of the findings to a broader student population or diverse living conditions. Practical Implication: The study's outcome is significant for public health and daily hygiene practices. The Ghana health service can use the information to educate individuals on the dangers they will likely be exposed to using bed linens for an extended period without washing and raise awareness about the often-overlooked issue of microbial contamination in everyday environments. Social Implication: The study highlights the need for increased awareness and responsibility regarding personal hygiene within living environments. Originality/Value: This study advances the understanding of microbial contamination in students' bed linens by identifying the roles of usage period, user gender, and fabric type.

Structural maintenance of concrete roads: replacement of concrete slabs on the A1 West Motorway – a Case Study

Common problems on motorways with concrete pavements include edge chipping along the edges of the concrete slabs, damaged pavement transitions (joint damage), reduced skid resistance, cracking and concrete slab migration or subsidence. To effectively repair these damages, concrete pavement rehabilitation works are carried out during periods of low usage, such as weekends and/or during night hours. During these periods, the concrete pavement will be rehabilitated in sections on individual lanes while traffic is routed on the remaining lanes.

Analysis and Comparison of the Compensation Topologies of Magnetic Coupled Resonant Wireless Charging for Electric Vehicles

In recent years, with the rise of electric vehicle industry and people’s increasing demand for convenient life, wireless charging technology used on electric vehicles has become one of the research hotspots. Magnetic coupled resonant wireless power transmission (MCR-WPT) has also been widely concerned because of its high suitability for electric vehicles. This paper mainly focuses on the compensation topologies of magnetic coupled resonant wireless charging for electric vehicles, analyzes 3 types of compensation topologies: P-S, P-P and LCL-LCC. Firstly, the transmission efficiency of these 3 topologies is analyzed from the theoretical point of view. Then, their transmission capacity under different coupling coefficients is compared through simulation. It is found that, in the reality where the coupling coefficients are low, P-S compensation topology should be preferred. Furthermore, by changing the load resistance, it is found that LCL-LCC compensation topology has high stability, which is suitable for scenarios that have long usage period.

Analysis of Qatar's electricity landscape: Insights from load profiling, clustering, and policy recommendations

Demand side management (DSM) is an important strategy for promoting sustainable consumption in resource-rich countries with high purchasing power and subsidized tariffs. Global energy and environmental resource consumption have increased rapidly due to advances in production and transportation, leading to inefficient and wasteful use of resources. DSM aims to address these issues by promoting efficiency. To implement appropriate DSM strategies, a greater understanding of consumer behavior is needed. To do so, load profiling and load clustering are two popular methods that can be used. This paper aims to i) summarize the most recent global load profiling and clustering works ii) use official smart meter data to understand key electricity consumption trends in Qatar, such as temperature-demand correlation, weekend vs. weekday, and public holiday consumption patterns, and iii) perform load clustering to propose policies that would help manage the electricity load in Qatar for its green growth. This study provides insights into the electricity consumption trends of various sectors in Qatar, including commercial, government, hospitality, and residential sectors. It was found that among all the sectors there were only two usage periods of the same time. There is naturally a strong correlation between temperature and electricity consumption throughout the sectors. Furthermore, it was observed that the consumption of the sectors is highly similar which leads to multiple sectors being present in the same cluster. Finally, policy changes are proposed based on the results to encourage demand response programs in Qatar.

Microbiome and Mycobiome Analyses of Continuous Positive Airway Pressure Devices.

Microorganisms are likely present in continuous positive airway pressure (CPAP) devices in daily use. Given the potential risk of infection among CPAP users, we aimed to compare the microbiomes of CPAP devices with those of nasal mucosa samples obtained from patients using these devices. We conducted a prospective cohort study at multiple tertiary medical institutions. Samples were collected from the tubes and filters of CPAP devices and the nasal mucosa of device users. Microbiomes and mycobiomes were analyzed using 16S ribosomal RNA and internal transcribed spacer region sequencing. The results were compared according to sampling site and usage duration for each patient. Overall, 27 paired samples of human nasal mucosa and CPAP components were analyzed. Bacteria were detected in 7 of the 27 tubes (25.9%) and in 22 of the 27 filters (81.5%). Fungi were found in 2 tubes (7.4%) and 16 filters (59.3%). The most prevalent bacterial phyla across all samples were Actinobacteria and Firmicutes. Fungi were not detected in any nasal mucosa samples. However, fungi were identified in the CPAP filters and tubes, with the Basidiomycota and Ascomycota phyla predominating. No significant associations were identified according to sampling site or duration of CPAP use. Some bacteria or fungi are detectable in CPAP samples, even after a short period of CPAP usage. However, the association between respiratory infections and these microbiomes or mycobiomes was not investigated. Further research is required to clarify the risk posed by CPAP devices as a microbial contamination source.

Modeling and Analysis of Current Loading Effects on Electric Vehicle’s Lithium-Ion Batteries: A MATLAB-Based Model Approach

Beyond portable mobile devices, lithium-ion batteries play a crucial role in electric vehicle operations and stationary grid power generation. However, the aging of lithium-ion batteries, often accelerated by extreme temperatures and load current influences, requires thorough examination and solution. The high load current, cycling, temperature differential, and operational conditions are factors contributing to the reduction in capacity and shortened lifespan of lithium-ion batteries. In this study, a lithium-ion (LiNixMnyCozO2) battery was modeled by using the MATLAB/Simulink model technique. In order to investigate the effect of resistance build-up in the batteries, the capacity of the batteries (old and new batteries) was analyzed over different usage periods: 360 cycles, 1000 cycles, and 2000 cycles. A cooling system was introduced to explicitly carry out an inductive analysis of the effect of temperature on the performances of the batteries. The effect of load current on the capacity of the battery was examined between 30 A and 100 A. The results showed that the available capacity of a battery is proportional to its usage rate. Generally, when the load current on the batteries (old and new batteries) was 30 A, the battery was ideally in good health even after 1000 cycles for a 2 h discharge time. In addition, the old battery, however, showed a capacity decrease to about 74.15% and 74.94% for scenarios 1 and 2 after 1000 cycles for a 2 h discharge time when the batteries were subjected to a 100 A discharge current. Amongst other factors, scenarios 1 and 2 can be differentiated by whether the battery pack discharges uniformly or non-uniformly, whether the individual cells operate under the same or different discharge cycles, and whether the batteries are with cooling or without a cooling system. The voltage and temperature differences between the old and new batteries, after 2000 cycles for the 100 A load current, are 4.0 V and 5.3 °C (scenario 2), respectively. Moreover, after 360 cycles at a 100 A discharge current, the temperature difference between the old and new batteries was 4.5 °C in scenario 1 and 2.3 °C in scenario 2. Based on the results obtained in this study, useful equations for proper calibration, voltage, and cooling switching time characteristics were proposed. Additionally, the study results indicated that at higher load currents, battery degradation became less affected by temperature differentials. The results of this study will aid in the adequate load optimization and thermal management of lithium-ion batteries for electric vehicle applications.

Ampere-Hour-Scale Soft-Package Potassium-Ion Hybrid Capacitors Enabling 6-Minute Fast-Charging

Extreme fast charging of Ampere-hour (Ah)-scale electrochemical energy storage devices has emerged as a crucial target for advancing their widespread adoption in various applications, ranging from portable electronics to electric vehicles. The ability to recharge energy storage systems rapidly enhances convenience, productivity, and efficiency, driving the demand for technologies capable of achieving charging times of less than 10 minutes. However, conventional lithium-ion (Li-ion) batteries, ubiquitous in many electronic devices and electric vehicles, face significant challenges in meeting this demand due to their inherent reaction mechanisms and safety concerns associated with high current densities. Our work focuses on addressing the pressing need for extreme fast charging by introducing 1 Ah soft-package potassium-ion hybrid supercapacitors (PIHCs). These PIHCs are designed to leverage the combined merits of high-energy density from battery-type negative electrodes and high-power density from capacitor-type positive electrodes, offering a promising solution to the challenges posed by conventional Li-ion batteries. The architecture of the PIHCs is meticulously engineered to optimize performance while maintaining safety and reliability. The positive electrode of the PIHCs is composed of defect-rich, high specific surface area N-doped carbon nanotubes, facilitating efficient ion adsorption and desorption processes. On the other hand, the negative electrode incorporates MnO quantum dots inlaid within spacing-expanded carbon nanotubes, which provide ample active sites for potassium-ion storage and promote rapid charge transfer kinetics. Crucially, the electrolyte chosen for the PIHCs is a carbonate-based non-aqueous solution, carefully selected to ensure compatibility with the electrode materials and enable high ionic conductivity. Furthermore, the PIHCs feature a binder- and current collector-free cell design, reducing the overall weight and improving the energy density of the devices. Through systematic optimization of the cell configuration, electrode materials, and electrolyte composition, we have achieved remarkable performance metrics for the PIHCs. The full cells (1 Ah) exhibit a cell voltage of up to 4.8 V, enabling efficient energy storage and utilization. Importantly, the PIHCs demonstrate a high full-cell level specific energy of 140 Wh kg−1, calculated based on the entire mass of the device. Moreover, the extreme fast charging capability of the PIHCs is demonstrated through their remarkable charging time of only 6 minutes for a full 1 Ah capacity. This exceptional charging speed significantly enhances the usability and practicality of the PIHCs in various real-world applications, where rapid energy replenishment is essential. In addition to fast charging, the PIHCs exhibit excellent cycling stability and voltage retention under high current conditions. After 200 cycles at 10 C (corresponding to a discharge current of 10 A), the PIHCs maintain an impressive 88% capacity retention, highlighting their robustness and durability. Furthermore, the voltage retention of the PIHCs remains at 99% at a stable temperature of 25 ± 1 °C, underscoring their reliability and consistency over extended usage periods. Overall, the development of 1 Ah soft-package potassium-ion hybrid supercapacitors represents a significant advancement towards meeting the growing demand for extreme fast charging in electrochemical energy storage devices. By combining the advantages of battery-type and capacitor-type electrodes, along with innovative cell design and electrolyte selection, the PIHCs offer a compelling solution to the challenges posed by conventional Li-ion batteries, paving the way for the next generation of extreme fast charging energy storage technologies.[1]Ref. 1 H. Li etal. Nature Communications 14 (1), 6407 Figure 1

RANCANG BANGUN SISTEM JARINGAN FAILOVER AUTOMATIC TELLER MACHINE (ATM) MELALUI JARINGAN PUBLIK BERBASIS PROTOCOL EOIP DAN P2TP

The network on the Automatic Teller Machine currently uses a VSAT (Very Small Aperture Terminal) network based on Geostationary Satellites. VSAT networks often experience problems such as UP-DOWN or total DOWN. Some of the problems that cause interruptions on the VSAT interconnection include the usage period of the Modem electronic device and also ODU (Outdoor Unit) devices such as RFU, LNB, and BUC, ODU devices have a transmit period and also a received period that is uncertain according to the voltage at the location. Problems with the VSAT interconnection system cause ATM services to become unusable, resulting in losses for the bank's profits. In addition, the repair process is also generally quite time-consuming. The purpose of this study is to design an inexpensive ATM backup network solution so that when there is a break/disruption in the VSAT network the service on the ATM is not interrupted and in terms of operational costs does not swell. The backup network is intended to back up the satellite-based leading network automatically so that ATM services are always available when the satellite-based network is interrupted. The result of the study is that the proposed solution can resolve service outages at ATM machines in 34 seconds. The ATM network system will experience disconnection before finally reconnecting through the backup network. The proposed solution is much cheaper than the VSAT-based failover network solution, as it is based on the public internet network.

Comparison of Bacterial Adherence on Different Archwires after Clinical Use

ABSTRACT Background: An essential component of orthodontic therapy, orthodontic archwires may operate as surfaces for bacterial adhesion, which might result in issues with oral health. Materials and Methods: This study assessed the bacterial adhesion to three different kinds of orthodontic archwires: beta-titanium (TMA), nickel-titanium (NiTi), and stainless steel (SS). At the conclusion of the clinical usage period, samples of each kind of archwire were gathered and submitted to microbiological investigation in order to determine the quantity of adhering bacterial colonies. Using scanning electron microscopy (SEM), the surface properties of the archwires were evaluated after usage. Results: The three kinds of archwires had significantly different rates of bacterial adhesion, according to the findings. With an average of 1500 ± 50 CFU/mm2, SS archwires showed the highest level of bacterial adhesion, followed by NiTi archwires at 1100 ± 45 CFU/mm2. The least amount of bacteria adhered to TMA archwires, with an average of 800 ± 30 CFU/mm2. According to SEM pictures, the degree of bacterial adhesion was associated with the archwires’ surface roughness, with smoother surfaces showing less bacterial colonization. Conclusion: In comparison with SS and NiTi archwires, the research finds that TMA archwires are linked to the least amount of bacterial adhesion.

E-Fuel: An EV-Friendly Urgent Electrical Charge-Sharing Model with Preference-Based Off-Grid Services

Electric-powered vehicles (EVs) allow for an environmentally friendly and economic alternative to fuel-running ones. However, such an alternative is expected to impose further usage hikes and periods of instability on cities’ power systems. From their perspective, cities need to scale their infrastructure grids to allow for adequate power resources to feed such new power-hungry consumers. Indeed, for such a green alternative to proceed, our power grids need to be ready to cope with any unexpected hikes in the power consumption rates without compromising the stability of the services provided to our homes and workplaces. Operators’ steps in this path are still modest, and the coverage of EV charging stations is still insufficient as they are trying to avoid any further costs for upgrading their infrastructures. The lack of price consideration for the charging services offered at charging stations may result in EV drivers paying higher costs compared to traditional fuel vehicles to charge their EVs’ batteries, hindering the economic incentive of owning such sorts of vehicles. Hence, it may take a while for sufficient coverage to exist. Although for drivers the adoption of EVs represents a city-friendly alternative with affordable expenses, it usually comes with range anxiety and battery charging concerns. In this work, we are presenting e-Fuel, a charge-sharing model that allows for preference-based mobile EV charging services. In e-Fuel, we are proposing a stable weight-based vehicle-to-vehicle matching algorithm, through which drivers of EVs will be capable of requesting instant mobile charge-sharing service for their EVs. In addition to being mobile, such charging services are customized, as they are chosen based on the drivers’ preferences of price-per-unit, charging speed, and time of delivery. The developed e-Fuel matching algorithm has been tested in various environments and settings. Compared to the benchmark price-based matching algorithm, the resulting matching decisions of e-Fuel come with balanced matching attributes that mostly allow for 6- to 7-fold shorter service delivery times for a minimal increase in service charges that vary between 9% and 65%.

CTIM-06. OUTCOME OF PATIENTS WITH RECURRENT MEDULLOBLASTOMA AND EPENDYMOMA TREATED WITH PEMBROLIZUMAB, AN IMMUNE CHECKPOINT INHIBITOR: A PEDIATRIC BRAIN TUMOR CONSORTIUM STUDY (PBTC045)

Abstract BACKGROUND Children with recurrent medulloblastoma and ependymoma typically have a dismal prognosis. Immune checkpoint blockade has revolutionized treatment of some resistant cancers. PBTC045 is a safety and preliminary efficacy study of pembrolizumab, a PD-1 inhibitor, in children with recurrent/progressive brain tumors. Here we present the strata that included children with recurrent/progressive ependymoma and medulloblastoma. METHODS Eligibility included patients aged 1-21 years old with recurrent/progressive ependymoma or medulloblastoma and with functional scores ≥70, no greater than physiologic steroid usage and standard washout periods, among other eligibility criteria. Pembrolizumab was administered at 2mg/kg IV every three weeks. Tumor tissue (if available) and serum were collected for correlative studies. Primary endpoints included toxicity and response, and two stages were planned requiring objective responses for potential expansion. RESULTS Patients with progressive ependymoma (Stratum D, n=13) or medulloblastoma (Stratum E, n=14, one ineligible) were enrolled. The median age at diagnosis was 3.37 years (range, 1-12.7) and 7 years (range, 2.2-13.1), respectively. The median age at study entry was 8.5 years and 11.4 years, respectively. There were no first-stage objective responses in either stratum, and thus the expansion arms were not initiated. Grade 3 or greater treatment-related adverse events included rash (n=1), anemia (n=1) and neutropenia (n=2), with no dose-limiting toxicities. The median progression-free survival (PFS) for ependymoma and medulloblastoma were 1.38 months (range, 1.08-1.51) and 1.41 months (range, 1.22-3.45), respectively. Immune correlative analyses are ongoing. CONCLUSIONS Immune checkpoint inhibition holds potential for CNS cancer treatment, and as a single agent, is well tolerated. However, in the current study, checkpoint inhibition monotherapy does not appear to significantly prolong survival or induce radiographic responses in children with recurrent/progressive ependymoma or medulloblastoma. Potential incorporation of checkpoint inhibitors into treatment for these patients will likely require combinatorial approaches that may be informed by the correlative science.

Investigating illicit drug hotspots and daily variations using sewer-network wastewater analysis

Previous wastewater-based epidemiology (WBE) research on illicit drug use has predominantly focused on wastewater treatment plant (WWTP) influents, but information on sewer-network wastewater is very limited. This study represents a pioneering small-scale WBE investigation based on the analysis of sewer-network wastewater samples from different sewer manholes in suburban (Tamsui region) and urban areas (Zhongshan and Wanhua regions) and a comparison of the results with those obtained from corresponding WWTP influents. Among sixteen illicit drugs, methamphetamine exhibited the highest concentration in sewer-network wastewater across both areas. Suburban–urban variations were observed, with more types of illicit drugs detected in the suburban area. Back-calculation indicated that methamphetamine and ketamine were the most-consumed illicit drugs in both sewer-network wastewaters and WWTP influents. Similar types of illicit drugs were detected in the sewer-network wastewaters and WWTP influents, indicating the representativeness of WWTP influents in assessing regional illicit drug abuse. Nevertheless, the sewer-network wastewater results offered additional information making it possible to pinpoint potential hotspots of illicit drug and identify peak usage periods throughout the day, in contrast to the WWTP influent results. In the non-suspected suburban area of Tamsui, high potential hotspots of methamphetamine (sampling points 3 and 6) and ketamine (sampling points 1 and 8) were identified. Although the Zhongshan and Wanhua regions were chosen as suspected hotspots of illicit drug abuse, more severe illicit drug use was observed in Wanhua. Moreover, a trend toward higher illicit drug use from early morning to morning was observed. Despite sampling challenges and higher costs, small-scale WBE via sewer-network wastewater analysis provides superior identification of drug abuse hotspots and peak usage periods. Therefore, this study provides valuable insights for law enforcement and can help prevent and combat illicit drug abuse by targeting potential hotspots and understanding daily illicit drug use dynamics.

A study on the water content in distribution pole transformer using random forest model

This study proposes and validates an artificial intelligence (AI)-based method for estimating the water content in the insulating oil of distribution-level transformers. The methodology includes data augmentation using noise addition, outlier removal via Isolation Forest, and data normalization through square root transformation. A Random Forest (RF) model is developed to estimate water content based on the usage period of the transformer. Correlation analyses identified the usage period as the key variable affecting water content. The model demonstrated high estimation accuracy with an R-squared value of 0.83, closely aligning estimated values with measured data. This approach provides a practical solution for real-world applications, expanding the focus to distribution-level transformers and ensuring reliable estimations through validation with actual field data. Despite limitations due to a dataset comprising 100 samples of transformer usage and oil analysis data, the method shows promise for accurate transformer lifespan assessment and efficient asset management. Future research will enhance model performance by incorporating diverse environmental conditions and comparative analyses with other machine learning (ML) algorithms, aiming to optimize estimation reliability and safety for distribution-level transformers. Consistency in the methodology description and actual models used will be maintained to avoid discrepancies.

Quality Parameter Adaptive Optimization for Spinning Process Using Dynamic Non-Dominated Sorting Algorithm

ABSTRACT Intelligent textile equipment can discover potential patterns in the production process through data mining, and utilize these patterns through intelligent optimization, ultimately achieving intelligent and automated textile production. This paper focuses on the spinning process parameters optimization under changing spinning conditions and proposes a dynamic non-dominant ranking parameter quality adaptive optimization algorithm. The factors of spinning process condition changes are transformed into mathematical dynamic constraints and constructing an adaptive optimization model for spinning parameter quality. Based on this, the response mechanism of spinning environment is established to readjust the optimization direction according to the change of spinning conditions, and the DNSGA-II is used to solve the quality adaptive optimization model. A case study is designed to validate the effectiveness, results show that for different usage periods of wire rings, the optimal breaking strength is 5.6, and the number of details is 33.3, 31.1, and 41.6 respectively. In some degree, the proposed algorithm can effectively adapt to the quality optimization problem of spinning process parameters under different spinning conditions, which could provide corresponding parameter optimization combinations for different spinning conditions.

Analysis of the Creep Mechanism of Low-Alloy Steel in Terms of Plastic Deformation

As the proportion of renewable energy sources within the energy grid increases, boiler operations increasingly rely on managing disparities in energy supply. This condition substantially curtails their operational lifespan due to frequent switching cycles. Materials exposed to prolonged stress at high temperatures in harsh environments gradually degrade and eventually fail catastrophically. Thus, understanding processes like creep is essential for accurately evaluating the condition of operational components under new operational standards in power plants. In this regard, this paper introduces an innovative methodological framework for analyzing the creep mechanism, focusing on the plastic deformation of a crucial pipeline segment, specifically an elbow composed of 14MoV6-3 steel, both before and after extensive usage periods (164,000 and 302,000 h). The study explored the development of microstrain from the material's surface employing the electron backscattered diffraction method. This analysis assessed how operational durations influence dislocation structural changes, as examined by synchrotron radiation techniques, across a material depth from 0 to 1.5 mm. Based on these observations, the extent of deformation over time was demonstrated. Furthermore, the evolution of precipitation processes was investigated through transmission electron microscopy. These tests allowed to obtain and compare information on the dislocation structure of the tested steel after service in creep conditions, of small and large volumes of material.

Awareness Regarding Medical Device Safety among Physicians: A Knowledge, Attitude, and Practice Survey

Abstract Background: With the huge inflow of medical devices into the healthcare market, it is imperative to monitor the device safety throughout its period of usage, with the help of a systematic continual surveillance mechanism. Although the Materiovigilance Program of India has been formally introduced since 2015, there exists a dearth of awareness regarding its existence and functioning among the major stakeholders of patient safety. Addressing this gap, the study probes the awareness regarding materiovigilance among the doctors in eastern India. Methods: A cross-sectional questionnaire-based study was carried out using a 20-item structured survey tool to evaluate the knowledge, attitude, and practice of medical professionals regarding safety surveillance of medical devices. Responses obtained were statistically analyzed and interpreted. Results: Principal elements of medical device adverse event (MDAE) reporting were known to around 40% of respondents. However, the time frame for reporting an event/incident was found to be grossly unclear. Over 30% of respondents were aware of the effect of MDAEs and that its reporting could enhance patient safety. Items related to practice domains revealed that although around 50% of respondents of either category encountered MDAEs in their practice setup, only 10.95% of faculty and 6.41% of residents have reported it. Less than 5% of the respondents were ever trained on how to report MDAEs. The mean domain score for knowledge was on higher mark for the faculty, while that for attitude and practice domains were higher for the residents. Conclusion: There was a gross dearth of awareness regarding materiovigilance among the doctors. Enhanced sensitization regarding medical device safety surveillance in all healthcare levels can definitely help the situation ensuring patient safety.

Damage source localisation in complex geometries using acoustic emission and acousto-ultrasonic techniques: an experimental study on clear aligners

Passive non-destructive evaluation tools such as acoustic emission (AE) testing and acousto-ultrasonics (AU) approach present a complex problem in damage localisation in complex and nonhomogeneous geometries. A novel AU-guided AE frequency interpretation approach is proposed in this research work which aims at overcoming this limitation. For the experimental evaluation, the damage sources from a geometrically complex clear dental aligners are tested under cyclic compression load and their origins are evaluated. Despite the rapid worldwide diffusion of the clear aligners, their mechanical behaviour is poorly investigated. In this work, the frequency characteristics of the artificially simulated stress wave, generated from different dental positions of the clear aligners, are studied using the AU approach. These frequency characteristics are then used to analyse the AE signals generated by these aligners when subjected to cyclic compressive loading. In addition, the time domain characteristics of the AE signals are studied using their Time of Arrival (ToA). The Akaike Information Criterion (AIC) is used to estimate the ToA. These frequency and time domain characteristics of the AE signals are used to estimate the local damage origin in the clear dental aligners. This will help in identifying localised damage sources during the usage period of the aligners. Experimental results revealed significant damages in the left maxillary premolar and right maxillary third molar of the aligners.

Using green nanocomposite containing eggshell in the electroperoxone process in a baffled reactor to remove the emerging tetracycline pollutant

This study examined the eradication of Tetracycline hydrochloride (TCH) antibiotic, an emerging pollutant, by utilizing eggshell membrane activated carbon (EMAC) and magnetite (Fe3O4) nanocomposite in conjunction with the electroperoxone process employing the One Factor at a Time method (OFAT) in a baffled reactor. The nanocomposite was synthesized through the hydrothermal method using an autoclave, and its properties were assessed via XRD, FTIR, FESEM, EDAX Mapping, BET, and VSM analyses. The findings revealed that under optimal conditions (including a pollutant concentration of 300 mg/L, a natural pH of 6.2, an ozone consumption rate of 0.28 g/h, a nanocomposite concentration of 0.2 g/L, a flow intensity of 0.5 A, a wastewater recirculation flow rate of 8 L/h, and a 0.1 M Na2SO4 electrolyte concentration), 95.9%, 76.4%, and 53.4% of pollutants, COD, and TOC were respectively eliminated after 90 min. Additionally, the reusability of the nanocomposite was evaluated over five usage periods, during which the process efficiency decreased from 95.9% to 83.1%.In short, this study proved that EMAC/Fe3O4 nanocomposites are promising electroperoxone catalysts due to their low cost, excellent stability and reusability, environmental compatibility, and superior catalytic activity for TCH antibiotics removal.

Acceptance of an IoT System for Strawberry Cultivation: A Case Study of Different Users

The Internet of Things (IoT) has been impacting multiple industries worldwide for over a decade. However, less developed countries have yet to make the transition to these technologies. South America is among the regions with the least IoT influence in all sectors, indicating a need for studies to explore IoT acceptance among various users in this region. This study analyzes two different users of a monitoring and irrigation system for strawberry (Fragaria × ananassa) farming. Monitored variables include soil moisture, and ambient temperature and humidity, with irrigation performed via water pumping from a reservoir. The system is based on the M5Core2 development kit for the local station and the IoT platform ThingSpeak for remote access. It features a web user interface consisting of an application developed in HTML using a plugin on ThingSpeak. Thus, the system can be used locally via a touchscreen and remotely through a web browser. Measurements are cross-verified with commercial meters to ensure their reliability, and users are asked to fill out a Technology Acceptance Model (TAM) for IoT to gauge their acceptance level. Additionally, an interview is conducted that explores four critical factors, aimed at understanding their experience and interaction with the system after a period of usage. The findings confirm the validity of the monitored variables and demonstrate a global acceptance rate of slightly over 80%, albeit with varying user acceptance perspectives. Specifically, the technical user exhibits greater acceptance than the crop administrator, evidenced by a mean discrepancy of 1.85 points on the TAM scale.

Bike Sharing Demand Forecasting Based on the Informer Model

Advancements in machine learning and artificial intelligence have been instrumental in enhancing demand forecasting for shared transportation modes, significantly benefiting urban traffic management and promoting a balanced supply-demand dynamic. This study harnesses the deep learning Informer model to forecast bike-sharing demand, integrating multiple factors to provide an empirical foundation for transport scheduling and redeployment, thereby contributing to the advancement of the field. Our experimental analysis reveals that the Informer model outperforms LSTM in predictive accuracy, as evidenced by improved R² scores and MSE values, reflecting gains in training efficiency and predictive precision. Despite these advances, predictions for extreme demand values, particularly during peak usage periods, indicate potential areas for refinement. Future research directions are oriented towards optimizing model accuracy by leveraging multimodal data for a comprehensive analysis and enhancement of traffic management strategies. This approach aims not only to better predict demand and alleviate congestion but also to deliver more optimized solutions for businesses and convenience for users. Furthermore, the model's application extends beyond bike-sharing to other forms of shared mobility, such as electric scooters, potentially aiding in reducing carbon emissions and furthering the development of the sharing economy and environmental sustainability.