Optimal Level Research Articles

An experimental investigation of process parameters on material removal and surface roughness improvement in abrasive flow machining

Abstract Today, there is huge demand for mechanical components with long life cycles, and superior surface finishes as a result of the industrial revolution. New methods for nano-finishing complex structures, including abrasive flow machining (AFM), have been developed in response to the need for superior surface finish. This is a relatively new technique in non-conventional machining techniques. Because abrasion only happens in locations where flow is restricted, this approach is utilized to polish metallic parts as well as interior, inaccessible cavities, and recesses using a semi-liquid paste. It has evolved to flow of abrasives with a viscoelastic polymer (referred to as abrasive media) over complex geometries, and edges to deburr, and radius those surfaces. This paper focuses on investigating the impact of process parameters on material removal, and percentage improvement in surface roughness on cylindrical brass workpieces using Taguchi L9 orthogonal array, the number of cycles, extrusion pressure, and grit size of abrasives have been selected. The abrasive medium employed in this investigation is composed of a blend of polymer, hydrocarbon gel, and aluminium oxide abrasive particles with varying grit sizes. The outcome shows which process parameters to optimize for material removal, and improvement in percentage improvement in surface roughness. The number of cycles that have the largest percentage contribution to material removal was 83.74%. For the response material removal, the percentage contributions of the abrasive particle grit size, and extrusion pressure are 2.03%, and 14.16%, respectively. N2P2G3 has the optimal level used for material removal. The largest percentage contribution to improvement in surface roughness, or 83.48%, was the result of the number of cycles. Extrusion pressure, and abrasive particle grit size account for 7.38%, and 8.88% of the total percentage contribution, respectively. N3P2G3 has the optimal level obtained for percentage improvement in surface roughness improvement.

Myths and methodologies: Assessing glycaemic control and associated regulatory mechanisms in human physiology research.

Accurate measurements of glycaemic control and the underpinning regulatory mechanisms are vital in human physiology research. Glycaemic control is the maintenance of blood glucose concentrations within optimal levels and is governed by physiological variables including insulin sensitivity, glucose tolerance and β-cell function. These can be measured with a plethora of methods, all with their own benefits and limitations. Deciding on the best method to use is challenging and depends on the specific research question(s). This review therefore discusses the theory and procedure, validity and reliability and any special considerations of a range common methods used to measure glycaemic control, insulin sensitivity, glucose tolerance and β-cell function. Methods reviewed include glycosylated haemoglobin, continuous glucose monitors, the oral glucose tolerance test, mixed meal tolerance test, hyperinsulinaemic euglycaemic clamp, hyperglycaemic clamp, intravenous glucose tolerance testand indices derived from both fasting concentrations and the oral glucose tolerance test. This review aims to help direct understanding, assessment and decisions regarding which method to use based on specific physiology-related research questions.

The role of psychological formulation in inpatient settings in supporting staff empathy and therapeutic optimism for adults diagnosed with borderline personality disorder: A pre- and post-vignette study.

OBJECTIVES: National Health Service (NHS) values, such as empathy and therapeutic optimism, are integral when supporting service users with complex mental health presentations. There is some evidence to suggest that psychological formulation can increase empathy and optimism in healthcare professionals. This study, therefore, aimed to investigate whether a psychological formulation of a hypothetical service user with a complex presentation, typically labelled with a diagnosis of borderline personality disorder (BPD), increased empathy and therapeutic optimism in professionals working in mental health inpatient services. Sixty-six mental health professionals working in NHS inpatient services took part in a pre- and post-vignette study. Participants were asked to read a case vignette about a hypothetical service user, with a diagnostic label of BPD, and complete questionnaires capturing levels of empathy and therapeutic optimism. Participants were then randomized into two conditions and either asked to read the same information again (control condition) or read a psychological formulation based on the same hypothetical service user (intervention condition). The findings were analysed using a series of ANCOVAs/ANCOHETs. Two constructs of empathy (i.e. perspective taking and empathic concern), and therapeutic optimism significantly increased following exposure to the psychological formulation when compared to the control group condition. This study warrants further replication. These initial findings; however, indicate that psychological formulation can significantly increase the ability to perspective take, display empathic concern, and hold therapeutic optimism towards service users with a presentation associated with a diagnosis of BPD.

Mechanistic insights into rumen function promotion through yeast culture (Saccharomyces cerevisiae) metabolites using in vitro and in vivo models

IntroductionYeast culture (YC) enhances ruminant performance, but its functional mechanism remains unclear because of the complex composition of YC and the uncertain substances affecting rumen fermentation. The objective of this study was to determine the composition of effective metabolites in YC by exploring its effects on rumen fermentation in vitro, growth and slaughter performance, serum index, rumen fermentation parameters, rumen microorganisms, and metabolites in lambs.MethodsIn Trial 1, various YCs were successfully produced, providing raw materials for identifying effective metabolites. The experiment was divided into 5 treatment groups with 5 replicates in each group: the control group (basal diet without additives) and YC groups were supplemented with 0.625‰ of four different yeast cultures, respectively (groups A, B, C, and D). Rumen fermentation parameters were determined at 3, 6, 12, and 24 h in vitro. A univariate regression model multiple factor associative effects index (MFAEI; y) was established to correlate the most influential factors on in vitro rumen fermentation with YC metabolites (x). This identified the metabolites promoting rumen fermentation and optimal YC substance levels. In Trial 2, metabolites in YC not positively correlated with MFAEI were excluded, and effective substances were combined with pure chemicals (M group). This experiment validated the effectiveness of YC metabolites in lamb production based on their impact on growth, slaughter performance, serum indices, rumen parameters, microorganisms, and metabolites. Thirty cross-generation rams (Small tail Han-yang ♀ × Australian white sheep ♂) with good body condition and similar body weight were divided into three treatment groups with 10 replicates in each group: control group, YC group, pure chemicals combination group (M group).ResultsGrowth performance and serum index were measured on days 30 and 60, and slaughter performance, rumen fermentation parameters, microorganisms, and metabolites were measured on day 60. The M group significantly increased the dressing percentage, and significantly decreased the GR values of lambs (p < 0.05). The concentration of growth hormone (GH), Cortisol, insulin (INS), and rumen VFA in the M group significantly increased (p < 0.05).DiscussionThese experiments confirmed that YC or its screened effective metabolites positively impact lamb slaughter performance, rumen fermentation, and microbial metabolism.

Comparison of drying methods for biochemical composition, energy aspects, and color properties of alfalfa hay

AbstractAlfalfa is a crucial feed source for livestock, necessitating proper drying to achieve optimal moisture levels before consumption. This study aims to reveal alfalfa's drying characteristics and quality properties, defined as the queen of feed, by drying it under different drying conditions. Alfalfa samples were dried using different drying techniques: Open-sun, shade, greenhouse, hybrid (microwave-air-convective), air-convective, microwave, and freeze-drying. The influence of drying techniques on the drying kinetics, energy aspects, biochemical properties, mineral composition, fatty acids, carotene, and color attributes of alfalfa were investigated. Present findings revealed that shade and high-temperature drying conditions increased the crude fiber content (23.18%) of alfalfa samples. The highest protein values were found in open-sun drying (22.01%) and 60 °C air-convective drying (22.10%). The highest values for mineral composition were determined in freeze drying, and the greatest Zn (33.68 ppm) and Fe (135.45 ppm) contents were determined in 100 °C air-convective drying. The highest saturated fatty acids open-sun (21.27%) and freeze dryer (21.07%), unsaturated fatty acids at 60 °C (84.51%) and 80 °C (84.26%), poly unsaturated fatty acids at 60 °C (78.36%), 80 °C (78.14%) and 100 °C (77.74%) were obtained. The drying kinetics of alfalfa were best modeled with the Jena&Das, and in terms of a* (greenness) values, the hybrid drying yielded the best results. The lowest total energy consumption (54.00 kWh) was seen in 200W + 80 °C hybrid drying and the highest (324.72 kWh) in 100 °C air-convective drying. The color attributes and energy consumption findings showed that hybrid drying could efficiently be used for alfalfa drying. In addition, freeze drying was determined to be the best drying method in terms of nutrient preservation.

Leveraging Artificial Intelligence for Enhanced Performance in Matrix Organizations: A Research Perspective

In today's complex business landscape, matrix organizations represent a prevalent structural framework characterized by overlapping reporting structures and cross-functional project teams. This organizational model, while offering flexibility and specialization, also presents unique challenges in resource allocation, decision-making, collaboration, and project management. This research paper investigates the potential of artificial intelligence (AI) to address these challenges and enhance the performance of matrix organizations. Drawing upon an extensive review of existing literature and relevant case studies, the paper provides a comprehensive analysis of AI's role in optimizing various aspects of matrix organization operations. Specifically, it examines how AI technologies can streamline resource allocation processes by analyzing project requirements, team capabilities, and individual skill sets to ensure optimal staffing levels and skill matching across projects. Additionally, the paper explores AI's capabilities in providing decision support to managers and team leaders by leveraging data analytics to offer insights into project progress, risk assessment, and performance evaluation.

Potato Consumption Forecasting Based on a Hybrid Stacked Deep Learning Model

AbstractPotato consumption forecasting is crucial for several stakeholders in the food market. Due to the market flexibility, the farmers can manipulate the volumes planted for a given type of produce to reduce costs and improve revenue. Consequently, it means that establishing optimal inventories or inventory levels is possible and critical in that sense for the sellers to avoid either inadequate inventory or excessive inventories that may lead to wastage. In addition, governments can predict future food deficits and put measures in place to guarantee that they have a steady supply of food some of the time, especially in regions that involve the use of potatoes. Increased potato-eating anticipation has advantages for the sellers and buyers of the potatoes. The experiments of this study employed various machine learning and deep learning (DL) models that comprise stacked long short-term memory (Stacked LSTM), convolutional neural network (CNN), random forest (RF), support vector regressor (SVR), K-nearest neighbour regressor (KNN), bagging regressor (BR), and dummy regressor (DR). During the study, it was discovered that the Stacked LSTM model had superior performance compared to the other models. The Stacked LSTM model achieved a mean squared error (MSE) of 0.0081, a mean absolute error (MAE) of 0.0801, a median absolute error (MedAE) of 0.0755, and a coefficient of determination (R2) value of 98.90%. These results demonstrate that our algorithms can reliably forecast global potato consumption until the year 2030.

Assessment of Food Masticatory Capability with Clear Aligners

Nowadays, aligners represent a possible therapeutical approach that combines both esthetic and function in order to address dental malocclusion. However, they require a significant level of compliance from the patient. According to the manufacturer, at least 22 h of wearing a day is demanded to reach the optimal therapeutical level; hence, aligners can only be removed during meals. Patients’ compliance might increase and the duration of the treatment might decrease if they were allowed to eat with aligners on. The idea of patients keeping the aligners on during meals has been contemplated, not only to favor patients’ compliance but also treatment effectiveness. This study aims to assess the degree of chewing difficulty that aligners cause when eating certain kinds of food and the quantity of residue left. Material and Methods: A questionnaire titled “Questionnaire for the Assessment of Masticatory Function with Aligners” was administered using Google Forms to 240 patients in treatment with clear aligners. The survey was validated through the reliability test using the test–retest method. This method had a higher correlation coefficient of 0.9 across all items (with a cutoff of 0.8) with statistical significance, and an excellent internal correlation coefficient (α > 0.9). The statistical analysis performed consisted of descriptive analysis, frequencies, percentages, Pearson’s correlation test and Friedman’s test. Results: Pearson’s test showed a statistically significant correlation between all items except between meat or clams and yogurt or ice cream and with mozzarella or soft cheese regarding food chewing difficulties. Pearson’s test showed a statistically significant correlation between all items regarding food residues. A total of 69.2% of the cohort reported some movements of aligners during mastication. In total, 88.3% of them affirmed not to have perceived deformations or breakage of aligners during chewing. Furthermore, 79.2% of them declared that they would continue to eat if eating with aligners was proven to speed up treatment. Conclusions: Wearing clear aligners while chewing foods such as yogurt, ice cream, soft cheese, bread, rice, etc., can be possible and can help shorten the duration of orthodontic treatment, benefiting both the patient and the orthodontist. However, further research using qualitative methods is needed to understand the barriers and facilitators to chewing food with aligners.

Optimal management of international reserves

AbstractThis article develops a continuous time stochastic model for determining the optimal international reserves management policy when the data generating process of international reserves is a geometric Brownian motion. The policy is a two‐parameter control‐limit that consists of an appropriate and a ceiling level on reserves holdings, given an exogenous floor on international reserves. The optimal solution is determined so as to minimize the total costs of international reserves holdings. It is proved that our results extend the framework of Frenkel and Jovanovic when the logarithm of international reserves is an arithmetic Brownian motion. We also explain that Jung paper does not extend Frenkel and Jovanovic model as claimed by the former. The model is calibrated to derive the optimal level and liquidity tranche size upper bound of international reserves of the CEMAC monetary union.

Role of senataxin in R-loop mediated neurodegeneration

Abstract Senataxin (SETX) is an RNA:DNA helicase that plays an important role in the resolution of RNA:DNA hybrids (R-loops) formed during transcription. R-loops are involved in the regulation of biological processes such as immunoglobulin class switching, gene expression, and DNA repair. Excessive accumulation of R-loops results in DNA damage and loss of genomic integrity. SETX is critical for maintaining optimal levels of R-loops to prevent DNA damage and acts as a genome guardian. Within the nucleus, SETX interacts with various RNA processing factors, and DNA damage response and repair proteins. SETX interactors include survival motor neuron (SMN) and zinc finger protein 1 (ZPR1), with whom it colocalizes in sub-nuclear bodies. Despite its ubiquitous expression, mutations in SETX specifically affect neurons and result in distinct neurodegenerative diseases such as amyotrophic lateral sclerosis type 4 (ALS4) and ataxia with oculomotor apraxia type 2 (AOA2), which are attributed to the gain-of-function and the loss-of-function mutations in SETX, respectively. In addition, low levels of SETX (loss-of-function) in spinal muscular atrophy (SMA) result in the accumulation of R-loops causing DNA damage and motor neuron degeneration. SETX may play multiple functions in diverse cellular processes, however, its emerging role in R-loop resolution and maintenance of genomic integrity is gaining attention in the field of neurodegenerative diseases. In this review, we highlight the role of SETX in R-loop resolution and its potential as a therapeutic target to treat neurodegenerative diseases.

Influence of FexOy and Al2O3 Contents on the Thermal Stability of Iron Ore-Waste Fibers: Key Mechanisms and Control

Traditional rock wool fibres are susceptible to crystallization and pulverization. To mitigate this, glass fibres were produced from iron ore waste (IOW). When the ratio of Fe2+ and Fe3+ is 1:3 and the Al2O3 content is 10 wt.%, increasing the FexOy content enhances the thermal stability.At an FexOy content of 17–19% and an Al2O3 content of 10–13%, the glass transition temperature (Tg) peaked. Increasing the FexOy content from 10% to 20% enhanced the stability of Si-O and Al-O bonds and increased bridged oxygen, stabilizing the structure. Here, Fe2+ balances structural charges, while Fe3+ replaces some Al atoms in the network. When the Al2O3 content is 10–13% and the FexOy content is 17–19%, the thermal stability of the IOW rock glass reaches its optimal level. At 20% FexOy content, the structure becomes three-dimensional and cyclic, increasing polymerization. Consequently, incorporating FexOy alongside a 10% Al2O3 content improves thermal stability, supporting the development of high-stability rock wool from IOW. This approach also enhances the refractory properties of rock wool fibres within the FexOy-Al2O3-SiO2-MgO-CaO system.

The Risk of Atrial Fibrillation and Previous Ischemic Stroke in Cognitive Decline

Objectives: Our study investigated the inverse relationship between cognitive decline (CD) and the presence of documented atrial fibrillation (AFib), ischemic stroke, heart failure, lower extremity peripheral artery disease, and diabetes mellitus. Methods: We conducted a retrospective cross-sectional study between December 2016 and November 2019. A total of 469 patients were enrolled who underwent cognitive evaluation with three cognitive tests (Montreal Cognitive Assessment—MOCA, Mini-Mental State Examination—MMSE, and General Practitioner Assessment of Cognition—GPCOG). We used the standard cut-off values, and the optimal thresholds were obtained from the receiver operating characteristic curves. Results: The standard cut-off level of the MOCA (<26 points) was associated with the presence of AFib (OR: 1.83, 95% CI: 1.11–3.01) and the optimal cut-off level with <23 points with ischemic stroke (OR: 2.64, 95% CI: 1.47–4.74; p = 0.0011). The optimal cut-off value of the MMSE (<28 points) was associated with the presence of ischemic stroke (OR: 3.07, 95% CI: 1.56–6.07; p = 0.0012), AFib (OR: 1.65, 95% CI: 1.05–2.60; p = 0.0287), and peripheral artery disease (OR: 2.72, 95% CI: 1.38–5.36; p = 0.0039). GPCOG < 8 points were associated with ischemic stroke (OR: 2.18, 95% CI: 1.14–4.14; p = 0.0176) and heart failure (OR: 1.49, 95% CI: 1.01–2.21; p = 0.0430). Conclusions: Our research highlighted the broader utility of cognitive assessment. The MOCA and MMSE scores proved to be associated with documented AFib. Higher cognitive test results than the standard threshold for CD of the MMSE, GPCOG, and lower MOCA scores represented risk factors for the presence of previous ischemic stroke.

Optimal reserve policies for emergency medical supplies: Joint consideration of reserving safety stocks, production capacity, and capital

The tragic consequences of the COVID-19 epidemic highlight to governments the importance of keeping sufficient medical supplies. Given that the physical stocks are subject to considerable obsolescence risk, existing studies have considered combining holding safety stocks with keeping production capacity or capital reserve. Pioneering the exploration of keeping all three resources of safety stocks, production capacity, and capital in reserve, we derive closed-form optimal reserve policies under different scenarios. One crucial insight is that the optimal reserve policies should be determined by considering both the cost and utilization efficiency, e.g., solely adopting safety stocks is optimal when the utilization efficiency of safety stocks exceeds that of production capacity and capital reserves, and when the expected benefit of utilizing safety stocks outweighs the cost. Additionally, we find that the optimal safety stocks level is impacted by the combination of demand uncertainty and epidemic outbreak probability, i.e., the optimal safety stocks level decreases (resp., increases) with demand uncertainty in case with a sufficiently low (resp., high) epidemic outbreak probability, whereas the optimal levels of production capacity and capital always increase with demand uncertainty. We show that coordinating the three resources brings extra benefits compared with keeping fewer resources. By exploring the practical scenario where the government faces multiple potential epidemics, we find that below a threshold, a lower outbreak probability of a large-scale epidemic leads to more capital and production capacity reserves.

Impact and Interrelationships of Striatal Proteins, EPHB2, OPRM1, and PER2 on Mild Cognitive Impairment.

With the global increase in life expectancy, there has been a rise in the incidence of cognitive impairments attributed to diverse etiologies. Notably, approximately 50% of individuals diagnosed with mild cognitive impairment (MCI) progress to dementia within 3years. However, the precise mechanisms underlying MCI remain elusive. Therefore, this study aimed to elucidate potential mechanisms implicated in MCI utilizing Per2 knockout (KO) mice, which have previously been shown to have cognitive deficits. Behavioral (Y-maze, Barnes maze) and molecular (electrophysiology, RNA sequencing, western blot, and immunofluorescence) experiments were conducted in Per2 KO and wild-type (WT) mice. Per2 KO mice exhibited impaired spatial working memory in the Y-maze and Barnes maze. However, there were no significant group differences in hippocampal long-term potentiation (LTP) between Per2 KO and WT mice, whereas striatal LTP in Per2 KO mice was lower compared to WT mice. In RNA sequencing analysis, 58 genes were downregulated and 64 genes were upregulated in the striatum of Per2 KO mice compared to WT mice. Among the differentially expressed genes, four genes (Chrm2, EphB2, Htr1b, Oprm1) were identified. Optimal expression levels of EPHB2 and OPRM1 were found to significantly enhance cognitive performance in mice. Additionally, Per2 KO mice exhibited reduced EPHB2-NMDAR-LTP and OPRM-mTOR signaling, along with elevated amyloid beta (Aβ) levels, when compared to WT mice. However, these alterations were reversed upon administration of morphine treatment. Striatal OPRM1-mTOR signaling, EPHB2-NMDAR-LTP signaling, and Aβ expression levels may exert a combined effect on MCI under the control of Per2 expression.

Production of sustainable GGBFS-based Self-Compacting Geopolymer Concrete containing E-waste aggregates under ambient temperature curing

Geopolymer concrete (GPC) has gained global attention due to its environmentally friendly composition and a reduction in the carbon footprint compared to conventional concrete. Self-compacting geopolymer concrete (SCGC) combines the environmental benefits of geopolymers with the convenience of self-compacting concrete (SCC), offering a sustainable solution that minimizes manual labor, reduces noise pollution, and ensures a high-quality finish in diverse construction locations. The rapid proliferation of electronic devices in contemporary society has substantially enhanced electronic waste (e-waste), creating significant environmental challenges. This study explores a sustainable approach in managing the menace created by e-waste through its partial replacement against natural aggregates in producing ground granulated blast furnace slag (GGBFS)-based SCGC under ambient temperature curing. The research methodology involves the preparation of eleven mixes, including the control SCGC mix (with no e-waste aggregate), utilizing printed circuit boards (PCBs) as partial replacement in natural fine and coarse aggregates at varying percentages, i.e., 10 %, 20 %, 30 %, 40 %, and 50 %. It is revealed through a detailed fresh property, mechanical property, durability property, and microstructural analysis that the optimal replacement levels with e-waste are 30 % NFA or 10 % NCA in producing a sustainable and workable SCGC comprising 28.2 % and 19.2 % of waste materials (GGBFS, EWF, and EWC) moving towards a sustainable concrete yield.

Ascorbate insufficiency disrupts glutamatergic signaling and alters electroencephalogram phenotypes in a mouse model of Alzheimer's disease

Clinical studies have reported that increased epileptiform and subclinical epileptiform activity can be detected in many patients with an Alzheimer's disease (AD) diagnosis using electroencephalogram (EEG) and this may correlate with poorer cognition. Ascorbate may have a specific role as a neuromodulator in AD as it is released concomitantly with glutamate reuptake following excitatory neurotransmission. Insufficiency may therefore result in an exacerbated excitatory/inhibitory imbalance in neuronal signaling. Using a mouse model of AD that requires dietary ascorbate (Gulo−/-APPswe/PSEN1dE9), EEG was recorded at baseline and during 4 weeks of ascorbate depletion in young (5-month-old) and aged (20-month-old) animals. Data were scored for changes in quantity of spike trains, individual spikes, sleep-wake rhythms, sleep fragmentation, and brainwave power bands during light periods each week. We found an early increase in neuronal spike discharges with age and following ascorbate depletion in AD model mice and not controls, which did not correlate with brain amyloid load. Our data also show more sleep fragmentation with age and with ascorbate depletion. Additionally, changes in brain wave activity were observed within different vigilance states in both young and aged mice, where Gulo−/-APPswe/PSEN1dE9 mice had shifts towards higher frequency bands (alpha, beta, and gamma) and ascorbate depletion resulted in shifts towards lower frequency bands (delta and theta). Microarray data supported ascorbate insufficiency altering glutamatergic transmission through the decreased expression of glutamate related genes, however no changes in protein expression of glutamate reuptake transporters were observed. These data suggest that maintaining optimal brain ascorbate levels may support normal brain electrical activity and sleep patterns, particularly in AD patient populations where disruptions are observed.

Graves' Disease in Hypopituitarism Due to Pituitary Apoplexy.

Central hypothyroidism and autoimmune hyperthyroidism are contrasting pathologies requiring careful hormone monitoring for restoring euthyroidism. Their coexistence is rare and challenging for clinicians [1, 2]. We have, herein, presented the case of a 41-year-old female patient with an unremarkable clinical history except for chronic autoimmune thyroiditis in euthyroidism. At the 21st week of gestation, she experienced a spontaneous abortion. The patient underwent an assessment of the uterine cavity, which was complicated by bleeding and hypotensive shock. In the postoperative course, the patient presented worsening headache, and after an MRI, the diagnosis of pituitary apoplexy due to an ischemic-hemorrhagic base was made. Laboratory tests showed anterior panhypopituitarism. Multiaxial replacement therapy was initiated with hydrocortisone, levothyroxine (LT4), and subsequently estrogen-progestin and GH. After two years of good recovery with stable LT4 dosage, the patient experienced palpitations and fine tremors; blood tests showed hyperthyroidism with suppressed Thyroid-stimulating Hormone (TSH) levels and elevated free thyroid fractions and anti-TSH receptor antibodies. Diagnosis of Graves' disease was made, and therapy with methimazole was initiated. During antithyroid therapy, TSH remained persistently suppressed, consistent with the underlying central hypothyroidism. This condition required close follow-up, with monitoring based solely on free thyroid hormone levels. After six months of antithyroid therapy, disease remission was achieved, with negative antibodies and mild hypothyroxinemia. Therefore, methimazole was discontinued and replacement therapy gradually resumed until optimal hormone levels were reached. This case is unique demonstrating autoimmune hyperthyroidism to coexist with central hypothyroidism, rendering TSH a misleading disease progression indicator. Consequently, managing Graves' disease has become more complex and challenging.

Experimental investigation of a transcritical CO2 refrigeration system incorporating rotary gas pressure exchanger and low lift ejectors

Natural refrigerants like CO2 are playing a significant role in making refrigeration and heat pump systems climate-friendly by slowly phasing out the high global warming refrigerants like hydrofluorocarbons (HFCs). However, the efficiency of a transcritical CO2 refrigeration system declines significantly when the ambient temperature increases, primarily attributed to the high-pressure lift and the losses incurred during expansion. To remedy this issue, this paper presents a novel rotary gas pressure exchanger (PXG) device, which simultaneously achieves high differential pressure expansion work recovery and the “free compression” of the portion of the flash gas in a compact, rotary machine. For this, a PXG device is designed, fabricated, and tested to achieve free compression of CO2 over the entire differential pressure of approximately 70 bar between a receiver and a gas cooler. This is one of the highest free-pressure lift provided by any device to date in CO2 refrigeration. However, there is a small pressure loss of approximately 1–2 bar in the system due to viscous and inertia losses in the piping and in the PXG itself, which needs to be overcome by an external booster device. Results on a baseline PXG integrated system with two low lift booster compressors are presented, which show up to 60 bar free pressure lift and up to 18.2 % COP improvement provided by PXG. Additionally, key performance characteristics of the PXG, like the expansion work recovery, the mass boost ratio, direct fluid-to-fluid contact, and no pass-through operation are experimentally quantified. This work also presents a novel method to integrate two low lift ejectors with PXG to eliminate the need for separate low lift compressors. The low lift ejectors are designed, fabricated, and tested in-house, followed by their integration with the PXG device. A new type of transcritical CO2 refrigeration system is designed to integrate these low lift ejectors with PXG, and experiments are conducted at various evaporator thermal duties and gas cooler exit temperatures, simulating varying ambient temperature conditions. A novel control system to control the gas cooler pressure to optimal thermodynamic levels using PXG rotational speed is demonstrated experimentally. Further, automated control of high-pressure low lift ejector mass flow using an in-built needle design has been successfully demonstrated to optimise PXG mass boost performance. The LP low lift ejector achieved a successful pressure lift of 3.8 bar, and the HP low lift ejector showed a lift of 5.7 bar on the top of 42 bar free pressure lift provided by PXG for up to 5.8 kg/min mass flow delivered by free PXG compression. The results from this study demonstrate that the PXG device provides a significant energy efficiency improvement to the transcritical CO2 refrigeration system, and the novel low lift ejectors, when integrated with PXG, provide a successful method to maximise PXG’s thermodynamic potential.

Appropriate Mother Wavelet Selection with Optimum Level of Disintegration for Analyzing Various Faults of Induction Motor Under Variation in Motor Loading

Appropriate Mother Wavelet Selection with Optimum Level of Disintegration for Analyzing Various Faults of Induction Motor Under Variation in Motor Loading

Synergistic Dynamical Decoupling and Circuit Design for Enhanced Algorithm Performance on Near-Term Quantum Devices

Dynamical decoupling (DD) is a promising technique for mitigating errors in near-term quantum devices. However, its effectiveness depends on both hardware characteristics and algorithm implementation details. This paper explores the synergistic effects of dynamical decoupling and optimized circuit design in maximizing the performance and robustness of algorithms on near-term quantum devices. By utilizing eight IBM quantum devices, we analyze how hardware features and algorithm design impact the effectiveness of DD for error mitigation. Our analysis takes into account factors such as circuit fidelity, scheduling duration, and hardware-native gate set. We also examine the influence of algorithmic implementation details, including specific gate decompositions, DD sequences, and optimization levels. The results reveal an inverse relationship between the effectiveness of DD and the inherent performance of the algorithm. Furthermore, we emphasize the importance of gate directionality and circuit symmetry in improving performance. This study offers valuable insights for optimizing DD protocols and circuit designs, highlighting the significance of a holistic approach that leverages both hardware features and algorithm design for the high-quality and reliable execution of near-term quantum algorithms.