Search Results (136)

The intensive use of various sensors in industrial machines has the potential to indicate the real-time health status of critical equipment. This is achieved through the connectivity of their automation systems (PIMS and MES), enabling the optimization of the preventive maintenance interval, a reduction in corrective maintenance and safety-related failures, an increase in productivity and reliability and a reduction in maintenance costs. Through the use of the CRISP-DM data analysis methodology, the fault logs of ceramic plates applied in an iron ore filtration process are coupled with sensor readings of the process variables over the time of operation to create exponential survival models via two techniques: a multiple linear regression model with averaged data and a random forest regression machine learning model with individual instant data. The instantaneous reliability of ceramic plates is then used in the online prediction of the remaining useful life of the components. The model obtained from the instantaneous reading of 12 sensors led to the estimation of the remaining useful life for ceramic plates with up to 5600 h of use, allowing the adoption of a strategy of replacing these components by condition instead of replacing them by a fixed time, leading to increased process reliability and improved stock planning. The linear regression model for reliability prediction had an R² of 78.32%, whereas the random forest regression model had an R² of 63.7%. The final model for predicting the remaining useful life had an R² of 99.6%. Full article

Neuro-oncology focuses on the diagnosis and treatment of brain tumors, which, despite their rarity, are associated with high mortality due to their invasiveness and limited treatment options. Emerging evidence suggests that dopamine (DA), a neurotransmitter crucial for cognitive and emotional processes, and its receptors may influence tumor growth and the tumor microenvironment. This study aimed to evaluate the potential anticancer effects of repurposed antipsychotic dopamine-targeting drugs (Clozapine, CLZ; Pimozide, PIM; Olanzapine, OLZ; and Risperidone, RIS) and antiemetic drugs (Domperidone, DOM; Droperidol, DRO) on neuroblastoma (SH-SY5Y) and glioblastoma (A172) cell lines, and to assess whether their efficacy is modulated by oxidative stress and DA synthesis. The drugs were first tested individually, followed by co-treatment with tyrosine (Tyr), a dopamine precursor, and hydrogen peroxide (H₂O₂), an inducer of oxidative stress. Additionally, drug activity was evaluated in the simultaneous presence of H₂O₂ and Tyr. CLZ exhibited the highest cytotoxicity in both cell lines, suggesting strong anticancer potential and also synergism among the different combinations, particularly in SH-SY5Y. Liquid chromatography of the extracellular medium showed greater Tyr consumption in SH-SY5Y compared to A172 cells, indicating a higher dependence on extracellular Tyr to mitigate drug- and/or stress-induced cytotoxicity. In summary, several of the repurposed antipsychotics demonstrated cytotoxic effects on central nervous system tumor cells, with CLZ showing the most promising activity, even under oxidative stress conditions. These findings support further investigation into dopamine-targeting drugs as potential therapeutic agents in neuro-oncology. Full article

CoCrMo alloys are interesting materials for implantable devices due to their favorable mechanical properties, high wear resistance, and good biocompatibility with the human body. Recent studies have demonstrated the possibility to further increase their wear resistance with an innovative approach consisting of nitriding treatments by the High-Power Impulse Magnetron Sputtering (HiPIMS) technique. Given the novelty of this treatment, it is relevant to develop a preliminary sustainability analysis of the processes to highlight the total environmental impact and to evaluate possible strategies to decrease it. Here, a Life Cycle Assessment (LCA) of HiPIMS nitriding treatments of CoCrMo alloys using a tantalum or molybdenum target is presented. The main impact driver in all impact categories was the electrical consumption of the vacuum apparatus and cooling system of HiPIMS instrumentation with a 45–47% and 37–39% contribution for Ta-based, and 39–40% and 41–42% for Mo-based treatments, respectively. Climate Change was found to be the most impacted category, followed by Resource Use both for Mo and Ta nitriding targets. Therefore, some perspectives to enhance the environmental sustainability of the synthesis process have been considered by means of a sensitivity analysis. Moreover, a Critical Raw Material (CRM) assessment is discussed, providing a complete sustainability evaluation of the proposed HiPIMS treatments. Full article

Background: Older adults are vulnerable to adverse drug reactions (ADRs), particularly in low-income settings, yet data on ADR prevalence in Africa, including Ethiopia, remain limited. Objective: This study aimed to evaluate the incidence, severity, and preventability of ADRs among hospitalized older adults, as well as all-cause inpatient mortality. Methods: A cross-sectional observational study was conducted at Jimma Medical Center, located in Jimma town, Ethiopia, from 6 September 2021 to 26 December 2022. The study participants were older adults (n = 162) admitted to the medical wards. ADRs were assessed using the Naranjo ADR probability scale, severity was classified according to the modified Hartwig and Siegel criteria, and preventability was determined using the Schumock and Thornton criteria. Results: The median age of participants was 65 years (interquartile range: 60–70). During their hospital stay, 84 patients (51.9%) experienced at least one ADR. A total of 123 ADRs (76 ADRs per 100 admissions) were captured. Most ADRs (93.5%) were classified as mild to moderate in severity, and 84.5% (n = 105) were considered preventable. Endocrine and metabolic systems (48.8%) and diuretics (43.9%) were the most frequently affected organ systems and drug class linked to ADRs, respectively. Furosemide (41.5%) and aspirin (10.6%) were the most frequently implicated medications, commonly causing hypokalemia (35.3%) and dyspepsia (53.8%), respectively. The observed all-cause in-patient mortality rate was 6.8% (5 deaths per 1000 patient-days). The use of potentially inappropriate medications (PIMs) (aOR: 4.747, p = 0.003) and presence of digestive system disorders (aOR: 8.784, p = 0.038) were associated with an increased risk of ADRs, while a history of recent traditional medicine use (aOR: 0.285, p = 0.042) was linked to a lower risk. Conclusions: More than half of the hospitalized older adults experienced ADRs, most of which were mild to moderate in severity and considered preventable. Regular medication review for screening and minimizing PIM use in older adults may play a crucial role in lowering ADR occurrence. The borderline but statistically significant association between a history of traditional medicine use and lower occurrence of ADRs requires cautious interpretation and further investigation to explore possible explanations. Nearly seven deaths per hundred hospitalized patients were recorded. Full article

High production costs and extended development timelines pose significant challenges to the manufacturing of bearingless permanent magnet synchronous motors (BPMSMs). Moreover, uncertainties regarding the motor’s ability to generate suspension and torque often persist even after prototyping, primarily due to the limitations of lumped parameter models in capturing the system’s complex dynamics. Since this technology is not yet fully consolidated, there is a clear need for a solution that enables the effective evaluation of BPMSMs prior to physical production. To address this, a reduced-order model (ROM) was developed for BPMSMs with combined windings, capturing the cross-coupling effects associated with rotor eccentricity, magnetic saturation, and topological complexity. The model was constructed using the parametric interpolation method (PIM), enabling efficient and accurate representations of nonlinear electromechanical behavior as ferromagnetic materials and spatial harmonics are addressed through finite element modeling. Additionally, hardware-in-the-loop (HIL) techniques were used for gain tuning, and active disturbance rejection control (ADRC) was applied to enhance performance. This combined approach offers a comprehensive solution for the design and control of BPMSMs. Full article

In this paper, polymer inclusion membranes (PIMs) were created using poly(vinyl chloride)-based alkyl sulfonic acid derivatives as ion carriers and dioctyl terephthalate as a plasticizer for the selective separation of Pb(II), Cu(II), and Cd(II) ions from aqueous nitrate solutions. The ion carriers were dinonylnaphthalenesulfonic acid (DNNSA) and nonylbenzenesulfonic acid (NBSA). The influence of the carrier and the plasticizer concentration in the membrane on the transport efficiency was investigated. For the PIM system, 15% wt. of carrier (DNNSA, NBSA), 20% wt. of plasticizer, and 65% wt. of polymer poly(vinyl chloride) PVC were the optimal proportions, with which the process was the most effective. Research on the transport kinetics has shown that the transport of Pb(II) ions through PIMs containing acidic carriers adheres to a first-order kinetics equation, which is characteristic of a facilitated transport mechanism. The activation parameter for these processes suggests that the high performance of these ion carriers is associated with the immobilization of the carrier within the membrane. It was found that PIMs based on DNNSA facilitate the selective separation of Pb(II)/Cu(II) and Pb(II)/Cd(II) mixtures, achieving high separation factors. Full article

Polymer inclusion membranes (PIMs) have been reported to be useful for the selective separation of numerous metal ions, with multiple applications in areas such as analytical chemistry, water quality monitoring, water treatment, and metal recovery. This review aims to update the recent advancements related to PIM technology in metal ion separation, with a particular emphasis on environmentally friendly production and applications. PIMs have many advantages over classical liquid–liquid extraction, such as excellent selectivity, ease of use with simultaneous extraction and back-extraction, stability, and reusability. PIMs typically consist of a base polymer, a carrier, and, if necessary, a plasticizer, and can therefore be tailored to specific analytes and specific matrices. Consequently, numerous studies have been carried out to develop PIMs for specific applications. In analytical chemistry, PIMs have been used mostly for analyte preconcentration, matrix separation, speciation analysis, and sensing. They can be used as passive sampling tools or integrated into automated water monitoring systems. PIMs are also widely studied for the extraction and purification of valuable metals in the frame of the circular economy, as well as for wastewater treatment. Even if they are a greener alternative to classical metal extraction, their production still requires petroleum-based polymers and toxic and volatile solvents. In recent years, there has been a clear trend to replace classical polymers with biodegradable and bio-sourced polymers and to replace the production of PIMs using toxic solvents with those based on green solvents or without solvents. According to the published literature, environmentally friendly PIM-based techniques are a highly recommended area of future research for metal ion separation directed toward a wide range of applications. Full article

This study presents an innovative Dual-Band Frequency Selective Surface (FSS) designed for LTE applications, offering an effective solution for minimizing Passive Inter-Modulation (PIM) in contemporary wireless communication systems at the base station. The proposed passband FSS filter is designed to deliver optimal dual-band filtering characteristics with consistent stability over incidence angles up to 80°. Corresponding to antenna systems requirements, the proposed method gives resonant frequencies at 1.9 and 2.1 GHz which operate in the LTE band with bandwidths of 40 and 60 MHz, respectively. Moreover, the proposed design is analyzed to establish the optimal range for each resonant frequency by examining the parametric effects. The suggested FSS-based filter consists of a single-layer structure with the dimension of the unit cell of 0.33${\lambda }_1$ × 0.33${\lambda }_1$ where ${\lambda }_1$ is the wavelength of low frequency, which delivers desired reflection and transmission coefficients using RT/Duroid 5880 with a thickness of 0.508 mm. The designed filter is validated through measurements of a fabricated prototype, demonstrating its practicality and performance. Simulations carried out with Equivalent Circuit Modeling (ECM) are demonstrated by measurements from a constructed 4 × 4 array prototype, showing a robust alignment with experimental findings. This work emphasizes an asymmetric FSS design that improves frequency selectivity and angular stability for the desired LTE dual band and also depicts the future possibilities for tuneable models and broader applications to meet the demands of modern wireless communication. Full article

Background/Objectives: Cytokines related to the Th17 response have been associated with peri-implant diseases; however, the effect of peri-implant therapy on their modulation remains underexplored. To evaluate the effect of peri-implant therapy on the expression of cytokines related to the Th17 response in the peri-implant crevicular fluid (PICF) (GM-CSF, IFN-γ, IL-1β, IL-4, IL-6, IL-10, IL-12 (p70), IL-17A, IL-21, IL-23, and TNF-α) of partially edentulous patients with peri-implant disease (PID). Methods: Thirty-seven systemically healthy individuals presenting with peri-implant mucositis (PIM) (n = 20) or peri-implantitis (PI) (n = 17) were treated and evaluated at baseline (T0) and three months after therapy (T1). Clinical parameters (probing depth (PD), clinical attachment level (CAL), plaque index, and bleeding on probing index (BoP), were evaluated. The PIM group underwent non-surgical therapy, while the PI group received a surgical approach. PICF was collected with absorbent paper strips and analyzed with a multiplex assay. Results: Eighty-eight implants were treated in 37 patients (56 in the PIM group and 32 in the PI group). After therapy, significant reductions in PD, CAL, plaque index, and BoP were observed in the PIM group (p < 0.05). In the PI group, significant reductions in PD, CAL, and BoP were noted (p < 0.05). The PIM group showed a significant reduction of IL-17A and TNF-α after therapy, while the PI group showed a significant reduction of IL-1β, IL-6, and TNF-α (p < 0.05). Conclusions: The peri-implant therapy for patients with PID reduced the expression of cytokines related to the Th17 response in PICF. Full article

As the scope of artificial intelligence (AI) expands and the structure becomes more complex, the amount of data for inference and training has increased. In traditional computer architectures, the memory bandwidth limitations have intensified bottlenecks in AI systems, and processing-in-memory (PIM) architectures have been proposed to overcome this issue. PIM is an architecture that performs computations within memory, thereby reducing data movement between the CPU and memory. However, since PIM is difficult to optimize as a general-purpose architecture, it is essential to adopt an architecture suitable for the target application. While various simulators and emulators have been introduced for the design space exploration (DSE) of different PIM architectures, simulators are limited in debugging hardware operations, and emulators face challenges in flexibly modifying the system configuration, as emulators implement the entire architecture in hardware. Therefore, this paper introduces PIMCoSim, a comprehensive hardware–software co-simulator for the DSE of DRAM-PIM systems. This co-simulator partially emulates simplified hardware-implemented processing elements (PEs) and integrates software models for memory operations, facilitating the DSE of PIM systems. To validate PIMCoSim, we analyzed results for different computational workloads by varying PIM structures and operational policies, demonstrating the efficiency of DRAM-PIM systems. The co-simulation approach in PIMCoSim aims to contribute to analyzing DRAM-PIM configurations and adopting optimized structures. Full article

The rapid advancement of artificial intelligence (AI) technology, combined with the widespread proliferation of Internet of Things (IoT) devices, has significantly expanded the scope of AI applications, from data centers to edge devices. Running AI applications on edge devices requires a careful balance between data processing performance and energy efficiency. This challenge becomes even more critical when the computational load of applications dynamically changes over time, making it difficult to maintain optimal performance and energy efficiency simultaneously. To address these challenges, we propose a novel processing-in-memory (PIM) technology that dynamically optimizes performance and power consumption in response to real-time workload variations in AI applications. Our proposed solution consists of a new PIM architecture and an operational algorithm designed to maximize its effectiveness. The PIM architecture follows a well-established structure known for effectively handling data-centric tasks in AI applications. However, unlike conventional designs, it features a heterogeneous configuration of high-performance PIM (HP-PIM) modules and low-power PIM (LP-PIM) modules. This enables the system to dynamically adjust data processing based on varying computational load, optimizing energy efficiency according to the application’s workload demands. In addition, we present a data placement optimization algorithm to fully leverage the potential of the heterogeneous PIM architecture. This algorithm predicts changes in application workloads and optimally allocates data to the HP-PIM and LP-PIM modules, improving energy efficiency. To validate and evaluate the proposed technology, we implemented the PIM architecture and developed an embedded processor that integrates this architecture. We performed FPGA prototyping of the processor, and functional verification was successfully completed. Experimental results from running applications with varying workload demands on the prototype PIM processor demonstrate that the proposed technology achieves up to 29.54% energy savings. Full article

Background: Frailty and polymedication are closely interrelated. Addressing these concurrent conditions in primary care settings relies on the utilization of potentially inappropriate medication (PIM) lists and medication reviews (MRs), particularly in rural areas, where healthcare professionals serve as the sole point of access to the medical system. The aim of this study was to examine the relationship between medication appropriateness and variables related to frailty in a rural municipality in order to propose potential strategies for therapy optimization. Methods: This cross-sectional study included all adult community dwellers aged 50 and above officially registered in the municipality of Tiriez (Albacete, Spain) in 2023 (n = 241). The primary outcome variable was frailty (assessed using the fatigue, resistance, ambulation, illness, and loss of weight (FRAIL) scale). The independent variables were age, gender, medication regimen, history of falls, comorbidities, PIMs (evaluated using the screening tool of older persons’ prescriptions (STOPP) 2023 criteria), fall-risk-increasing drugs (FRID), and anticholinergic burden (ACB). Results: The prevalence of frailty was approximately 20%. FRID and ACB scores were statistically associated (p-value < 0.001) with frailty, 1.1 ± 1.3 vs. 2.5 ± 1.7, and 1.0 ± 1.3 vs. 2.8 ± 2.5, respectively. Regardless of age, frailty was observed to be more prevalent among females (odds ratio (OR) [95% confidence interval (CI)]: 3.5 [1.5, 9.0]). On average, 2.1 ± 1.6 STOPP criteria were fulfilled, with the prolonged use of anxiolytics and anti-peptic-ulcer agents being the most frequent. Priority interventions (PIs) included opioid dose reduction, benzodiazepine withdrawal, and the assessment of antidepressant and antiplatelet treatment plans. Conclusions: The optimization of medication in primary care is of paramount importance for frail patients. Interventional measures should focus on ensuring the correct dosage and combination of drugs for each therapeutic regimen. Full article

Accurately simulating the deployment process of coupled systems in deep-sea environments remains a significant challenge. This study employs the Absolute Nodal Coordinate Formulation (ANCF) to dynamically model and analyze multi-body systems based on the Pendant Installation Method (PIM). Utilizing the principle of energy conversion, this study calculates the stiffness, generalized elastic forces, mass matrices, and Morison equation, formulating a motion equation for the dynamic coupling of nonlinear time-domain forces in cables during pendulum deployment, which is numerically solved using the implicit generalized-α method. By comparing the simulation results of this model with those from the catenary theory model, the advanced modeling capabilities of this model are validated. Lastly, the sensitivity of the multi-body system under various boundary conditions is analyzed. The results indicate that deployment operations are more effective in environments with strong ocean currents. Furthermore, upon comparing the impacts of structural mass and deployment depth on the system, it was found that deployment depth has a more significant effect. Consequently, the findings of this study provide a scientific basis for formulating subsequent optimization strategies. Full article

The clinical context of drug interactions detected by automated analysis systems is particularly important in older patients with multimorbidities. We aimed to provide unique, up-to-date data on the prevalence of potentially inappropriate medications (PIMs) and drug–drug interactions (DDIs) in the Polish geriatric population over 80 years old and determine the frequency and the most common PIMs involved in DDIs. We analyzed all non-prescription and prescription drugs in a representative national group of 178 home-dwelling adults over 80 years old with excessive polypharmacy (≥10 drugs). The FORTA List was used to assess PIMs, and the Lexicomp^® Drug Interactions database was used for DDIs. DDIs were detected in 66.9% of the study group, whereas PIMs were detected in 94.4%. Verification of clinical indications for the use of substances involved in DDIs resulted in a reduction in the total number of DDIs by more than 1.5 times, as well as in a nearly 3-fold decrease in the number of interactions requiring therapy modification and drug combinations that should be strictly avoided. The most common PIMs involved in DDIs were painkillers, and drugs used in psychiatry and neurology. Special attention should be paid to DDIs with PIMs since they could increase their inappropriate character. The use of automated interaction analysis systems, while maintaining appropriate clinical criticism, can increase both chances for a good therapeutic effect and the safety of the elderly during treatment processes. Full article

A lot of research on deep learning and big data has led to efficient methods for processing large volumes of data and research on conserving computing resources. Particularly in domains like the IoT (Internet of Things), where the computing power is constrained, efficiently processing large volumes of data to conserve resources is crucial. The processing-in-memory (PIM) architecture was introduced as a method for efficient large-scale data processing. However, PIM focuses on changes within the memory itself rather than addressing the needs of low-cost solutions such as the IoT. This paper proposes a new approach using the PIM architecture to overcome memory bottlenecks effectively in domains with computing performance constraints. We adopt the RISC-V instruction set architecture for our proposed PIM system’s design, implementation, and comprehensive performance evaluation. Our proposal expects to efficiently utilize low-spec systems like the IoT by minimizing core modifications and introducing PIM instructions at the ISA level to enable solutions that leverage PIM capabilities. We evaluate the performance of our proposed architecture by comparing it with existing structures using convolution operations, the fundamental unit of deep-learning and big data computations. The experimental results show our proposed structure achieves a 34.4% improvement in processing speed and 18% improvement in power consumption compared to conventional von Neumann-based architectures. This substantiates its effectiveness at the application level, extending to fields such as deep learning and big data. Full article