Suboptimal Processing Research Articles

Influence of ground control point reliability and distribution on UAV photogrammetric 3D mapping accuracy

ABSTRACT This study investigates the impacts of Ground Control Points (GCP) reliability, quantity, and distribution on the accuracy of Unmanned Aerial Vehicle (UAV) photogrammetric 3D mapping. Previous studies have predominantly focused on overall accuracy metrics, often overlooking local variations in error distribution and failing to conduct thorough analysis of error sources, potentially resulting in inefficiencies and reduced photogrammetric 3D mapping accuracy due to sub-optimal GCP deployment and processing strategies. By employing a structured framework, this research analyzes GCP roles based on variations in their reliability. Aerial images of undulated terrains were analyzed and fifteen distinct GCP configurations were designed which incorporated various distributions and quantities. Random noise was added to simulate GCP reliability unpredictability, subsequently analyzed through a multifaceted approach. The results indicate that GCP reliability influences the upper limit of photogrammetric 3D mapping accuracy and significantly affects the influence of GCP quantity and distribution. It was also found that: when GCP reliability is high (RMSE within 0.1 m; density more than 10 GCP/ k m 2 ), GCP number should prioritize over distribution, ie. accuracy improving as the number of GCPs increases; when GCP reliability is moderated (RMSE within 5 m; density around 5–10 GCP/ k m 2 ), each of the uniform and distributed-edge distributions leads in different conditions; when GCP reliability is low (RMSE beyond 5 m; density less than 5 GCP/ k m 2 ) it is favorable to use a distributed-edge and center distribution. Errors typically originate in edge areas and regions with significant topographic variations and propagating as GCP reliability decreases. Therefore, additional GCPs should be placed in error-prone regions to improve the accuracy.

Are conspiracy beliefs a sign of flawed cognition? Reexamining the association of cognitive style and skills with conspiracy beliefs

Throughout human history, political leaders, oppositional forces, and businesspeople have frequently coordinated in secret for their own benefit and the public’s disadvantage. In these cases, conspiracy theories are capable of accurately describing our environment. However, the vast majority of research today operationalizes conspiracy theories as irrational beliefs that contradict our everyday knowledge. It is not surprising, then, that belief in implausible conspiracy theories has been associated with suboptimal information processing. To get a richer understanding of the phenomenon, we argue that researchers should have this limitation in mind when designing future studies.

Experimental Study of the Effect of Plastic Pyrolysis Oil on the Physical-Chemical Properties of Rubber Seed Biodiesel and Diesel Engine Performance using a Mixture of Plastic Pyrolysis Oil-Rubber Seed Biodiesel-Diesel Fuel

Rubber seed oil is a potential source of biodiesel, but its utilization is still limited. From previous research studies, the addition of Rubber Seed Biodiesel (RSB) to diesel will reduce engine performance and increase BSFC but can increase emissions. At the same time, plastic waste has posed a very serious environmental challenge due to its large quantity and suboptimal processing problems. Pyrolysis is considered to be an efficient solution for handling plastic waste, because it operates at low pressure and produces Plastic Pyrolysis Oil (PPO) as fuel. The success of research on converting plastic waste into fuel can be a solution to limited biodiesel raw materials, as well as a solution for handling plastic waste. From various previous studies it is known that PPO has a higher heating value than biodiesel, but biodiesel has a higher centane number. Based on these various symptoms, it is interesting to research the use of a mixture of RSB and PPO fuels, because they are thought to complement each other. In this research, PPO was added to RSB starting at 10%, 20%, 30% and 40%, then the physicochemical properties of each mixture were examined, it was found that PPO could increase the heating value, reduce viscosity, density and acid number, reduce the cetane number and reduces the oxidation stability of RSB. To test on a diesel engine, PPO was added to the diesel-RSB mixture. The diesel portion is set at 60%, while the RSB and PPO portions are varied starting from 40% RSB 0% PPO, 30% RSB 10% PPO, 20% RSB 20% PPO and 10% RSB 30% PPO. Diesel engine performance and emissions were investigated when PPO was added to the fuel mixture. The search results show that BTE decreased by 4% when using B10P30 compared to using pure diesel, but increased by around 30% compared to using RSB40 at full load. The addition of PPO to the Diesel-RSB mixture increases CO, HC and smoke emissions, but is still lower than pure diesel emissions.

Does aberrant electrocortical dynamics pattern imply atypical balance control in adolescents with idiopathic scoliosis?

Adolescent idiopathic scoliosis (AIS) is a developmental disorder described as a three-dimensional spine deformity with multifactorial etiology. The multifactorial model of AIS suggests that the patient population is diverse. Identifying individuals with sensorimotor control impairments could enable personalized treatments, potentially leading to improved outcomes for those with AIS. We hypothesize that abnormal electrocortical dynamics within the sensorimotor cortex will be related to less efficient balance control in standing during ankle proprioception alteration in the absence or presence of vision. To test this hypothesis, the balance control performance of adolescents with AIS will be assessed on a force platform by computing the root mean square value of the scalar distance between the center of pressure and the center of gravity. Electroencephalography will be recorded while challenging balance control by altering ankle proprioception in the presence and absence of visual cues. Time-frequency analyses will be calculated to determine alpha, and beta band power in both conditions. Pilot data from 13 participants with AIS were analyzed to support our hypothesis. Three participants with less efficient balance control showed electrocortical dynamics changes, such as an increase in beta band power in the presence and absence of vision and a decrease in alpha band power in the presence of vision compared to the AIS group with efficient balance control. These findings support our hypothesis of suboptimal sensorimotor information processing in the subgroup of adolescents with AIS with less efficient balance control, which could have significant implications for developing personalized treatments for AIS.

Nano Plasma Membrane Vesicle-Lipid Nanoparticle Hybrids for Enhanced Gene Delivery and Expression.

Lipid nanoparticles (LNPs) have emerged as the leading nonviral nucleic acid (NA) delivery system, gaining widespread attention for their use in COVID-19 vaccines. They are recognized for their efficient NA encapsulation, modifiability, and scalable production. However, LNPs face efficacy and potency limitations due to suboptimal intracellular processing, with endosomal escape efficiencies (ESE)below 2.5%. Additionally, up to 70% of NPs undergo recycling and exocytosis after cellular uptake. In contrast, cell-derived vesicles offer biocompatibility and high-delivery efficacy but are challenging to load with exogenous NAs and to manufacture at large-scale. To leverage the strengths of both systems, a hybrid system is designed by combining cell-derived vesicles, such as nano plasma membrane vesicles (nPMVs), with LNPs through microfluidic mixing and subsequent dialysis. These hybrids demonstrate up to tenfold increase in ESE and an 18-fold rise in reporter gene expression in vitro and in vivo in zebrafish larvae (ZFL)and mice, compared to traditional LNPs. These improvements are linked to their unique physico-chemical properties, composition, and morphology. By incorporating cell-derived vesicles, this strategy streamlines the development process, significantly enhancing the efficacy and potency of gene delivery systems without the need for extensive screening.

Parameter Optimization and Flaw Type Dependent Tensile Properties of 15‐5PH Stainless Steel Manufactured by Laser Powder Bed Fusion

Careful consideration of processing parameters to mitigate defect formation is crucial to ensure reliability of components produced by laser powder bed fusion (LPBF). In this work, additive manufacturing parameter optimization and flaw type dependent tensile properties of 15‐5 precipitation hardened (PH) stainless steel produced by LPBF is examined to understand the correlation among processing parameters, flaw types, and corresponding tensile mechanical behavior. It is reported that LPBF samples with keyhole (KH) pores exhibit superior mechanical properties compared to those induced with lack‐of‐fusion (LOF) flaws, for a given relative density level. Diminished mechanical properties in samples with LOF flaws is correlated to the flaws’ irregular shapes and sharp corners, causing stress concentration. Minimal improvements to the mechanical properties of LOF‐induced samples, despite increasing relative density, indicate even small amounts of LOF flaws cause stress concentration and crack initiation. Conversely, KH pores exhibit high circularity and lower curvature values, and samples with KH pores demonstrate a strong correlation between tensile properties and relative density. Findings from this study suggest that when suboptimal LPBF processing is inevitable, favoring excessive energy input to induce KH pores over LOF flaws can lead to more predictable mechanical behavior of LPBF components.

CNN-LSTM for MFCC-based Speech Recognition on Smart Mirrors for Edge Computing Command

Smart mirrors are conventional mirrors that are augmented with embedded system capabilities to provide comfort and sophistication for users, including introducing the speech command function. However, existing research still applies the Google Speech API, which utilizes the cloud and provides sub-optimal processing time. Our research aim is to design speech recognition using Mel-frequency cepstral coefficients (MFCC) and convolutional neural network–long short-term memory (CNN-LSTM) to be applied to smart mirror edge devices for optimum processing time. Our first step was to download a synthetic speech recognition dataset consisting of waveform audio files (WAVs) from Kaggle, which included the utterances “left,” “right,” “yes,” “no,” “on,” and “off. ” We then designed speech recognition by involving Fourier transformation and low-pass filtering. We benchmark MFCC with linear predictive coding (LPC) because both are feature extraction methods on speech datasets. Then, we benchmarked CNN-LSTM with LSTM, simple recurrent neural network (RNN), and gated recurrent unit (GRU). Finally, we designed a smart mirror system complete with GUI and functions. The test results show that CNN-LSTM performs better than the three other methods with accuracy, precision, recall, and an f1-score of 0.92. The speech command with the best precision is "no," with a value of 0.940. Meanwhile, the command with the best recall is "off," with a value of 0.963. On the other hand, the speech command with the worst precision and recall is "other," with a value of 0.839. The contribution of this research is a smart mirror whose speech commands are carried out on the edge device with CNN-LSTM.

Principles of miRNA/miRNA* function in plant MIRNA processing.

MicroRNAs (miRNAs) are essential regulators of gene expression, defined by their unique biogenesis, which requires the precise excision of the small RNA from an imperfect fold-back precursor. Unlike their animal counterparts, plant miRNA precursors exhibit variations in sizes and shapes. Plant MIRNAs can undergo processing in a base-to-loop or loop-to-base direction, with DICER-LIKE1 (DCL1) releasing the miRNA after two cuts (two-step MIRNAs) or more (sequential MIRNAs). In this study, we demonstrate the critical role of the miRNA/miRNA* duplex region in the processing of miRNA precursors. We observed that endogenous MIRNAs frequently experience suboptimal processing in vivo due to mismatches in the miRNA/miRNA* duplex, a key region that fine-tunes miRNA levels. Enhancing the interaction energy of the miRNA/miRNA* duplex in two-step MIRNAs results in a substantial increase in miRNA levels. Conversely, sequential MIRNAs display distinct and specific requirements for the miRNA/miRNA* duplexes along their foldback structure. Our work establishes a connection between the miRNA/miRNA* structure and precursor processing mechanisms. Furthermore, we reveal a link between the biological function of miRNAs and the processing mechanism of their precursors with the evolution of plant miRNA/miRNA* duplex structures.

Eco-enzyme Making Assistance for Family Welfare Guidance Women

The lack of understanding and skills of Family Welfare Guidance women in Bawen Village in making eco-enzymes in processing organic waste impacts the suboptimal processing of household-based waste. This community service program aims to provide counseling, education, and practice in making eco-enzymes. The methods used in this activity are socialization and counseling, practical training in making, harvesting, and application. The activity lasted for five months, starting from January to May 2023. The results of this activity are twofold: participants' increased understanding of eco-enzymes, participants' ability to make eco-enzymes, participants' ability to harvest eco-enzymes, and participants' ability to use eco-enzymes for various kitchen and plant fertilization purposes. All participants were able to make, harvest, and use eco-enzymes. The increase in participants' understanding is evident.

Implementasi Kebijakan Simantri dan Sipadu dalam Kajian Green Politics

The empowerment of the agricultural sector in accelerating technology transfer through the SIMANTRI program in Bali has been ongoing for over ten years and has been further developed into the SIPADU program, which has been fully handed over to the Gapoktan as the implementer. Field facts indicate that there are still challenges in optimizing the program, such as undernourished breeding cows, suboptimal processing of cattle waste into solid and liquid organic fertilizers, conflicts of interest among other agricultural institutions, and the failure of Gapoktan to maintain its existence. The research objective is to analyze the extent to which the implementation of Gapoktan 356 Sari Buana aligns with the SIMANTRI and SIPADU policies in the context of green politics, as well as the impact on the traditional subak organization. This research adopts a descriptive qualitative approach. The analysis process utilizes John Barry's Green Politics theory, focusing on aspects of distributive justice, democratization process, and ecological sustainability. The study is conducted in Antapan Village, Baturiti District, Bali. The research participants consist of 9 individuals selected through purposive sampling. Data collection techniques include interviews, observations, and documentation. The research analysis reveals that, theoretically, the SIMANTRI and SIPADU program policies have implemented the concepts of Green Politics, aiming towards sustainable agricultural development in Bali. However, in the program implementation phase, the analysis of Gapoktan 356 Sari Buana, in terms of distributive justice, democratization process, and ecological sustainability, cannot be deemed successful as it has not fulfilled the elements of sustainable societies and a symbiotic moral relationship with the environment. The presence of Gapoktan 356 Sari Buana has demonstrated a positive impact on the subak institution.

Augmented Machine Intelligence with Human Intelligence for Cryptocurrency Price Prediction

This research explores the integration of human and machine intelligence in the financial industry. While AI systems excel at analyzing data, humans possess unique traits that contribute to accurate predictions. Inspired by the concept of Augmented Financial Intelligence, the study aims to integrate human intelligence with existing models, considering the limitations of current human input methods. Natural Language Processing and Sentiment Analysis are used to enhance prediction tasks, but limited training data and suboptimal processing can introduce noise. The study introduces a framework that integrates human and machine intelligence to enhance cryptocurrency market forecasts, analyzing six cryptocurrencies and utilizing an Elo-based rating system to identify exceptional predictors. The framework aims to minimize noise and optimize human input in financial forecasting.

Implementation of Inpatient Medical Records at dr. Reksodiwiryo Hospital: A Qualitative Study

The objective of this study was to examine the implementation of medical records for hospitalized patients at dr. Reksodiwiryo Padang Hospital. The study employed a qualitative research methodology. Seven research informants were selected using the purposive sampling technique. Data were gathered through the utilization of in-depth interviews, on-site observations, and scrutiny of documents. The Miles and Huberman model was employed for data analysis, encompassing reduction, display, and conclusion operations. The data validity test employed both source triangulation and method triangulation. According to the research findings on the input element, the quantity of medical record personnel was adequate and there was an existing standard operating procedure (SOP). However, there was a lack of dedicated budget and insufficient facilities and infrastructure. The procedural element of patient documentation, particularly in regard to the recording of admission and discharge sheets, was not fully completed. There was a delay in the submission of medical records, resulting in suboptimal medical record processing. An error occurred during the medical record storage process, resulting in a misfile. The implementation of Medical Records for Inpatients at dr. Reksodiwiryo Padang Hospital was suboptimal and required enhancement. It is advisable for the hospital to provide training sessions to enhance the proficiency of medical record officers and familiarize them with the existing Standard Operating Procedures (SOPs).

Experimental study on various factors contributing to poor microtremor data processing results using ESAC method

In practical applications, microtremor data often encounter contamination from various sources of noise, leading to suboptimal processing outcomes. This study aimed to provide a comprehensive analysis of the impact of noise on the microtremor method. The focus was on presenting explicit and quantitative evidence to demonstrate the extent to which these noises influence the accuracy and reliability of the method. Initially, a ten-station triangular array was employed to gather a sequence of high-precision microtremor data. Next, various influencing factors, such as high-amplitude random noise, were generated and incorporated into the meticulously collected real data in order to simulate the actual perturbation process. Based on both the raw and the artificially contaminated data, a series of dispersion curves of the fundamental-mode Rayleigh waves were obtained by applying the extended spatial autocorrelation method (ESAC). Based on the obtained results, several valuable conclusions have been derived that can effectively inform the procedures for acquiring and processing microtremor data. If the amplitude and duration of sudden onset vibrations are considerable, more data should be collected to reduce the Negative impact. If non-random noise exhibits similar frequency characteristics as microtremor data, and the duration and energy of the noise are relatively small, it can be ignored; otherwise, more data should be collected. The dispersion curve, determined by the principle of the ESAC method, remains unaffected by any changes in the amplitude of one or more traces. The likelihood of frequency dispersion curve distortion increases as the impact of entire data anomalies on the coherence curve becomes greater. In general, when the collection positions of abnormal data are more widely spaced, the impact on the frequency dispersion curve tends to decrease. It is important to note that, in a nested-triangular array configuration, the central geophone plays a crucial role in ensuring accurate and dependable outcomes.

Suboptimal multisensory processing in pediatric migraine without aura: a comparative, cross-sectional study

Alterations of sensory processing in migraine are well known. There is some evidence to suggest that multisensory processing is altered in migraine as well, but the area is underexplored, especially regarding pediatric migraine. A visual and an audiovisual version of the Rutgers Acquired Equivalence Test paradigm was administered to pediatric patients with migraine without aura (aged 7–17.5 years) and to age- and sex-matched controls. The application of audiovisual stimuli significantly facilitated associative pair learning in migraine-free children and adolescents, but not in pediatric migraine patients. The results of this study corroborate the hypothesis that multisensory processing is altered in pediatric migraine without aura.

Parameters of clustered suboptimal miRNA biogenesis

The nuclear cleavage of a suboptimal primary miRNA hairpin by the Drosha/DGCR8 complex ("Microprocessor") can be enhanced by an optimal miRNA neighbor, a phenomenon termed cluster assistance. Several features and biological impacts of this new layer of miRNA regulation are not fully known. Here, we elucidate the parameters of cluster assistance of a suboptimal miRNA and also reveal competitive interactions amongst optimal miRNAs within a cluster. We exploit cluster assistance as a functional assay for suboptimal processing and use this to invalidate putative suboptimal substrates, as well as identify a "solo" suboptimal miRNA. Finally, we report complexity in how specific mutations might affect the biogenesis of clustered miRNAs in disease contexts. This includes how an operon context can buffer the effect of a deleterious processing variant, but reciprocally how a point mutation can have a nonautonomous effect to impair the biogenesis of a clustered, suboptimal, neighbor. These data expand our knowledge regarding regulated miRNA biogenesis in humans and represent a functional assay for empirical definition of suboptimal Microprocessor substrates.

The Influence of the A Priori Uncertainty of the Shallow Sea Sound Channel Model on the Vertical Array Gain

The purpose of this paper is to numerically demonstrate and comparatively analyze the critically strong and ambiguous impact of the a priori uncertainty of a shallow sea waveguide model in its main physical parameters on the output performance of model-based methods for spatial processing of multimode signals received by a vertical antenna array. The scenario is specified when a relatively weak signal of a remote underwater source is received against the background of intensive interference excited by a subsurface source (like a ship, for example) and ambient sea noise excited by wind waves. The considered array processing methods include matched-signal processing, optimal processing of the signal on the background of interference and noise, and suboptimal processing based on matched-mode array filtering of one of the signal-carrying modes with adaptive selection of its number. Quantitative estimates are obtained from above for the environment uncertainties, or model errors, with respect to the sound velocity in the water column and geoacoustic parameters of the underlying bottom, at which the array gain loss does not exceed a given level. It is shown that such estimates are very different both for different environmental parameters and for processing methods, with the determining role played by the conditions of useful signal reception, namely, the modal composition and intensity levels of the interference and sea noise at the array input. The problem statement and results are considered to be useful to detail the requirements for operational oceanography tools designed to support the effective operation of sonar antenna systems in real sea environments.

The Influence of the A Priori Uncertainty of the Shallow Sea Sound Channel Model on the Vertical Array Gain

The purpose of this paper is to numerically demonstrate and comparatively analyze the critically strong and ambiguous impact of the a priori uncertainty of a shallow sea waveguide model in its main physical parameters on the output performance of model-based methods for spatial processing of multimode signals received by a vertical antenna array. The scenario is specified when a relatively weak signal of a remote underwater source is received against the background of intensive interference excited by a subsurface source (like a ship, for example) and ambient sea noise excited by wind waves. The considered array processing methods include matched-signal processing, optimal processing of the signal on the background of interference and noise, and suboptimal processing based on matched-mode array filtering of one of the signal-carrying modes with adaptive selection of its number. Quantitative estimates are obtained from above for the environment uncertainties, or model errors, with respect to the sound velocity in the water column and geoacoustic parameters of the underlying bottom, at which the array gain loss does not exceed a given level. It is shown that such estimates are very different both for different environmental parameters and for processing methods, with the determining role played by the conditions of useful signal reception, namely, the modal composition and intensity levels of the interference and sea noise at the array input. The problem statement and results are considered to be useful to detail the requirements for operational oceanography tools designed to support the effective operation of sonar antenna systems in real sea environments.

Strain and serovar variants of Salmonella enterica exhibit diverse tolerance to food chain-related stress.

Non-Typhoidal Salmonella (NTS) continues to be a leading cause of foodborne illness worldwide. Food manufacturers implement hurdle technology by combining more than one approach to control food safety and quality, including preservatives such as organic acids, refrigeration, and heating. We assessed the variation in survival in stresses of genotypically diverse isolates of Salmonella enterica to identify genotypes with potential elevated risk to sub-optimal processing or cooking. Sub-lethal heat treatment, survival in desiccated conditions and growth in the presence of NaCl or organic acids were investigated. S. Gallinarum strain 287/91 was most sensitive to all stress conditions. While none of the strains replicated in a food matrix at 4°C, S. Infantis strain S1326/28 retained the greatest viability, and six strains exhibited a significantly reduced viability. A S. Kedougou strain exhibited the greatest resistance to incubation at 60°C in a food matrix that was significantly greater than S. Typhimurium U288, S Heidelberg, S. Kentucky, S. Schwarzengrund and S. Gallinarum strains. Two isolates of monophasic S. Typhimurium, S04698-09 and B54Col9 exhibited the greatest tolerance to desiccation that was significantly more than for the S. Kentucky and S. Typhimurium U288 strains. In general, the presence of 12mM acetic acid or 14mM citric acid resulted in a similar pattern of decreased growth in broth, but this was not observed for S. Enteritidis, and S. Typhimurium strains ST4/74 and U288 S01960-05. Acetic acid had a moderately greater effect on growth despite the lower concentration tested. A similar pattern of decreased growth was observed in the presence of 6% NaCl, with the notable exception that S. Typhimurium strain U288 S01960-05 exhibited enhanced growth in elevated NaCl concentrations.

Dynamic Load Balancing in Stream Processing Pipelines Containing Stream-Static Joins

Data stream processing systems are used to continuously run mission-critical applications for real-time monitoring and alerting. These systems require high throughput and low latency to process incoming data streams in real time. However, changes in the distribution of incoming data streams over time can cause partition skew, which is defined as an unequal distribution of data partitions among workers, resulting in sub-optimal processing due to an unbalanced load. This paper presents the first solution designed specifically to address partition skew in the context of joining streaming and static data. Our solution uses state-of-the-art principles to monitor processing load, detect load imbalance, and dynamically redistribute partitions, to achieve optimal load balance. To accomplish this, our solution leverages the collocation of streaming and static data, while considering the processing load of the join and the subsequent stream processing operations. Finally, we present the results of an experimental evaluation, in which we compared the throughput and latency of four stream processing pipelines containing such a join. The results show that our solution achieved significantly higher throughput and lower latency than the competing approaches.

The impact of hearing aid user's own voice on device signal processing

Hearing aid processing is designed to improve audibility for sounds of interest, often by targeting external speech signals. During natural conversation, however, the hearing aid user is also the source of speech, potentially interacting with hearing aid function and leading to suboptimal processing. In this study, we investigated how the presence of own voice impacts the deployment of specific features designed to enhance audibility and intelligibility of conversational partners. We recorded real-time hearing aid feature engagement (directional microphones, noise reduction, and speech enhancement) during simulated conversations in quiet and in background noise. Simulations were performed using an acoustic manakin (GRAS 45BC-12) capable of producing speech via internal speaker, which was situated in the center of a 24-speaker spatial array. The results demonstrate how the presence of a hearing aid user’s own voice disrupts the intended use of these adaptive features, and that there is a need for optimizing hearing enhancement devices to account for own voice in dynamic scenarios. Future studies will determine the impact of a hearing aid user’s own voice on the device’s ability to improve speech intelligibility and user satisfaction.