Sequential Model Research Articles

Exploiting explicit item–item correlations from knowledge graphs for enhanced sequential recommendation

In recent years, the research of employing knowledge graphs (KGs) in sequential recommendation (SR) has received a lot of attention, since the side information extracted from KGs, especially the information of the correlations between items, indeed helps the SR models achieve better performance. However, many previous KG-based SR models tend to introduce some noise information when learning item embeddings, or insufficiently fuse item–item correlations into their sequential modeling, thus limiting their performance improvements. In this paper, we propose a Distance-Aware Knowledge-based Sequential Recommendation model (DAKSR), which exploits the explicit item–item correlations from KGs to achieve enhanced SR. Specifically, as one critical component in our DAKSR, the distance score matrix (DSM) is first obtained to indicate the correlations between items, and then leveraged in the following three major modules of DAKSR. First, in the Item-Set Embedding layer (ISE) all item embeddings are learned based on DSM, in which the noise information is eliminated effectively. Meanwhile, the Knowledge-Infused Transformer (KIT) incorporates DSM into its attention mechanism to improve the feature extraction. Furthermore, the Knowledge Contrastive Learning module (KCL) also leverages the item–item correlations presented in DSM to generate two credible sequence views, which are used to refine sample representations through a contrastive learning strategy, and thus improve the model’s robustness. Our extensive experiments on three SR benchmarks obviously demonstrate our DAKSR’s superior performance over the state-of-the-art (SOTA) KG-based recommendation models. The implementation of our DAKSR is available at https://github.com/Easonsi/DAKSR for reproducing our experiment results conveniently.

IPSRM: An intent perceived sequential recommendation model

Objectives:Sequential recommendation aims to recommend items that are relevant to users’ interests based on their existing interaction sequences. Current models lack in capturing users’ latent intentions and do not sufficiently consider sequence information during the modeling of users and items. Additionally, noise in user interaction sequences can affect the model’s optimization process. Methods:This paper introduces an intent perceived sequential recommendation model (IPSRM). IPSRM employs the generalized expectation–maximization (EM) framework, alternating between learning sequence representations and optimizing the model to better capture the underlying intentions of user interactions. Specifically, IPSRM maps unlabeled behavioral sequences into frequency domain filtering and random Gaussian distribution space. These mappings reduce the impact of noise and improve the learning of user behavior representations. Through clustering process, IPSRM captures users’ potential interaction intentions and incorporates them as one of the supervisions into the contrastive self-supervised learning process to guide the optimization process. Results:Experimental results on four standard datasets demonstrate the superiority of IPSRM. Comparative experiments also verify that IPSRM exhibits strong robustness under cold start and noisy interaction conditions. Conclusions:Capturing latent user intentions, integrating intention-based supervision into model optimization, and mitigating noise in sequential modeling significantly enhance the performance of sequential recommendation systems.

The Shield of Self-Esteem: Buffering against the Impact of Traumatic Experiences, Fear, Anxiety, and Depression.

Adverse life occurrences (e.g., severe accidents, violence/abuse, organic disorders such as COVID-19) can elicit traumatic responses that heighten fear, anxiety, and depression. However, scientific research has shown that certain variables, such as self-esteem, based on theories like terror management theory (TMT) and the anxiety-buffering hypothesis (ABH), can mitigate the negative effects of trauma. This study aimed to test the ABH by assessing the buffering role of self-esteem in the relationships among the impact of traumatic experiences, fear, anxiety, and depression. An observational research design was used. This study involved 321 participants who experienced COVID-19 as a traumatic experience. A sequential multiple-mediation model with observed variables (path analysis) was used to test the impact of the traumatic experience on fear, anxiety, and depression, examining the protective role of self-esteem. A path analysis revealed that fear and anxiety mediated the relationship between the impact of the traumatic experience of COVID-19 and depression. Additionally, in line with the ABH, self-esteem was found to mediate the relationship between the predictors and their adverse psychological consequences. This suggests that self-esteem played a buffering role, mitigating the negative impact of traumatic experiences on mental health outcomes. These findings underscore the central mediating role of self-esteem, as well as fear and anxiety, in the pathway from trauma-related factors to depression. These insights advocate for evidence-based interventions aimed at alleviating the psychological suffering associated with traumatic experiences, fostering adaptation, and supporting psychological health.

Assessment of the antinociceptive effect of a single fentanyl bolus dose in children: A pharmacokinetic and pharmacodynamic analysis based on the nociception level index during sevoflurane general anesthesia.

The Nociception Level Index has shown benefits in estimating the nociception/antinociception balance in adults, but there is limited evidence in the pediatric population. Evaluating the index performance in children might provide valuable insights to guide opioid administration. To evaluate the Nociception Level Index ability to identify a standardized nociceptive stimulus and the analgesic effect of a fentanyl bolus. Additionally, to characterize the pharmacokinetic/pharmacodynamic relationship of fentanyl with the Nociception Level Index response during sevoflurane anesthesia. Nineteen children, 5.3 (4.1-6.7) years, scheduled for lower abdominal or urological surgery, were studied. After sevoflurane anesthesia and caudal block, a tetanic stimulus (50 Hz, 60 mA, 5 s) was performed in the forearm. Following the administration of fentanyl 2 μg/kg intravenous bolus, three similar consecutive tetanic stimuli were performed at 5-, 15-, and 30-min post-fentanyl administration. Changes in the Nociception Level Index, heart rate, mean arterial pressure, and bispectral index were compared in response to the tetanic stimuli. Fentanyl plasma concentrations and the Nociception Level Index data were used to elaborate a pharmacokinetic/pharmacodynamic model using a sequential modeling approach in NONMEM®. After the first tetanic stimulus, both the Nociception Level Index and the heart rate increased compared to baseline (8 ± 7 vs. 19 ± 10; mean difference (CI95) -12(-18--6) and 100 ± 10 vs. 102 ± 10; -2(-4--0.1)) and decrease following fentanyl administration (19 ± 10 vs. 8 ± 8; 12 (5-18) and 102 ± 10 vs. 91 ± 11; 11 (7-16)). In subsequent tetanic stimuli, heart rate remained unchanged, while the Nociception Level Index progressively increased within 15 min to values similar to those before fentanyl. An allometric weight-scaled, 3-compartment model best characterized the pharmacokinetic profile of fentanyl. The pharmacokinetic/pharmacodynamic modeling analysis revealed hysteresis between fentanyl plasma concentrations and the Nociception Level Index response, characterized by plasma effect-site equilibration half-time of 1.69 (0.4-2.9) min. The estimated fentanyl C50 was 1.93 (0.73-4.2) ng/mL. The Nociception Level Index showed superior capability compared to traditional hemodynamic variables in discriminating different nociception-antinociception levels during varying fentanyl concentrations in children under sevoflurane anesthesia.

The indirect effect of parents’ work-family conflict on students’ psychosocial well-being

ABSTRACT This study aims to investigate the cascading impact of parents’ work-family conflict (WFC) on students’ psychosocial well-being. This study examines the sequential mediation effect of parents’ well-being and positive discipline using the conservation of resources (COR) theory and emotional contagion theory. Data were collected using an online survey platform where every participant received a concern form and unique code. The analysis used data from 351 parent–child dyads after removing 76 participants who failed the attention check items or did not complete the three phases of data collection. Data were analysed using a sequential mediation regression model six proposed by Hayes. The results confirmed a sequential mediation model where the parent's WFC indirectly impacted the student's psychosocial well-being through the mediating role of the parent's well-being and positive discipline. This study also discovered that WFC directly impacted parents’ well-being, positive discipline, and students’ psychosocial well-being. Organisations must provide parents with psychological services to reduce WFC and maintain their well-being. On the other hand, schools should communicate with parents and encourage positive discipline for students. This current study has advanced the literature by examining the link between parents’ WFC and students’ psychosocial well-being.

Disparities in cancer mortality outcomes among adult immigrants in the United States.

134 Background: The “immigrant health paradox” posits that immigrants are generally healthier than US natives of similar demographic and socioeconomic background. However, immigrant noncitizens with cancer face unique barriers to care, including limited access to routine screening and continuous healthcare coverage, which may portend worse outcomes. Methods: The 2011-2018 National Health Interview Survey Linked Mortality Files were used to identify all adult (age ≥18 years) US natives, foreign-born citizens, and noncitizens with a cancer history. Differences by group were assessed using the chi-squared test with Rao & Scott’s second-order correction. Cox-proportional hazards models tested associations between nativity/citizenship status and cancer-specific (CSM) and all-cause mortality (ACM), adjusted by age, gender, and survey year. Sequential models separately tested the added effects of individual patient-level covariates selected a priori —including race/ethnicity, smoking status, region, educational attainment, employment, insurance status, and having a usual source of care. Results: A total of 23,603,743 US natives, 1,647,188 foreign-born citizens and 477,861 noncitizens met criteria for inclusion. Compared to US natives, immigrants had significantly greater distribution of gastroesophageal (2.3%, noncitizens vs 2.3%, foreign-born citizens vs 1.0%, US natives, p <0.001), head & neck (8.4% vs 6.2% vs 4.4%, p =0.003), and lung cancers (3.1% vs 4.2% vs 2.8%, p =0.03). Of the1,302,0771 US natives, 105,258 foreign-born citizens, and 21,937 noncitizens who died during the study period, cancer was the primary cause of death for 52% of noncitizens and 62% of foreign-born citizens (vs 44%, p <0.001). Lung cancer was the most common diagnosis in this group, accounting for 40% of cancer deaths among non-citizens (vs 19% vs 13%; p =0.03). Relative to US natives, foreign-born citizens had a significantly lower risk of all-cause (adjusted HR [95%CI], 0.67 [0.54-0.83]) but not cancer-specific mortality (0.94 [0.71-1.23]); no differences in mortality risk among noncitizens were observed. On subset analysis limited to adults with lung cancer, noncitizens had over twice the hazard of cancer death (2.72 [1.22-6.09]) as well as death from any cause (2.14 [1.01-4.55]). While the association with ACM was no longer significant in sequential models, the association remained significant for CSM. Importantly, insurance status attenuated but did not eliminate the risk of lung cancer death for noncitizens (2.63 [1.16-5.96]). Conclusions: Although immigrant noncitizens did not have a significantly greater risk of cancer death overall, those with lung cancer were more than twice as likely to die from their disease, even after adjustment for patient-level variables, including insurance status and having a usual source of care. Future research is warranted to identify root causes and potential points for intervention.

Seasonal alternation of soil phosphorus saturation in rice-wheat rotation systems under multiple fertilization strategies

The rice-wheat rotation system has been shown to improve agricultural productivity with associated economic benefits. The degree of phosphorus saturation (DPS) refers to saturation of soil sorption sites with phosphorus (P), and reflects the tendency of soil P fixation or release. A current knowledge gap is to investigate how a rice-wheat rotation system affects soil DPS, which is closely related to P loss to the environment. In the present field study, a rice-wheat rotation was established and different fertilization treatments were implemented, and the Anthrosol soils collected from the rice season and the wheat season, were evaluated. Sequential extraction, Langmuir model, and structural equation modeling were used to reveal the proportion of P fractions, P sorption capacity, and the relationships between the degree of P saturation and soil properties, in order to reveal the geochemical mechanisms of P transformation. A higher degree of P saturation is always found in the rice season soils rather than in the wheat season soils, which should be attributed to the lower P retention capacity and the higher presence of P in forms with greater mobility potential in the rice season soils. The reduction of the proportion of residual P fraction coupled with the enhancement of NaHCO3-P/NaOH-P/HCl-P fractions indicated a prevalence of P mobilization due to flooding during the rice planting period. Simultaneously, lower P sorption capacity was also found in the rice season soils. Structural equation modelling was able to confirm that the change of P sorption capacity together with the enhancement proportion labile P in total P, and mobilization of soil residual P combined to affect the degree of P saturation. Management interventions, such as flooding, induced changes from crystalline iron oxides to amorphous iron oxides and this may have enhanced the release of P retained by crystalline iron oxides, which contributed to the mobilization of P in the soils after the rice season. This knowledge can be utilized to avoid P losses to the environment, while still achieving efficient P utilization.

Collaborative Seismic Impedance Inversion under Bayesian Parameter Estimation

Abstract At present, the industry mainly divides seismic inversion methods into deterministic inversion and deterministic inversion based on the methods and principles used in inversion. There are two types of geostatistical inversion. Deterministic inversion is the process of directly modifying the model quantity through optimization algorithms under given initial constraint conditions to obtain a definite solution; Geostatistical inversion uses random simulation methods to obtain inversion results through simulation and selection. The seismic data did not participate in the direct modification of the model, but rather served as a matching optimization constraint for the random model. Starting from the Bayesian formula, this article unifies deterministic inversion and geostatistical inversion within the Bayesian parameter estimation framework. From a theoretical framework, it analyzes the similarities and differences between deterministic inversion and geostatistical inversion, and proposes a method that combines deterministic inversion and geostatistical inversion - seismic wave impedance co inversion. Sequential Gaussian simulation and model constrained inversion are used as examples of the two major methods, The correctness of the research results and the feasibility and effectiveness of the proposed new inversion method and process have been verified through the application of the Marmousi theoretical model and actual data from Fudong, Fukang Depression, Xinjiang. It has been proven that the proposed method can improve the accuracy and precision of inversion. The inversion method and process used in this study obtained high-resolution and high-precision inversion results, which have good application prospects.

Mamba-in-Mamba: Centralized Mamba-Cross-Scan in Tokenized Mamba Model for Hyperspectral image classification

Hyperspectral image (HSI) classification plays a crucial role in remote sensing (RS) applications, enabling the precise identification of materials and land cover based on spectral information. This supports tasks such as agricultural management and urban planning. While sequential neural models like Recurrent Neural Networks (RNNs) and Transformers have been adapted for this task, they present limitations: RNNs struggle with feature aggregation and are sensitive to noise from interfering pixels, whereas Transformers require extensive computational resources and tend to underperform when HSI datasets contain limited or unbalanced training samples. To address these challenges, Mamba architectures have emerged, offering a balance between RNNs and Transformers by leveraging lightweight, parallel scanning capabilities. Although models like Vision Mamba (ViM) and Visual Mamba (VMamba) have demonstrated improvements in visual tasks, their application to HSI classification remains underexplored, particularly in handling land-cover semantic tokens and multi-scale feature aggregation for patch-wise classifiers. In response, this study introduces the Mamba-in-Mamba (MiM) architecture for HSI classification, marking a pioneering effort in this domain. The MiM model features: (1) a novel centralized Mamba-Cross-Scan (MCS) mechanism for efficient image-to-sequence data transformation; (2) a Tokenized Mamba (T-Mamba) encoder that incorporates a Gaussian Decay Mask (GDM), Semantic Token Learner (STL), and Semantic Token Fuser (STF) for enhanced feature generation; and (3) a Weighted MCS Fusion (WMF) module with a Multi-Scale Loss Design for improved training efficiency. Experimental results on four public HSI datasets—Indian Pines, Pavia University, Houston2013, and WHU-Hi-Honghu—demonstrate that our method achieves an overall accuracy improvement of up to 3.3%, 2.7%, 1.5%, and 2.3% over state-of-the-art approaches (i.e., SSFTT, MAEST, etc.) under both fixed and disjoint training-testing settings.

A deep-learning model for characterizing tumor heterogeneity using patient-derived organoids

Genotypic and phenotypic diversity, which generates heterogeneity during disease evolution, is common in cancer. The identification of features specific to each patient and tumor is central to the development of precision medicine and preclinical studies for cancer treatment. However, the complexity of the disease due to inter- and intratumor heterogeneity increases the difficulty of effective analysis. Here, we introduce a sequential deep learning model, preprocessing to organize the complexity due to heterogeneity, which contrasts with general approaches that apply a single model directly. We characterized morphological heterogeneity using microscopy images of patient-derived organoids (PDOs) and identified gene subsets relevant to distinguishing differences among original tumors. PDOs, which reflect the features of their origins, can be reproduced in large quantities and varieties, contributing to increasing the variation by enhancing their common characteristics, in contrast to those from different origins. This resulted in increased efficiency in the extraction of organoid morphological features sharing the same origin. Linking these tumor-specific morphological features to PDO gene expression data enables the extraction of genes strongly correlated with intertumor differences. The relevance of the selected genes was assessed, and the results suggest potential applications in preclinical studies and personalized clinical care.

Enhancing Oil Recovery in Eagle Ford Shale: A Multiscale Simulation Study of Surfactant Huff ’n’ Puff Methodology

In this paper, we present a simulation case study of a surfactant huff ’n’ puff pilot in the black oil window of the Eagle Ford (EF) Shale. The target horizontal well, which had been depleted for nearly 8 years, underwent stimulation via a surfactant huff ’n’ puff treatment. The surfactant was selected through laboratory screening using reservoir rock and fluid samples. After a 17-hour injection and a 1-month shut-in period, the well’s production increased fivefold from the baseline oil rate, sustaining incremental oil production for at least 2 years. The surfactant enhances oil recovery by altering rock wettability toward a more water-wet state and moderating oil/water interfacial tension (IFT). This process is modeled by surfactant adsorption in the simulator, indicating the degree of dynamic changes in relative permeability (krl) and capillary pressure (Pc) curves. We propose a comprehensive workflow comprising three stages: development of core-scale and field-scale models, sequential model calibrations, and multiobjective optimization to integrate laboratory measurements and field data from this pilot into multiscale numerical simulations. By matching oil recoveries from imbibition experiments on the core model and field production histories on the field model, krl and Pc profiles of two extreme states, basic reservoir properties, and additional reservoir properties altered during huff ’n’ puff operations are characterized. The matched core model reproduces a 15.1% incremental oil recovery for surfactant-assisted spontaneous imbibition (SASI) process relative to pure brine imbibition process. The matched reservoir model predicts the surfactant huff ’n’ puff treatment increases the oil production by 21.9% relative to water huff ’n’ puff treatment and by 52.9% relative to primary depletion for a 4-year period. The calibrated reservoir model also serves as a base case for optimizing well operation schedules through the implementation of a multiobjective genetic algorithm. The surfactant injection rate, injection time, and well shut-in time of the base case are varied to achieve higher oil production and reduced surfactant usage. Statistical analysis of eight trade-off cases indicates that optimal well operations, compared with existing practices, frequently involve increased injection rates [16.6–18.9 barrels per minute (bpm)], shorter injection periods (10–11.3 hours), and prolonged shut-indurations (49–65 days). This workflow offers valuable insights into surfactant huff ’n’ puff treatments for unconventional reservoirs, thereby facilitating the optimization of well operations and maximizing tertiary oil recovery.

SLIDE-Net: A Sequential Modeling Approach With Adaptive Fuzzy C-Mean Empowered Data Balancing Policy for IDC Detection

SLIDE-Net: A Sequential Modeling Approach With Adaptive Fuzzy C-Mean Empowered Data Balancing Policy for IDC Detection

Integration of machine learning and process‐based model outputs via ensemble Kalman filter enhanced space–time modelling of soil organic carbon in a highly human impacted area

AbstractAccurate prediction of soil organic carbon stock (SOCS) dynamics in areas with intensive human activities is crucial for developing sustainable soil management practices and climate change mitigation strategies. This study investigated the spatiotemporal dynamics of SOCS by collecting a total of 1219 topsoil samples in southern Jiangsu Province of China in 1980, 2000 and 2015, and compared the performance of three predictive models: random forest (RF), RothC, and a hybrid model of RF‐RothCEnKF. The hybrid model integrated outputs from the process‐based RothC model and the data‐driven RF model using the Ensemble Kalman Filter (EnKF) for sequential model state updates. Results showed that the three models presented similar spatial patterns of SOCS from 1980 to 2015, with relatively higher SOCS mainly distributed in the areas surrounding Taihu Lake. The mean SOCS change rates estimated by the RF‐RothCEnKF model represented an overall net increase of 0.04 t C ha−1 yr.−1 during that period. The RF‐RothCEnKF model exhibited high prediction accuracy, with an R2 of .52, a mean absolute error (MAE) of 7.38 t C ha−1, and a root mean square error (RMSE) of 9.13 t C ha−1 in 2015. This highlighted the RF‐RothCEnKF's ability to enhance performance when the individual RF model (R2 = .47, MAE = 7.66 t C ha−1, and RMSE = 9.42 t C ha−1) and the RothC (R2 = .13, MAE = 8.77 t C ha−1, and RMSE = 10.87 t C ha−1) fell short. Our findings may not only provide a framework for integrating process‐based and machine learning models to enhance the accuracy and adaptability of SOCS modelling in areas affected by intensive human activities, but also offer some guidance for developing sustainable agricultural practices and carbon management strategies in complex environmental settings.

Improving Health Outcomes Through Treatment Sequencing Optimization in Multiple Myeloma: A Simulation Model in Transplant-Ineligible Patients.

Patients with multiple myeloma often require multiple treatment lines. The order in which treatments are sequenced has impact on clinical outcomes. This study aimed to estimate progression-free survival (PFS) and overall survival (OS) with common treatment sequences used in Portugal and the incremental benefit of an optimal sequence in transplant-ineligible patients with multiple myeloma. A state-transition sequential model with a five-health state conceptual structure was developed to simulate and compare survival outcomes between treatment sequences up to four lines of treatments. Data sources included randomized clinical trials and indirect treatment comparisons. A panel of Portuguese hematologists listed four most common treatment sequences and optimal sequence of choice in transplant-ineligible patients. Our simulation estimated an OS between 6.1 and 7.8 years using the most common sequences, with VMP + DRd + Pd + Kd as the most effective (7.8 years). Optimal sequence of choice (DRd + PVd + Kd + Vd) achieved OS of 9.8 years and may extend OS in 2.0-3.7 years vs. most common sequences (26%-61% increase). This benefit was mostly explained by extended PFS in the first line of treatment. Model results demonstrate that choosing the most effective treatment upfront is crucial in delaying disease progression thus yielding better survival outcomes in transplant-ineligible patients. There was a clear survival benefit in using daratumumab-based regimens in first line. This modelling exercise highlights the need to raise awareness around the impact of sequencing strategies to improve patient's outcomes.

Analog Sequential Hippocampal Memory Model for Trajectory Learning and Recalling: A Robustness Analysis Overview

The rapid expansion of information systems in all areas of society demands more powerful, efficient, and low‐energy consumption computing systems. Neuromorphic engineering has emerged as a solution that attempts to mimic the brain to incorporate its capabilities to solve complex problems in a computationally and energy‐efficient way in real time. Within neuromorphic computing, building systems to efficiently store the information is still a challenge. Among all the brain regions, the hippocampus stands out as a short‐term memory capable of learning and recalling large amounts of information quickly and efficiently. Herein, a spike‐based bio‐inspired hippocampus sequential memory model is proposed that makes use of the benefits of analog computing and spiking neural networks (SNNs): noise robustness, improved real‐time operation, and energy efficiency. This model is applied to robotic navigation to learn and recall trajectories that lead to a goal position within a known grid environment. The model is implemented on the special‐purpose SNNs mixed‐signal DYNAP‐SE hardware platform. Through extensive experimentation together with an extensive analysis of the model's behavior in the presence of external noise sources, its correct functioning is demonstrated, proving the robustness and consistency of the proposed neuromorphic sequential memory system.

Pengaruh Implementasi Kebijakan Program Keluarga Harapan Terhadap Kesejahteraan Masyarakat di Kecamatan Cimahi Selatan Kota Cimahi

This research is entitled “The Effect of the Implementation of the Program Keluarga Harapan Policy on Community Welfare in South Cimahi District, Cimahi City”. The research objective is to analyze the impact of the implementation of the Program Keluarga Harapan (PKH) policy on community welfare in Cimahi Selatan District. The method used is a combination (mixed methods) with a sequential explanatory model, involving 154 respondents as samples and informants such as the Head of Linjamsos and PFM, Kasi Linjamsos and PFM, PKH Social Coordinator of Cimahi City, and KPM of South Cimahi District. Data collection techniques include literature study, questionnaires, interviews, and observations, with analysis using product moment correlation techniques. The results showed that the implementation of PKH policy in Cimahi Selatan Sub-district was in the good category, with an average score of 2.64 (88.00%). The dimensions of communication and bureaucratic structure get the highest score (2.67 or 89%), while the resource aspect has the lowest score (2.60 or 86%). For community welfare, the mean score was 2.61 (87.00%), with the prosperous life aspect getting the highest score (2.67 or 89%) and good adjustment getting the lowest score (2.56 or 85%). Hypothesis testing showed a p-value of 0.04 (<0.05), indicating that the research hypothesis is accepted. The magnitude of the effect of PKH policy implementation on community welfare is 0.85 (85%).

S-слівна арифметика та високоточні обчислення

The intricacies of using S-word arithmetic, the influence of the value of the parameter S on the estimation of the rounding error are analyzed; what are high-precision calculations and where they are used. The problems of two-key cryptography, computer steganography and the problem of transcomputational complexity are considered as areas of application of S-word arithmetic. For the development of S-word arithmetic algorithms, sequential, parallel, quantum computing models are used, and systems of residual classes are used. The architectural features of the computer system for the implementation of an effective algorithm in various models of calculations are considered. For the parallel computing model, the importance of reducing the connected steps is indicated, which can increase the amount of processed data, but allows to involve a larger number of parallel processors. This approach is in conflict with a method that reduces the amount of processed data, and there is a need to maintain a balance between these two methods in a parallel computing model. For the quantum computing model, the connection of qubits is a key factor in determining the quantum volume. The physical scheme determines which pairs of qubits can be entangled in a quantum computer. Peculiarities of transferring algorithms to another computing model are considered. An analysis of the complexity of implementing S-word arithmetic operations in sequential, parallel, and quantum computing models is carried out. For the parallel computing model, the importance of reducing the connected steps is indicated, which can increase the amount of processed data, but allows to involve a larger number of parallel processors. This approach is in conflict with a method that reduces the amount of processed data, and there is a need to maintain a balance between these two methods in a parallel computing model. For the quantum computing model, the connection of qubits is a key factor in determining the quantum volume. The physical scheme determines which pairs of qubits can be entangled in a quantum computer. Information is provided about the ongoing scientific forum «Calculation optimization issues», the subject of which is closely related to the topic (1969-2023)

Community Empowerment pada Program One Product One RW (OPOR) Di Kota Cimahi

The aim of this research is to optimize the community empowerment model in Cimahi City through the One Product One RW (OPR) program after the Covid-19 pandemic. After the Covid-19 pandemic, Cimahi City became one of the cities in West Java Province that was affected by the Covid-19 pandemic. It is hoped that by re-implementing the OPOR program, the community will be more efficient and able to compete to create a prosperous Cimahi City. descriptive research method with a Mixed Method Sequential Explanatory model approach. The data collection and processing process will predominantly use a quantitative approach, supported by an explanation of the qualitative approach. The survey results will be processed using SPSS version 20 and Ms. Excel, while the interview data will be processed using NVivo12. The research results for the implementation quality of the One Product One RW (OPOR) Program in Cimahi City in 2023 are at an implementation quality score of 85. A score of 85 for the implementation quality of the OPOR Program indicates high implementation quality. It can be identified that the highest score is in the dimension of compatibility between the user group and the OPOR implementing organization with an implementation quality score of 86.8. , an indicator with 93% of respondents agreeing that the OPOR Program needs to be evaluated if it is to be continued and an indicator with 88.4% of respondents agreeing that the OPOR Program will be continued. This value shows that more respondents tend to want the OPOR program to be evaluated first before continuing. If the OPOR Program is to be continued, note that there needs to be a grouping of participants based on their business clusters, because the implementation of OPOR in 2023 is considered to combine fashion clusters with food, drink and craft value is unfair in terms of assessment weight.

Analyzing the Interplay Between Competition State Anxiety, Motor Motivation, and Coping Styles Among Adolescent Track and Field Athletes

The relationship between competition state anxiety, motor motivation and coping styles of adolescent track and field athletes in China was investigated using interview and questionnaire research methods. The results showed that the mean scores of cognitive state anxiety and somatic state anxiety were lower in junior track and field athletes who had entered the echelon for a short period of time than in older athletes, and the opposite was true for state self-confidence; there were highly significant differences and significant differences in the identity regulation and introjection regulation dimensions of motor motivation; and there were significant differences in the focused problem-solving coping dimension of coping style. This paper proposes an algorithm for classifying athletic visual mirrors based on sequential model mining. This paper focuses on two issues – feature extraction and definition of semantic rules. In the feature extraction stage, the track and field video footage is automatically segmented into a series of identifiable sequences of athletic events, and then each type of behavioral event is identified using a mechanically learned algorithm. There were no significant differences between the three age groups in terms of race state anxiety, identity regulation and introjection regulation, and no significant differences in coping styles. There were no significant differences in the anxiety of competition status, motivation and coping styles among youth athletes of different sport levels. The results showed the effectiveness of the present algorithm for classifying track and field video cameras.

Emotion regulation among adults with asthma: Links with short-acting inhaler medication overuse and utilization of acute medical care

Objective Existing research suggests that emotion plays an important role in airway inflammation and asthma symptom control. The objective of this study was to determine whether difficulties regulating emotion were associated with overuse of short-acting inhaled medications and acute medical care usage in adults with asthma. Methods The sample included 401 adults with asthma recruited from an online panel of adults with chronic respiratory disease. Sequential binary logistic regression models were used to examine the associations of emotion regulation with short-acting inhaled medication use and acute medical care use, controlling for patient characteristics and comorbid mental health conditions. Results Greater difficulties with emotion regulation were significantly associated with greater odds of short-acting inhaler medication overuse (p < 0.001), emergency department visits (p < 0.001), and hospitalizations (p = 0.001). Conclusions Emotion dysregulation may play an important role in asthma management. Evidence-based interventions to reduce difficulties in emotion regulation may help improve problematic patterns of short-acting medication overuse and acute service use. The current findings should be interpreted in the context of several limitations, including the use of self-report measures. Future research should use electronic medical records or metered dose inhalers to objectively assess short-acting inhaler overuse and acute medical care use.