Central Processing Unit Research Articles

Effective Machine Learning Techniques for Dealing with Poor Credit Data

Credit risk is a crucial component of daily financial services operations; it measures the likelihood that a borrower will default on a loan, incurring an economic loss. By analysing historical data for assessment of the creditworthiness of a borrower, lenders can reduce credit risk. Data are vital at the core of the credit decision-making processes. Decision-making depends heavily on accurate, complete data, and failure to harness high-quality data would impact credit lenders when assessing the loan applicants’ risk profiles. In this paper, an empirical comparison of the robustness of seven machine learning algorithms to credit risk, namely support vector machines (SVMs), naïve base, decision trees (DT), random forest (RF), gradient boosting (GB), K-nearest neighbour (K-NN), and logistic regression (LR), is carried out using the Lending Club credit data from Kaggle. This task uses seven performance measures, including the F1 Score (recall, accuracy, and precision), ROC-AUC, and HL and MCC metrics. Then, the harnessing of generative adversarial networks (GANs) simulation to enhance the robustness of the single machine learning classifiers for predicting credit risk is proposed. The results show that when GANs imputation is incorporated, the decision tree is the best-performing classifier with an accuracy rate of 93.01%, followed by random forest (92.92%), gradient boosting (92.33%), support vector machine (90.83%), logistic regression (90.76%), and naïve Bayes (89.29%), respectively. The classifier is the worst-performing method with a k-NN (88.68%) accuracy rate. Subsequently, when GANs are optimised, the accuracy rate of the naïve Bayes classifier improves significantly to (90%) accuracy rate. Additionally, the average error rate for these classifiers is over 9%, which implies that the estimates are not far from the actual values. In summary, most individual classifiers are more robust to missing data when GANs are used as an imputation technique. The differences in performance of all seven machine learning algorithms are significant at the 95% level.

A chip thermal management method realizing integrated applications of cooling, power generation and heat flow measurement based on thermoelectric effect

Thermoelectric cooling, power generation and heat flow measurement are widely used in frontier domains while each of them is still in an isolated state. They are now widely used in fields such as electronic cooling, wearable device, sensors and so on. This paper proposes a method of thermoelectric effect combined with pulse width modulation (PWM) to achieve integrating thermoelectric applications. In a PWM cycle, the integration of thermoelectric applications makes it possible to realize three different operations of temperature control, energy recovery and heat flow measurement through a single thermoelectric module. The experimental results indicate that increasing the input voltage of the thermoelectric module extends the duration for power generation and heat flow measurement, while also enhancing the energy recovery rate. When the target temperature is higher than the ambient temperature, although the temperature control accuracy slightly decreases with the increase of the target temperature, the average temperature fluctuation range of the whole experimental group is −0.09∼+0.1℃ and energy recovery rate increases by 79.9 %. When the target temperature is lower than the ambient temperature, the system can still achieve efficient energy recovery. Applying the method to the computer central processing unit(CPU), the experimental results present that the heat dissipation of CPU can be monitored by measuring heat flow. Compared with the CPU + FPU working, the total power generation is increased by 0.285 W and energy recovery rate is increased by 78.3 % when the CPU is idle.

Logic Diagnosis Based on Logic Built-In Self-Test Signatures Collected In-Field from Failing System-on-Chips

Modern embedded nanoelectronic devices, particularly in safety-critical sectors, require high dependability throughout their lifecycle. To address this, designers have started integrating extra circuitry for on-device self-testing, such as the Logic Built-In Self-Test (LBIST). However, while automatic test equipment (ATE) ensures exhaustive testing during manufacturing, in-field testing capabilities are limited. This study introduces a novel methodology for in-field data collection of failure information from LBIST engines and a subsequent logic diagnosis strategy to facilitate failure analysis of field returns. The information is collected from key-on and key-off self-tests, executed by central processing units (CPUs) with a fixed seed and different frequency configurations, primarily addressing transition delay (TRN) faults. The proposed approach capitalizes on the constrained in-field configurability of LBIST and does not require a custom architecture, making it highly practical and readily applicable to real-world devices. The logic diagnosis strategy significantly reduces the number of candidate faults by exploiting the first failing pattern index found during the in-field testing and data collection. Reducing fault candidates could enhance accuracy during failure analysis, especially when field return devices exhibit a “No Trouble Found” (NTF) behavior. The experimental results are reported for ITC’99 benchmarks and an industrial automotive system-on-chip (SoC) produced by STMicroelectronics, with about 20 million gates.

RISC-V Leads the Future: Evolution and Outlook of CPU Architecture

This essay mainly talked the development of CPU and some instruction set architecture based on RISCV. CPU is known as the Central Processing Unit, the main appliment is RISCV, Also the ARM architecture is characterized by a focus on the balance between low power consumption and high performance. Then X86, its significant advantage is its complex instruction set and excellent performance, so it can handle complex computational tasks. We also explore the Pipeline technology, it is a widely used parallel processing technology today. The design principle is to divide complex, multilevel combinational logic circuits into multiple levels. Then, we find some experiment data to explore our conclusion. We find that each has its own advantages and is better suited to different scenarios. In terms of performance, x86 offers superior capabilities but consumes more power, making it ideal for high-performance computing and server applications. ARM, on the other hand, excels in power efficiency and finds its primary use in mobile devices and embedded systems. RISC-V, known for its flexibility, allows for a balance between performance and power consumption based on specific requirements, making it suitable for highly customizable applications, IoT devices, and emerging high-performance computing markets. The advantages of each architecture in different fields depend on the specific application environment and demands placed on them.

Icelandic Prevention Model in Rural Appalachian Communities: Gauging Stakeholder Experience with the Core Processes Three Years into County-Level Implementation

Background: This study assessed stakeholder experience with the core processes of The Integrated Community Engagement (ICE) Collaborative, a primary prevention approach addressing adolescent substance use in rural West Virginia, after three years of county-level implementation. Objectives: Guided by the Icelandic Prevention Model (IPM), the ICE Collaborative aims to enhance cooperation between researchers, policy makers, local practitioners, and community members and facilitate a paradigm shift in youth and community substance use prevention. This shift involves moving away from strategies focused on the repeated allocation of short-term grants that fund time-limited programs to a long-term, holistic, and sustainable approach overseen by local practitioners and coalitions. Results: We conducted qualitative interviews and focus groups with 33 stakeholders during the fall of 2022. Data analyses generated six major themes: 1) It Takes a Village to Prevent Adolescent Substance Use, 2) Improving Understanding and Commitment to Prevention Through Outreach, 3) Enhancing Student Engagement and Program Accessibility, 4) Addressing the Chronic Underfunding of Prevention and Youth Development Programs, 5) Acknowledging Family Contexts and Family Member Substance Use as Risk Factors, and 6) ICE/IPM Inspiring Solution-based Conversations, Goal Setting, and Strategy Selection. Conclusions: Stakeholders reported multiple positive characteristics of ICE for their communities and applauded the long-term focus and access to local data. Several suggestions for improved strategies were also reported. Results are discussed in line with the theoretical underpinnings of the IPM and current discourse around community health promotion in rural areas.

Reducing emotion dysregulation online in nonclinical population with compassion focused therapy and emotional competencies program: A randomized controlled trial.

The present randomized controlled study aimed to evaluate the effects of two interventions on emotional dysregulation as a primary outcome and on depression, anxiety, stress, well-being, self-compassion and emotional competencies as secondary outcomes. Finally, the mediating role of emotional dysregulation as a core process in the effect of interventions on anxiety-depressive and stress symptoms was evaluated. One hundred and seventy-nine individuals aged 18-68 years (M = 39, SD = 12.4; 83.9% females) with emotion regulation difficulties were randomly assigned to one of the three 12-weeks online programs: Compassion Focused Program (CFP), an Emotional Competencies Program (ECP) or a Waitlist control condition (WL). Participants completed pre-, post- and 3 months follow-up measures of each outcome. Mixed effect linear models compared groups on primary and secondary outcomes. Results showed that (1) perceived credibility and expectancy and satisfaction did not differ between the two interventions, (2) both interventions reduced emotion regulation difficulty, depression, anxiety and stress symptoms, and enhanced well-being, self-compassion, and emotional competencies; (3) ECP was more effective to enhance well-being than CFP; (4) CFP was more effective to enhance emotional competencies than ECP, and (5) emotion regulation difficulty mediated the effect of the interventions on depression, anxiety, and stress symptoms. Findings offer preliminary support for the usefulness of online CFP and ECP to reduce emotion dysregulation, symptoms of depression, anxiety, and stress and to increase well-being, self-compassion and emotional competencies. These results are discussed regarding clinical practice and the transdiagnostic role of emotional dysregulation.

Navigating Digital Transformation in the UAE: Benefits, Challenges, and Future Directions in the Public Sector

Digital transformation is a process in which the latest technologies are used in various business fields to keep pace with continuous changes. It involves the strategic and profound integration of digital technologies into an organization’s core business operations, processes, and models. In this study, a quantitative approach was used to study the impact of DT adoption on public sector transformational change projects in the United Arab Emirates (UAE). The diffusion of innovation theory (DIT) and the unified theory of acceptance and use of technology model (UTAUT) were used in the factor analysis. This study highlights that digital transformation initiatives in the UAE have benefited from a strategic alignment with government initiatives, such as AI and blockchain strategies. However, public sector organizations face challenges, such as the high costs of technology adoption and cybersecurity risks during integration with legacy systems. The significance of social influence, including elements like use behavior and behavioral intention, was identified as essential for digital transformation, suggesting the importance of technology in job performance. Similarly, digital transformation projects improve IT competence and reduce resistance to change among leaders and individuals. The findings underscore the importance of investing in infrastructure and continuous IT training to sustain digital transformation. More studies are required across specific sectors to further explore the impact and scalability of DT initiatives in the UAE public sector.

Nearest data processing in GPU

Memory wall is known as one of the most critical bottlenecks in processors, rooted in the long memory access delay. With the advent of emerging memory-intensive applications such as image processing, the memory wall problem has become even more critical. Near data processing (NDP) has been introduced as an astonishing solution where instead of moving data from the main memory, instructions are offloaded to the cores integrated with the main memory level. However, in NDP, instructions that are to be offloaded, are statically selected at the compilation time prior to run-time. In addition, NDP ignores the benefit of offloading instructions into the intermediate memory hierarchy levels. We propose Nearest Data Processing (NSDP) which introduces a hierarchical processing approach in GPU. In NSDP, each memory hierarchy level is equipped with processing cores capable of executing instructions. By analyzing the instruction status at run-time, NSDP dynamically decides whether an instruction should be offloaded to the next level of memory hierarchy or be processed at the current level. Depending on the decision, either data is moved upward to the processing core or the instruction is moved downward to the data storage unit. With this approach, the data movement rate has been reduced, on average, by 47 % over the baseline. Consequently, NSDP has been able to improve the system performance, on average, by 37 % and reduce the power consumption, on average, by 18 %.

Capturing Potential Interventions for the Empowerment of Older People and Informal Caregivers in Transitional Care Decision-Making: A Qualitative Study Using Focus Groups.

To capture older people's, informal caregivers' and health professionals' ideas on potential interventions for empowering older people and informal caregivers in transitional care decision-making. A descriptive qualitative design was adopted. The study was conducted between February and May 2022 in the region of Flanders, Belgium, as part of the TRANS-SENIOR consortium's collaborative research. Data were collected using focus groups, including older people, informal caregivers and healthcare professionals involved in any physical relocation of the older person across home, hospital or nursing home settings. Thematic data analysis was performed based on Braun and Clarke's six-step method. A total of 40 people participated in the focus groups. Four main themes were identified, which describe ideas on how to empower older people and informal caregivers in transitional care: Providing clear and timely information, preparing people for what is to come, person-centredness and providing professional and peer support for informal caregivers. Healthcare (professionals) should facilitate older people's and informal caregivers' empowerment in transitional care decision-making by setting them at the core and inception of the decision-making process. While informal caregivers support their loved ones in decision-making processes, they should also be supported and monitored for burdensome issues. Multicomponent, well-planned and personalised interventions are needed to empower older people and informal caregivers in transitional care decision-making. The ideas raised by all stakeholders who participated in this study can inform these interventions. Adhered to consolidated criteria for reporting qualitative research checklist. Organisations advocating for the interests of older people and informal caregivers played a pivotal role in shaping the TRANS SENIOR project. Furthermore, the study benefitted from the collaborative input of AGE Platform Europe, which amplified the voices and representation of older people during the project design phase.

Foundations and Frameworks of ELT and Applied Linguistics Research: Principles, Processes and Practices

This article offers a comprehensive analysis of the core principles, processes, and practices that characterize research in English Language Teaching (ELT) and Applied Linguistics. The article highlights the key distinctions between scholarship, aimed at personal knowledge acquisition, and research, which generates socially useful knowledge. Research in applied linguistics is problem-oriented, focused on addressing practical language-related challenges such as language acquisition, teaching methodologies, and multilingual communication. The paper emphasizes the interdisciplinary nature of applied linguistics, integrating insights from fields like psychology, sociology, and computer science to provide comprehensive solutions. Methodological diversity is a hallmark of this research, encompassing both qualitative and quantitative approaches, including case studies, surveys, and experimental studies. The research cycle, from identifying a problem to collecting and analyzing data, is systematically outlined. Key areas of ELT research-such as second language acquisition, curriculum development, and language assessment-are discussed, underscoring the field's contributions to both theory and practice. The article concludes by emphasizing the importance of applying research findings to improve language education, develop policies, and enhance communication across diverse linguistic contexts Int. J. Soc. Sc. Manage. Vol. 11, Issue-4: 126-135.

Improved modularity and new features in ipie: Toward even larger AFQMC calculations on CPUs and GPUs at zero and finite temperatures.

ipie is a Python-based auxiliary-field quantum Monte Carlo (AFQMC) package that has undergone substantial improvements since its initial release [Malone et al., J. Chem. Theory Comput. 19(1), 109-121 (2023)]. This paper outlines the improved modularity and new capabilities implemented in ipie. We highlight the ease of incorporating different trial and walker types and the seamless integration of ipie with external libraries. We enable distributed Hamiltonian simulations of large systems that otherwise would not fit on a single central processing unit node or graphics processing unit (GPU) card. This development enabled us to compute the interaction energy of a benzene dimer with 84 electrons and 1512 orbitals with multi-GPUs. Using CUDA and cupy for NVIDIA GPUs, ipie supports GPU-accelerated multi-slater determinant trial wavefunctions [Huang et al. arXiv:2406.08314 (2024)] to enable efficient and highly accurate simulations of large-scale systems. This allows for near-exact ground state energies of multi-reference clusters, [Cu2O2]2+ and [Fe2S2(SCH3)4]2-. We also describe implementations of free projection AFQMC, finite temperature AFQMC, AFQMC for electron-phonon systems, and automatic differentiation in AFQMC for calculating physical properties. These advancements position ipie as a leading platform for AFQMC research in quantum chemistry, facilitating more complex and ambitious computational method development and their applications.

AUTOMATED TECHNOLOGY OF MEASUREMENT OF PHYSIOLOGICAL PARAMETERS OF FARM ANIMALS

Abstract. The development and application of automated technologies in the agricultural sector have significantly transformed the management and monitoring of farm animals. This paper presents a comprehensive analysis of existing technical systems for measuring the physiological parameters of farm animals, particularly focusing on cows. The primary goal of these technologies is to enhance the accuracy and efficiency of health monitoring and to optimize livestock management through continuous data collection and analysis. Key technological solutions include the use of wireless data transmission systems, radio monitoring devices, and unmanned aerial vehicles (UAVs). These technologies enable real-time tracking of animal health parameters such as body temperature, heart rate, movement patterns, and other critical indicators. The use of various sensors, including ear, neck, and intra-body sensors, plays a crucial role in collecting primary physiological data. These sensors are integrated into a broader system that includes radio modules with built-in controllers for converting raw data into digital formats, enabling wireless transmission to central systems for processing and storage. The radio monitoring of animals allows for the continuous tracking of physiological and behavioral parameters across large areas, providing crucial insights into their well-being. It is particularly beneficial in detecting early signs of disease or health issues, ensuring timely intervention, and preventing potential economic losses for farmers. The integration of unmanned aerial vehicles (UAVs) further expands the capabilities of monitoring systems, enabling the visual assessment of animals' behavior and movement across vast pastures. Equipped with video cameras and data relays, UAVs collect video footage that is transmitted to a central processing unit for real-time analysis and storage. The combined use of radio monitoring and UAV technology forms a comprehensive framework for livestock management, allowing farmers to make data-driven decisions, optimize resource allocation, and improve overall herd health and productivity. By leveraging these automated systems, it becomes possible to reduce the dependency on manual labor, minimize human error, and ensure that animals are closely monitored without the need for constant physical presence. This represents a significant advancement in modern animal farming, aligning with global trends toward smart farming and precision agriculture. In conclusion, the integration of automated physiological monitoring technologies in farm animal management presents an innovative solution for enhancing animal welfare, improving farm efficiency, and reducing operational costs. These systems hold considerable potential for further advancements in the field of animal health monitoring, ultimately contributing to more sustainable and productive farming practices.

Analysis of the Dynamics of the Lower Drill String Assembly in a Deep Well Coring Operation

In order to carry out the geological characteristics, evolution law and other scientific research of the deep strata in the extra-deep well, it is necessary to carry out the coring operation in the extra-deep depth. However, with the increase of drilling depth, the force of drill string in the underground becomes more and more complicated, and there is contact collision between the core and the core after the core tool is bent, which makes the core broken. Clarifying the dynamic behavior of the BHA during the extra-deep well coring process is helpful to obtain a more complete core. Therefore, this paper adopts the simulation analysis method to simulate the coring process of the extra-deep well with a 163.5mm diamond bit and a 140mm corer under the real drilling hole trajectory. The stress, eddy motion and the contact between the inner barrel of the core tool and the core of the lower 1000m drill assembly are analyzed. The results show that the hole expansion rate of coring bit is 2.5% in the process of low bit weight and low rotational speed. When the core is 8m, the core barrel is under compression, bending and torsion, and under the restriction of the centralizer and the core hole, the middle and upper part of the core tool bend greatly, and the contact degree with the core is high, and the larger contact force is easy to cause the lower core to fracture.

Design of Digital Lux Meter Based on Arduino Using EL7900 Photodiode Sensor

Aims: To design and manufacture a digital lux meter to the illumination intensity (E) detection tool that is more efficient, relatively cheaper, and can be carried because it has a smaller shape and size compared to tools on the market. Study Design: Design of lux meter digital based on Arduino uno using an EL 7900 sensor. Place and Duration of Study: Physics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University, from June 2024 to September 2024. Methodology: The method used is to design electronic components so that it can become a digital lux meter. The main processor uses Arduino. The microcontroller functions as a data processor which is the output of the EL 7900 sensor. The HC-SR04 sensor is a distance sensor whose function is to measure the distance measured on the design tool. The detection results from the sensor are then processed in the microcontroller and displayed on the OLED. Results: Lux meter digital based on Arduino uno using EL 7900 sensors has been produced. The results of the research show that the further the measurement distance from the light source, the illumination intensity (E) decreases. This research also finds that the illumination intensity will increase if the source power is increased. Based on calibration testing between the design tool and the reference tool, it shows very good results with an error rate of 0.06%. Field test results showed that the level of accuracy of the illumination intensity between the design tool and the reference tool at a distance of 150 cm from the light source with different light source power reached 99.48%. Conclusion: In this research, digital lux meter to the illumination intensity (E) detection tool, relatively cheaper and can be carried because it has a smaller shape and size compared to tools on the market. Based on calibration testing, it shows very good results with an error rate of 0.06%.

Avoid backtracking and burn your inputs: CONUS-scale watershed delineation using OpenMP

The Memory-Efficient Watershed Delineation (MESHED) parallel algorithm is introduced for Contiguous United States (CONUS)-scale hydrologic modeling. Delineating tens of thousands of watersheds for a continental-scale study can not only be computationally intensive, but also be memory-consuming. Existing algorithms require separate input and output data stores. However, as the number of watersheds to delineate and the resolution of input data grow significantly, the amount of memory required for an algorithm also quickly increases. MESHED uses one data store for both input and output by destructing input data as processed and a node-skipping depth-first search to further reduce required memory. For 1000 watersheds in Texas, MESHED performed 95% faster than the Central Processing Unit (CPU) benchmark algorithm using 33% less memory. In a scaling experiment, it delineated 100,000 watersheds across the CONUS in 13.64s. Given the same amount of memory, MESHED can solve 50% larger problems than the CPU benchmark algorithm can.

Nonlinear integral backstepping control based on particle swarm optimization for a grid-connected variable wind energy conversion system during voltage dips

Double-fed induction generator (DFIG) wind turbines connected to the grid are particularly subject to grid problems such as voltage dips. Because of this, it might be challenging to maintain system stability and prevent system disconnections when using a Proportional-Integral (PI) Controller to operate this system. This paper applies the Integral Backstepping Control for the wind power plant system connected to the power grid. The Integral Backstepping is a nonlinear and recursive method that employs the Lyapunov theory to ensure the system's stability. The best selection of gain values for the Lyapunov function guarantees improved system control. These gains are frequently adjusted using the trial-and-error strategy, which is time-consuming and inefficient. This technique becomes more complex when many parameters need to be determined. It also limits the system's performance and restricts this nonlinear approach's advantages. The objective is to apply the particle swarm optimization for computing several constant values of the nonlinear approach by minimizing an integral absolute error criterion index. The weighted sum of errors is employed to solve the multiple objective problems. The proposed controller tracks the maximum power point, maintains the voltage of the DC-Link constant, and controls active and reactive power. The robustness of this method is verified in critical conditions, including system parameter variation and asymmetrical and symmetrical grid faults. The simulation findings highlight the effectiveness and robustness of the combination of Integral Backstepping with Particle Swarm Optimization in terms of reducing response time from 10.6 (ms) to 2 (ms), canceling static error, and improving overshoot compared to the vector control based on PI regulator. Besides, the DC-Link voltage ripples during the asymmetrical grid faults are reduced to ±1 (V) using the suggested controller. The latter can be implemented thanks to advancements in Central Processor Unit technology.

Evaluating Medical Entity Recognition in Health Care: Entity Model Quantitative Study.

Named entity recognition (NER) models are essential for extracting structured information from unstructured medical texts by identifying entities such as diseases, treatments, and conditions, enhancing clinical decision-making and research. Innovations in machine learning, particularly those involving Bidirectional Encoder Representations From Transformers (BERT)-based deep learning and large language models, have significantly advanced NER capabilities. However, their performance varies across medical datasets due to the complexity and diversity of medical terminology. Previous studies have often focused on overall performance, neglecting specific challenges in medical contexts and the impact of macrofactors like lexical composition on prediction accuracy. These gaps hinder the development of optimized NER models for medical applications. This study aims to meticulously evaluate the performance of various NER models in the context of medical text analysis, focusing on how complex medical terminology affects entity recognition accuracy. Additionally, we explored the influence of macrofactors on model performance, seeking to provide insights for refining NER models and enhancing their reliability for medical applications. This study comprehensively evaluated 7 NER models-hidden Markov models, conditional random fields, BERT for Biomedical Text Mining, Big Transformer Models for Efficient Long-Sequence Attention, Decoding-enhanced BERT with Disentangled Attention, Robustly Optimized BERT Pretraining Approach, and Gemma-across 3 medical datasets: Revised Joint Workshop on Natural Language Processing in Biomedicine and its Applications (JNLPBA), BioCreative V CDR, and Anatomical Entity Mention (AnatEM). The evaluation focused on prediction accuracy, resource use (eg, central processing unit and graphics processing unit use), and the impact of fine-tuning hyperparameters. The macrofactors affecting model performance were also screened using the multilevel factor elimination algorithm. The fine-tuned BERT for Biomedical Text Mining, with balanced resource use, generally achieved the highest prediction accuracy across the Revised JNLPBA and AnatEM datasets, with microaverage (AVG_MICRO) scores of 0.932 and 0.8494, respectively, highlighting its superior proficiency in identifying medical entities. Gemma, fine-tuned using the low-rank adaptation technique, achieved the highest accuracy on the BioCreative V CDR dataset with an AVG_MICRO score of 0.9962 but exhibited variability across the other datasets (AVG_MICRO scores of 0.9088 on the Revised JNLPBA and 0.8029 on AnatEM), indicating a need for further optimization. In addition, our analysis revealed that 2 macrofactors, entity phrase length and the number of entity words in each entity phrase, significantly influenced model performance. This study highlights the essential role of NER models in medical informatics, emphasizing the imperative for model optimization via precise data targeting and fine-tuning. The insights from this study will notably improve clinical decision-making and facilitate the creation of more sophisticated and effective medical NER models.

Design of Low Power Control Unit for RISC-V Processor Core

This research work focuses on the development of a low-power decode logic for a RISC-V processor core with specifications. The goal is to create a controller that performs all six groups of instruction formats outlined in the RV32I Base Integer Instruction Set. The control unit is designed to decode a total of 13 instruction sets, allowing for a comprehensive range of operations. A single instruction pipeline approach is implemented in the design to optimize performance. The synthesis of the design is carried out using the 32 nm standard library, resulting in a maximum operating frequency of 666.67 MHz. To further enhance power efficiency, clock gating techniques are employed, leading to a reduction in power consumption by 18.72 % from 112.15 µW to 91.45 µW. Additionally, the layout of the design is optimized, resulting in an area of 354.74 mm2. The successful development of this low-power decode logic demonstrates its potential for integration into larger RISC-V processor cores. Future enhancements can include expanding the instruction decoding capability to encompass the full range of 47 instructions in the RV32I Base Integer Instruction Set, as well as exploring additional pipeline stages to further improve performance. The results achieved in this project contribute to the ongoing pursuit of power-efficient and high-performance processor designs based on the RISC-V architecture.

The Study about the Impact of Douyin on Youth Culture and Consumption Behavior

This paper explores the significant influence of Douyin on youth culture and consumption behavior. Through a detailed case discussion and analysis of theoretical perspectives, it highlights the tension between content homogenization and multicultural expression. This tension is driven by personalized content tailored specifically for individual users, with core processes revolving around big data analytics and algorithmic recommendation systems. These systems raise ethical considerations in influencer marketing. The study reveals that the platform’s phenomenally popular cultural content amplifies the influencer economy and intensifies societal phenomena, such as the spectacle of society, which impact the cultural identity and consumption preferences of young people. The paper offers a series of recommendations based on these insights. Firstly, it suggests strategies for content diversification to combat the homogenization trend and promote a richer tapestry of cultural expression. Secondly, it emphasizes the need to reinforce ethics in influencer marketing to ensure that young audiences are not exploited or misled. Lastly, it advocates for measures to mitigate the negative impacts of the Society of the Spectacle on youth culture and consumer behavior. These recommendations aim to cultivate a more balanced digital ecosystem and facilitate a healthier and more democratic communicative environment. By implementing these strategies, Douyin can foster a platform that supports diverse cultural expressions while protecting and empowering its young users.

LINGUOCOGNITIVE AND LINGUOCULTURAL FOUNDATIONS OF THE PROCESSES OF TERMINOLOGIZATION IN MODERN KAZAKH AND TURKISH LANGUAGES

The intensification of the activity of the Kazakh language in accordance with its state status in all spheres of society, the renewal of its potential strengthened the connection of the language with the form of linguistic consumption, began to influence the requirements of everyday practice. This is evident from the active process of terminology creation in the social, economic, political, scientific and technical spheres. In addition, the core of the neonominational process, which created the interrelationships of language~ society, language~consciousness, language ~ culture, etc., is not only scientific and informational, but also cultural and social content. This is the basis for improving not only the quantitative, but also the qualitative level of modern language development. Therefore, the recognition of the influence of language and culture on each other in the form of new terms and the function of language as a unique means of transmitting culture within the framework of cultural representation is especially relevant. Based on this, new names and terms in the Kazakh language, which also serve as a unique feature of the cognitive level of language users, are considered in the article as a “linguistic picture of the world” in accordance with the process of updating the language. In this regard, the purpose of this article is to compare and characterize the linguocognitive and linguocultural foundations of terminologization processes in modern Kazakh and Turkish languages. The following methods were used in the course of the study: descriptive, comparative, cognitive, ethnolinguistic and conceptual. Also, to determine the cultural and national component structured in the mind of a native speaker in a terminologized word, diachronic, contrasting, definitive were used, and discursive analysis, connotative approach, and motivation methods were used to recognize the process of terminologization. The value of the research: by describing and showing the linguocognitive and linguocultural foundations of terminologization processes in modern Kazakh and Turkish languages, we contribute to the improvement and strengthening of the “Turkic world”. The linguistic data and conclusions presented in the article can be used as practical and theoretical material for research by students, undergraduates, doctoral students and young people studying at the faculties of Philology, Translation Studies, Oriental Studies and Turkology.