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The development of the business world in developing countries encourages companies to increase company value by improving their reputation in the eyes of the public. One important step in this effort is to increase profits and demonstrate responsibility towards the environment and society. This research aims to find and analyze theories that discuss the influence of green accounting, environmental performance and profit growth on company value. This research uses the literature review method to analyze and present the reasons behind the explanatory variables, as well as findings from previous research. The theories behind this research are agency theory and signal theory. Variables that are thought to influence company value include green accounting, environmental performance, and profit growth. The research results show that the relationship between green accounting, environmental performance and profit growth on company value varies, so a clear agreement has not been reached. This study provides a detailed picture based on existing facts and can be a reference for further research that uses green accounting, environmental performance and profit growth variables.

The electromagnetic field on high-frequency radio waves and its impact on signal transmission quality. High-frequency radio waves, used in wireless communication, can be affected by electromagnetic fields from various external sources, such as electronic equipment and building structures. This study analyzes the interaction between electromagnetic fields and radio waves, as well as their impact on transmission speed and signal range. The analysis results show that electromagnetic fields can cause distortion or interference in radio waves, affecting the quality of the received signal. This study provides deeper insights into the importance of managing electromagnetic fields to ensure stability and efficiency in high-frequency radio communication systems. The findings are expected to serve as a reference for the development of communication technology and the mitigation of electromagnetic interference on radio waves.

In this study, the implementation and simulation of a servo SG90 drive system based on Arduino Uno with dynamic angle control were carried out. The SG90 servo motor is widely used in various applications such as robotics and automation due to its ease of control and small size. The objective of this research is to develop a control system capable of adjusting the servo angle dynamically using Arduino Uno. The simulation method involves hardware and software simulations combined with servo control code based on PWM (Pulse Width Modulation) signals. The results show that the system is capable of accurately controlling the servo angle through a potentiometer as a dynamic input, along with simulation visualization in a software environment.

This research discusses the utilization of electromagnetic fields in navigation devices, which play a crucial role in daily life. With technological advancements, navigation systems such as GPS, radar, and compasses have become essential tools for determining position and direction. This study employs a literature review method, which involves a series of activities related to collecting library data, reading, recording, and processing relevant information for the research objective. The aim is to analyze various sources of information regarding the use of electromagnetic fields in these devices. The findings indicate that electromagnetic fields enable navigation devices to function effectively, despite challenges such as signal interference and weather conditions that may reduce accuracy. This research aims to explore how electromagnetic fields, which combine electric and magnetic fields, are utilized in modern navigation systems. The method used is a literature review, where the researchers collect and analyze information from various reliable sources to understand the effectiveness and challenges in the application of electromagnetic fields in these devices. The results show that electromagnetic fields allow navigation devices to operate effectively, providing accurate location information

This study comprehensively investigates the empirical impact of capital structure, profitability, and asset growth on the value of companies in the retail sector on the Indonesia Stock Exchange during the post-pandemic economic recovery phase (2021–2024). Adopting a quantitative methodology with purposive sampling of 12 entities (N=48), parameters were estimated using multiple linear regression after passing a rigorous classical assumption test including normality, multicollinearity, and heteroscedasticity. By proxiesizing the capital structure through Debt to Equity Ratio (DER), profitability through Return on Assets (ROA), and total asset growth to Price to Book Value (PBV), this study reveals the phenomenon of  market undervaluation with an average PBV of 0.17. The results of statistical tests confirm that these financial determinants partially and simultaneously have a significant influence on shaping the firm value. These findings provide a theoretical contribution regarding the relevance of funding policy and operational efficiency as crucial signals for investors in a volatile market landscape. The practical implications emphasize the importance of optimizing financial structure and operational performance to strengthen market confidence during the global economic transition period.

This paper is intended to analyze how institutions play a role as government institutions in upholding human rights, which focuses on human rights enforcement as reviewed from the performance of Komnas HAM institutions, the Ombudsman and other institutions, as well as the challenges faced in upholding human rights advocacy in Indonesia. Human rights problems are not a new problem in Indonesia, the many human rights violations in Indonesia if this problem continues to be left without evaluation and solutions, it will result in prosecution to the Government in order to solve the existing problems. Even after the reform, human rights violations continue to occur and are signaled in Indonesia, proving the inadequacy of the role of State Institutions to solve problems of human rights violations in Indonesia and ensure real security and protection of justice for the Indonesian people.

Bone health is a vital element in the quality of life aspect, especially with the increasing risk of osteoporosis and fractures with age. Extremely Low Frequency (ELF) electromagnetic field therapy offers the potential for bone regeneration through collagen synthesis, osteoblast stimulation, and molecular signal modulation. This study evaluates the potential and efficacy of ELF for bone health based on scientific literature. The results of the analysis show that ELF is effective in accelerating healing, increasing bone density, and is safe at certain intensities. However, optimization of parameters such as duration and intensity of exposure is still needed. This study supports the development of standard protocols for safe and effective ELF applications in bone therapy.

This study examines the selection of an optimal underwater monitoring system for early detection of submarines in the ALKI II region. ALKI II is known as a strategic area that requires intensive monitoring to protect maritime security and national interests. The main focus of the study is to analyze various technical, operational, and economic factors that influence the selection of an underwater monitoring system. Through a comprehensive analysis approach, this study considers the detection capability, resolution, and adaptability of the diverse marine environment in the ALKI II region. Various types of underwater monitoring systems are evaluated, including passive sonar, active sonar, and visual monitoring systems. Passive sonar uses sound signal reception from targets, while active sonar sends signals and detects the reflected signals. Visual monitoring systems rely on cameras and other optical equipment. The decision to select a system is based on a thorough analysis of the advantages and disadvantages of each system. Passive sonar, for example, excels in quiet environments but may be less effective in high sea noise. On the other hand, active sonar may be more accurate but can be detected by submarines equipped with anti-sonar technology. This study provides useful guidance for decision makers in determining the optimal underwater monitoring system for the ALKI II region. By considering technical, operational and economic factors, it is hoped that the results of this research can improve early detection capabilities for submarines and strengthen maritime security in the region.

Time Division Multiplexing (TDM) is a multiplexing technique that enables the transmission of multiple signals over a single communication channel by allocating separate time slots for each signal stream. This study aims to analyze the working principles of TDM, examples of its implementation, as well as the benefits and drawbacks of TDM in communication networks. The findings show that TDM can enhance data transmission efficiency through flexible bandwidth allocation, minimizing signal interference and thereby improving signal quality. Due to these advantages, TDM is widely used in fiber optic communication systems, such as telephone and internet networks. Additionally, all data processing is conducted within integrated circuits utilizing Plastic Optical Fiber (POF), allowing TDM to be implemented at a low cost. 

This study aims to obtain empirical evidence regarding the effect of trading volume activity, profitability and solvency on stock returns. This study analyzed 122 samples of banking companies listed on the Indonesia Stock Exchange for the 2019-2022 period. The samples were selected through purposive sampling method and analyzed using multiple linear regression. The results of the study indicate that trading volume activity has no effect on stock returns, profitability has a positive effect on stock returns and solvency has a negative effect on stock returns. The theoretical implications of this study are able to confirm the signal theory based on the hypothesis tests conducted. The practical implications of this study provide knowledge and can be used as consideration for companies regarding financial management policies and as considerations for investors regarding investment decision making

Objective: analyze the modulation scheme that can intelligently select the appropriate modulation model for service conditions to obtain a high Signal to Noise Ratio, as well as throughput efficiency on wireless networks through the DNN approach. Method: this study uses simulations with the Python language, through AI-Driven on BPSK, QPSK, 16-QAM, and 64-QAM modulation, to determine the SNR and Quality of Service (QoS) produced, both through conventional approaches and Deep Neuro Network (DNN). Researh Finding: AI-Driven modulation used for Cognitive Cellular Networks (CCN), through Deep Neuro Network designed to intelligently classify and select the appropriate modulation model to be applied, shows significant improvement in throughput efficiency, QoS and has the ability to adapt to the environment in dynamic networks. Conclussion: AI-Driven using Deep Neuro Network is able to dynamically adapt to determine the selected modulation model, according to the user's environmental conditions, increase spectrum efficiency and throughput, and increase SNR which can automatically increase the efficiency of network usage.

Cancer is characterized by uncontrolled cell proliferation. The upregulation and activation of the Cyclin-Dependent Protein Kinase 6 (CDK6) signaling pathway can induce unregulated breast cancer cell proliferation. Therefore, CDK6 inhibition continues to be developed as a potential target for drug design and development to treat breast cancer. This study aims to predict the biofunction of anthocyanin compounds from purple corn extract as inhibitors of Cyclin-Dependent Protein Kinase 6 (CDK6) using an in silico approach. The research methods included data mining, ligand and receptor preparation, molecular docking, docking visualization, and data analysis. Our results indicate that six compounds from purple corn extract (cyanidin, cyanidin 3-glucoside, pelargonidin-3-glucoside, pelargonidin, peonidin, and peonidin-3-glucoside) can bind to CDK6 at the C-terminal and N-terminal domains. The binding pattern suggests that cyanidin, cyanidin 3-glucoside, pelargonidin-3-glucoside, pelargonidin, peonidin, and peonidin-3-glucoside interact with CDK6 residues in the same manner as Palbociclib (control). This finding indicates that compounds from purple corn have the potential to act as competitive CDK6 inhibitors. Peonidin-3-glucoside exhibited the lowest binding energy of -282.5 kcal/mol, approaching that of Palbociclib (-329.4 kcal/mol).

This study presents the implementation of Transfer learning using the ResNet-18 architecture for classifying 10 musical instrument categories based on visual representations of audio signals. The audio waveform is transformed into image-like inputs appropriate for CNN processing, accompanied by data augmentation and ImageNet-standard normalization. ResNet-18 is utilized due to its efficient feature extraction capability enabled by residual blocks, which help overcome vanishing gradient issues. The model was trained for 10 Epochs using the AdamW optimizer and Cross-Entropy Loss. Experimental results show that the model achieved a maximum validation accuracy of 77.35%, with a stable downward trend in training loss, indicating effective feature learning. However, several misclassification cases were observed, particularly among instruments with similar spectral characteristics, such as drum–violin and tabla–sitar. These findings demonstrate that while ResNet-18 performs reliably for musical instrument classification, further improvements remain possible through deeper architectures like ResNet-50, more comprehensive hyperparameter optimization, and the use of richer audio representations such as Mel-Spectrograms. This research provides an essential foundation for developing automated music analysis systems powered by Deep Learning.

This study aims to evaluate the accuracy of the Fibonacci Retracement and Moving Average Convergence Divergence (MACD) technical indicators in analyzing the movement of banking stock prices listed on the LQ45 index in the 2020 period. Based on data from PT Kustodian Sentral Efek Indonesia (KSEI) in 2023, there was a significant increase in the number of investors in the Indonesian stock market by 103.6 percent in 2020. This study uses a quantitative descriptive approach with a census method for sampling, which resulted in 5 banking companies as samples: BBCA, BBNI, BBRI, BBTN, and BMRI. Data analysis was carried out using the Fibonacci Retracement indicator to identify potential support and resistance levels, and the MACD indicator to evaluate the strength, direction, and momentum of stock price movements. The results showed that 11 of the 11 signals generated by the Fibonacci Retracement were proven to be accurate, while 43 of the 53 signals generated by the MACD were also proven to be accurate. In conclusion, the buy and sell signals generated by the Fibonacci Retracement and MACD indicators are reliable and effective for use in banking stock trading.

The process in artificial intelligence is similar to the process that occurs in the human brain. The pattern recognition process is to determine whether the input and output values ​​are the same so that the system can detect the pattern. A reciprocal relationship between the input and output levels is made possible by the two interconnected layers of the Bidirectional Associative Memory (BAM) technique. As a result, this technique can serve as an associative memory, meaning that some of the data stored in it can be called upon.. The load signal is transmitted from the input layer X to the output layer Y in this procedure. The weight value will be modified by computing the outcomes between the ranks [1,0]. In this work, the active sigmoid function is employed. Three input characters—the letters S, O, and X—are used in this study's 5x5 order matrix. A pattern [27 4] that matches the target is produced by the target letter S [1,-1], a pattern [27 -3] that does not match the target is produced by the target letter O [1.1], and a pattern [-37 21] that matches the target is produced by the target letter X [-1,1]. Consequently, not all patterns are able to meet the established objectives.

Fire is an event that often occurs due to human negligence. Losses from fire include damage to goods, business delays and can even cause loss of life. For early handling of fires, a "Fire Fighting Control Design Using MQ-2 Sensor and Fuzzy Logic Based 5 Channel Flame Sensor" has been created. This tool uses the MQ-2 sensor to detect gases related to fires, such as hydrocarbon gas. A 5 channel flame sensor is used to detect flames. The predetermined fuzzy logic rules will be used to control the fire extinguisher and implemented using the Arduino Atmega 2560 microcontroller as the brain of the system. Next, the microcontroller will send a control signal to the 12V DC pump output as a water spray system to deal with fires. The test results of this system show that the tool can detect early fire hotspots and respond well to fire extinguishing according to the fuzzy rule decisions carried out by testing the tool with 30 fire spots at different distances.

The combination of hormone treatment and genetic liabilities in transgender women leads to a complicated situation for breast cancer growth particularly linked to BRCA1 mutations. Gender-affirming treatment relies heavily on estrogen and causes intense cell growth and differentiation in breast tissue driven by estrogen receptor mechanisms. Mutations in BRCA1 lead to weak DNA repair processes which enhance an individual's vulnerability to cancerous changes. Rodestrogens enhance the functioning of proliferative pathways like PI3K/AKT and MAPK pathways in cells with dysfunctional BRCA1. This collaborative action elevates the threats posed by benign growths such as fibroadenomas leading to invasive breast cancer. By affecting the expression of vital regulatory proteins linked to cell proliferation estrogen further compromises the genomic integrity in cells harboring BRCA1 mutations. The hormone environment influenced by exogenous estrogen therapy can shape the tumor microenvironment for better cancer progression and metastasis. Comprehending the relationship between estrogen signaling and pathways related to BRCA1 is important for identifying the enhanced risk of cancer in transgender women using hormone therapy. This detailed study aligns recent discoveries regarding genetic vulnerability and hormonal impacts with cell mechanisms to reveal a detailed insight into breast cancer progression in these individuals. The study emphasizes the necessity for custom-designed cancer screening methods and targeted treatments to help mitigate risks and support transgender care. Understanding these pathways greatly enriches our knowledge of hormone-induced carcinogenesis among those who carry certain genetic markers while also guiding the creation of personalized care for transgender women at enhanced risk of breast cancer.

Image denoising is a fundamental challenge in image processing, where the objective is to remove noise while preserving critical image features. Traditional denoising methods, such as Wavelet, Total Variation (TV) minimization, and Non-Local Means (NLM), often struggle to maintain the topological integrity of image features, leading to the loss of essential structures. This study proposes a Cubical Persistent Homology-Based Technique (CPHBT) that leverages persistence barcodes to identify significant topological features and reduce noise. The method selects filtration levels that preserve important features like loops and connected components. Applied to digit images, our method demonstrates superior performance, achieving a Peak Signal-to-Noise Ratio (PSNR) of 46.88 and a Structural Similarity Index Measure (SSIM) of 0.99, outperforming TV (PSNR: 21.52, SSIM: 0.9812) and NLM (PSNR: 22.09, SSIM: 0.9822). These results confirm that cubical persistent homology offers an effective solution for image denoising by balancing noise reduction and preserving critical topological features, thus enhancing overall image quality.

This study aims to analyze the financial performance of PT. Adaro Energy Tbk for the period 2021-2023 using the Index Time Series method. PT. Adaro Energy Tbk is an integrated energy company that focuses on coal mining. The data analyzed comes from the company's financial statements published on the Indonesia Stock Exchange. The research method is quantitative non-statistical with a purposive sampling technique to determine the sample. Data analysis techniques include Time Series calculations by looking at the financial performance values of the Liquidity, Solvency, and Profitability Ratios. The results of the analysis show that the financial performance of PT. Adaro Energy Tbk In the period 2021-2023, the company's Current Ratio increased from 2.08% in 2021 to 2.17% in 2022, before decreasing slightly to 2.01% in 2023. Despite the decline, the company still has sufficient liquidity. Debt to Equity Ratio dropped significantly from 83.47% in 2021 to 41.33% in 2023, signaling the end of management in reducing debt. Profitability through Net Profit Margin (NPM) increased from 23.27% in 2021 to 30.75% in 2022, but decreased to 25.22% in 2023. The results of this study provide stakeholders with insight into the dynamics of the company's financial performance, as well as provide a good foundation for making financial decisions in the future.

Dian Ekawati. Analysis of Accountability for Promotion of Civil Servant Ranks (Case Study of Application of SAPK BKN in BKD, South Sumatra Province). Accountability of Service Promotion for Civil Servants (Case Study of Application of SAPK BKN in BKD of South Sumatra Province) has basically been going well, but because there were some disturbances related to network problems and application system problems, so there were some things that were delayed in the management process. . It has not been added to the terms and files that are not appropriate so that many promotion files have not been approved and must be repaired. The supporting factor is the existence of technological advances that become service innovations, in the form of online applications, namely online SAPK in the management of promotions, it will provide convenience in the management of promotions. The inhibiting factor is that the existence of signal interference in several districts is a weak side of the online system that must be improved, as well as the requirements for completeness of files that have not been met by employees hampering the approval process for promotions.