The modulation of the structures and electronic properties of transition metal complexes by multi-ligands coordination has been widely exploited to enhance the catalytic?efficiency in organic transformations. Here, we present synthesis, characterization, and catalytic performance of Cu(II), Ni(II) and Pd(II) complexes of multidentate ligands- EDTA, 1,10-phenanthroline and?N-heterocyclic carbenes (NHCs). The Pd-NHC complex displayed the high catalytic activity for hydrogenation as well as?small-molecule activation, up to 450 h?¹ of turnover frequency (TOF) and total conversion of styrene to ethylbenzene at room temperature, and is higher efficient than Cu-EDTA (TOF =120 h?¹), Ni-Phen (TOF= 85 h?¹). The DFT calculation with Pd-NHC system showed that the small HOMO-LUMO gap (3.2 eV) and the short Pd–C bond (1.95 Å) of Pd-NHC,?which could facilitate the transfer of the growth species, contributed to the low energy of activation (28 kJ/mol). Mathematical?model also discussed its high catalytic activity (TOF/Ea = 16.07 h?¹·kJ?¹·mol) due to the strong ?-donor ligands and proper geometry. These findings underscore the influence of ligand architecture to secure transition states and modulate electron density in designing efficient sustainable catalysis that is?industrially relevant.

Skin diseases are health problems that require accurate diagnosis to evaluation and ultimately leading to treatment decisions. One of the crucial roles in the diagnostic process is medical imaging. Machine learning technology can assist in classifying skin diseases using image data and achieving high levels of accuracy in diagnosis. The purpose of this research is to review machine learning algorithms that can be utilized to develop image-based skin disease classification systems. The methodology employed is a Systematic Literature Review (SLR), which can be used to provide a comprehensive review of the application of machine learning in the classification of skin diseases. The literature search strategy was based on the Boolean technique, applied to the Scopus database. The selected articles were screened using predefined inclusion and exclusion criteria. The results indicate that the most used machine learning algorithm with achieved the highest classification accuracy is the Convolutional Neural Network (CNN).
Keywords - Skin Disease, Machine Learning, Classification, CNN.

Micro, Small, and Medium Enterprises (MSMEs) have a strategic contribution to economic equality and job creation, especially in rural areas. However, limitations in mastering digital technology, especially in marketing, are still a major challenge for MSMEs in Pepelegi Village, Sidoarjo. This community service activity aims to increase the digitalization capacity of MSMEs through training and mentoring in utilizing Google Maps as a promotional media. The method used in this study is Participatory Action Research (PAR), which includes observation, interviews, training, and evaluation. The location of the activity focused on the MSME Warkop Pojok SR in Pepelegi Village, involving the active participation of local business actors. The results of the activity showed an increase in participants' knowledge and skills in creating and managing business profiles on Google Maps. This has a positive impact on business visibility and the potential for increasing income. Digitalization through Google Maps not only makes it easier for consumers to find business locations, but also provides information regarding operating hours, customer reviews, and business contacts. Thus, this activity is expected to be a model for empowering MSMEs based on digital technology that can be replicated in other areas. The sustainability of the program can be strengthened through cross-sector collaboration to support the acceleration of MSME digital transformation.

The ease of investing in the digital era has driven Generation Z to dominate stock market participation, particularly in blue-chip stocks such as PT Bank Rakyat Indonesia Tbk (BBRI). However, stock price fluctuations influenced by macroeconomic factors, regulations, and global market sentiment make it difficult for investors to make accurate decisions. Decisions based on insufficient information pose a significant risk of loss, especially for novice investors. This study proposes a hybrid LSTM-XGBoost approach for predicting BBRI stock prices, combining the strengths of LSTM in capturing nonlinear time series patterns and XGBoost's effectiveness in reducing prediction errors. The model leverages both historical data and feature extraction outputs from the LSTM model. Future stock price values are then predicted by XGBoost using this combined dataset. The Hybrid LSTM XGBoost model outperforms the individual base models in terms of prediction accuracy, achieving an RMSE of 117.89, MAE of 92.45, and MAPE of 2.21%.

This study stems from the growing need to support university students' self-growth during early adulthood, a critical phase in preparing for independence and future careers. The Merdeka Belajar–Kampus Merdeka (MBKM) initiative, introduced by Indonesia’s Ministry of Education, provides diverse learning experiences beyond the classroom that are expected to foster personal development. The objective of this research is to evaluate the impact of the MBKM program on various dimensions of student self-development, including cognitive, emotional, and social growth. Employing a quantitative research method, data were gathered through surveys distributed to students who took part in the program. The questionnaire measured specific indicators of personal development. Results indicate that the MBKM program significantly and positively contributes to the self-development of participants. Students demonstrated enhanced critical thinking, emotional intelligence, and interpersonal as well as leadership skills. These findings suggest that experiential learning opportunities such as those offered by MBKM play a vital role in nurturing students' maturity and preparing them for professional challenges. As a result, universities are encouraged to embed similar programs within their curriculum to promote comprehensive student development..

A hoax or fake news on social media refers to information that is spread with the intent to mislead or confuse people, often without valid factual basis. This phenomenon has become increasingly prevalent, especially on platforms like Facebook, where information can spread quickly. Therefore, the way to recognize and combat hoaxes on social media, particularly Facebook, is by carefully checking the source of information, verifying facts through trustworthy websites, and being cautious of sensational headlines and images that seem relevant. Additionally, checking the date and time of the information is crucial to avoid falling for outdated or manipulated content. In this context, verification plays a vital role in helping users be more discerning when filtering news and preventing the spread of hoaxes. If we fail to recognize or combat hoax dissemination, the impact can be highly detrimental to individuals and society, leading to confusion, panic, and both material and psychological harm. Therefore, raising awareness and media literacy among the public is essential to reduce the negative impact of hoaxes, encouraging people to be more critical and responsible in sharing information.

With the rapid advancement of information technology, maintaining a stable and efficient computer network has become essential, particularly in educational settings. SMK Negeri 1 Surabaya encounters difficulties in managing its limited bandwidth because of the large number of users accessing the network simultaneously. This research focuses on applying bandwidth management through the Queue Tree technique combined with Per Connection Queuing (PCQ) on the school's computer network using Mikrotik hardware. The approach aims to enhance network performance by improving throughput, minimizing delays, and stabilizing jitter. Evaluation of Quality of Service (QoS) indicators—including throughput, packet loss, delay, and jitter—demonstrated favorable outcomes, with all metrics categorized as good. Findings confirm that using Queue Tree and PCQ methods contributes to fairer bandwidth distribution, mitigates network congestion, and improves overall network quality in the educational environment. This study offers a practical solution to support teaching and administrative processes by providing a more reliable and efficient network infrastructure.

The research purposes to assess the effectiveness of the receivables model in enhancing the quality of accounting information (QAF), analyze the relationship between this quality and tax revenue proceeds, and provide recommendations for improving the use of the receivables model in institutions to achieve better tax revenue outcomes. Additionally, it highlights the role of high-quality accounting information systems in increasing tax revenues by enhancing the relevance and reliability of accounting data, making it a vital tool for administrative decision-making that aligns with the demands of today's business environment. The study focuses on the General Authority for Taxes of Iraq for the period from 2015 to 2024 to apply the receivables model in measuring the QAF and its impact on boosting tax revenues. The study came to a number of conclusions, the most significant of which was that the two fundamental characteristics that enable various users to use accounting information for decision-making are relevance and reliability, and For the purpose of determining the general structure of the accounting information system in tax institutions and units in order to increase tax revenue collections, it is necessary to determine the type and nature of reports that require preparation to provide, display and communicate information to the entities and parties benefiting from it.

This study synthesized novel sulfadiazine-imine derivatives (A1 and A2) using a microwave-assisted method by reacting sulfadiazine with orsellinaldehyde and 2-hydroxy-5-methylbenzaldehyde. The synthesis involved the nucleophilic addition of sulfadiazine to the aldehyde groups, forming imine bonds efficiently under microwave irradiation within 15 minutes. The antibacterial efficacy of the synthesized derivatives was tested against Bacillus subtilis, Streptococcus pneumoniae, and Escherichia coli using agar well diffusion methods. The derivatives A1 and A2 exhibited significantly improved antibacterial activities compared to sulfadiazine, with A1 showing the highest efficacy. Specifically, inhibition zones for Bacillus subtilis reached 26 mm (A1 at 100 mg/mL) compared to 20 mm for sulfadiazine, and similar trends were observed against E. coli and S. pneumoniae. Furthermore, the cytotoxic activity against MCF-7 breast cancer cells revealed that both derivatives demonstrated dose-dependent cytotoxicity. For derivative A1, cell viability decreased to 19% at 320 ppm with an estimated IC50 between 40 and 80 ppm. Derivative A2 showed comparable cytotoxic behavior, with cell viability dropping to 12.6% at the highest concentration tested. The enhanced antibacterial and anticancer properties are attributed to increased lipophilicity and improved cellular penetration conferred by the imine functional group. This research highlights the potential of microwave-assisted derivatization of sulfadiazine to produce compounds with promising therapeutic applications.

The World Trade Organization (WTO) acts as the main institution in regulating the global trading system based on the principles of fairness and openness. However, in practice, protectionist challenges arising from large countries, such as in the trade conflict between the United States and China, hamper economic liberalization efforts and create instability in the global market. This study aims to analyze the effectiveness of the WTO's role in facing these protectionism challenges. The method used is normative juridical with a qualitative approach, which includes analysis of WTO regulations and policies, as well as a comparative approach to examine the impact of protectionism in several countries. Data were obtained through literature study of academic sources and relevant international legal documents. The results show that policy harmonization between the WTO and its member countries is needed to maintain the stability of the multilateral trading system. In addition, reforms to the WTO dispute settlement mechanism are crucial to make it more adaptive to global geopolitical dynamics. Developing countries are advised to improve competitiveness through market diversification and strengthening economic diplomacy to remain relevant in the changing international trade architecture.