During autumn/winter in 2016 – 2017 and 2020 – 2021, highly pathogenic avian influenza viruses (HPAIV) caused severe outbreaks in Germany and Europe. Multiple clade 2.3.4.4b H5 HPAI subtypes were responsible for increased mortality in wild birds and high mortality and massive losses in the poultry sector. To clarify putative entry sources and delineate interconnections between outbreaks in poultry holdings and wild birds, we applied whole-genome sequencing and phylodynamic analyses combined with the results of epidemiological outbreak investigations. Varying outbreak dynamics of the distinct reassortants allowed for the identification of individual, putatively wild bird-mediated entries into backyard holdings, several clusters comprising poultry holdings, local virus circulation for several weeks, direct farm-to-farm transmission and potential reassortment within a turkey holding with subsequent spill-over of the novel reassorted virus into the wild bird population. Whole-genome sequencing allowed for allowed for a unique high-resolution molecular epidemiology analysis of HPAIV H5Nx outbreaks, recommended to be used as a standard tool. The presented detailed account of the genetic, temporal and geographical characteristics of the recent German HPAI H5Nx situation emphasizes the role of poultry holdings as an important source of novel genetic variants and reassortants.

One in eight women will be diagnosed with breast cancer (BC) in their lifetime, resulting in over 2 million cases annually. BC is the most common cancer among women. Unfortunately, the etiology of majority of cases remains unknown. Recently, evidence has shown that the human microbiota plays an important role in health and disease. Intriguingly, studies have revealed the presence of microorganisms in human breast tissue, which was previously presumed to be sterile. Next-generation sequencing technologies have paved way for the investigation of breast microbiota, uncovering bacterial signatures that are associated with BC. Some of the bacterial species were found to possess pro-carcinogenic and/or anti-carcinogenic properties, suggesting that the breast microbiota has potentially crucial roles in maintenance of breast health. In this review, we summarize the recent findings on breast tissue microbiota and its interplay with BC. Bacterial signatures identified via next-generation sequencing as well as their impact on breast carcinogenesis and cancer therapies are reviewed. Correlation of breast tissue microbiota and other factors, such as geographical and racial differences, in BC is discussed. Additionally, we discuss the future directions of research on breast microbiota as well as its potential role in prevention, diagnosis and treatment of BC.

Targeted therapy indications for actionable variants in non-small cell lung cancer (NSCLC) have primarily been studied in Caucasian populations, with limited data on Latin American patients. This study utilized a 52-gene next-generation sequencing (NGS) panel to analyze 1,560 tumor biopsies from NSCLC patients in Chile, Brazil, and Peru. RNA sequencing reads, and DNA coverage were correlated to improve the detection of the actionable MET exon 14 skipping variant (METex14). The pathogenicity of MET variants of uncertain significance (VUS) was assessed using bioinformatic methods based on their predicted driver potential. The effects of the predicted driver VUS T992I and H1094Y on MET signaling activation, proliferation, and migration were evaluated in HEK293T, BEAS-2B, and H1993 cell lines. Subsequently, MET inhibitors were tested in 2D and 3D cell cultures, and drug affinity was determined using 3D structure simulations. The prevalence of MET variants in the South American cohort was 8%, and RNA-based diagnosis detected 27% more cases of METex14 than DNA-based methods. Notably, 20% of METex14 cases with RNA reads below the detection threshold were confirmed by DNA analysis. The novel actionable T992I and H1094Y variants induced proliferation and migration through c-Met/Akt signaling. Both variants showed sensitivity to crizotinib and savolitinib, but the H1094Y variant exhibited reduced sensitivity to capmatinib. These findings highlight the importance of RNA-based METex14 diagnosis and reveal the drug sensitivity profiles of novel actionable MET variants from an understudied patient population.

Cutaneous squamous cell carcinoma is a prevalent malignancy with a rising incidence and notably high mutational load. Exploring the genetic nuances of cSCC and investigating molecular approaches stands as a potential avenue for improving outcome in high-risk patients. This retrospective case-control study involved two cohorts of 14 patients (“discovery cohort”) and 12 patients (“validation cohort”) with cSCC located in head-neck anatomical district and diagnosed at pT2 stage. Overall, cases developed early local relapse of the disease whereas controls never re-lapsed during the entire follow-up period. Next generation sequencing (NGS) approach conducted on histological samples revealed that TP53 and CDKN2A were the most frequently mutated genes in our series. No specific mutation was identified as potential prognostic or therapeutic targets. Controls exhibited a tendency toward a higher mutational rate compared to cases. It is possible that an increased number of mutations could prompt the cSCC to expose more antigens, becoming more immunogenic and facilitating the recognition by the immune system. This could enhance and sustain immunological response, potentially preventing future recurrences.

Selection is a fundamental part of the plant breeding process, enabling the identification and development of varieties with desirable traits. Thanks to advances in genetics and biotechnology, the selection process has become more precise and efficient, resulting in faster breeding progress and better adaptation of crops to environmental challenges. Genetic parameters related to gene additivity and epistasis play a key role and can influence decisions on the suitability of breeding material. In this study, 188 rapeseed doubled haploid lines were assessed in field conditions for resistance to Leptosphaeria spp. Through next-generation sequencing, a total of 133,764 molecular markers (96,121 SilicoDArT and 37,643 SNP) were obtained. The similarity of the DH lines at the phenotypic and genetic levels was calculated. The results indicate that the similarity at the phenotypic level was markedly different than the similarity at the genetic level. Genetic parameters related to additive gene action effects and epistasis (double and triple) were calculated using two methods: based on phenotypic observations only and using molecular marker observations. All evaluated genetic parameters (additive, additive-additive and additive-additive-additive) were statistically significant for both estimation methods. The parameters associated with the interaction (double and triple) had opposite signs depending on the estimation method.

This paper represents a groundbreaking paradigm shift in network optimization. Departing from traditional static methodologies, this innovative approach harnesses the power of Generative Artificial Intelligence (AI) and Large Language Models (LLMs) to optimize cloud networks dynamically. By integrating advanced AI algorithms, this framework continuously adapts and evolves, ensuring optimal real-time performance. This dynamic optimization enhances efficiency and resilience, allowing cloud networks to adjust seamlessly to changing demands and conditions. Through the fusion of cutting-edge technology and adaptive intelligence, this approach heralds a new era in network optimization, empowering organizations to achieve unprecedent-ed levels of agility and scalability in their cloud infrastructures.

Sarcomas are a heterogenous group of tumours that commonly carry poor prognosis with limited therapeutic options. Adolescents and young adults (AYA) with sarcoma are a unique and under-studied patient population that have only achieved modest survival gains compared to other groups. We present our institutional experience of AYA with sarcoma who underwent comprehensive molecular profiling (CMP) using either large-panel targeted DNA sequencing or whole genome and transcriptome sequencing and evaluated the feasibility and clinical impact of this approach. Genomic variants detected were determined to be clinically relevant and actionable following the Molecular Tumour Board evaluation. Clinicians provided feedback regarding the utility of testing three months after reporting. Twenty-five patients who were recruited for CMP are included in this analysis. The median time from consent to final molecular report was 45 days (interquartile range 37-57). Potentially actionable variants were detected for 14 patients (56%) and new treatment recommendations were identified for 12 patients (48%). Pathogenic germline variants were identified in three patients (12%), and one patient had a change of diagnosis. Implementation for CMP for AYA with sarcoma is clinically valuable, feasible and should be increasingly integrated into routine clinical practice as technologies and turnaround times continue to improve.

The global COVID-19 pandemic claiming global spread continues to evolve, now to the verge of a third wave of outbreak possibly caused by the novel variants of concern of severe acute respiratory syndrome corona virus-2 (SARS-CoV-2). The test positivity rate (TPR) and case fatal-ity rate (CFR) have increased steeply in the second wave of COVID-19 compared to the first. From the example of Kerala, a state in southern India, positivity increased from 1.33% at the peak of wave one in 10th June 2020 to 13.45% during 10th June 2021 in the second wave of pandemic. SARS-CoV-2 is an enveloped single-stranded RNA virus. Angiotensin-Converting Enzyme-2 (ACE-2) is a trans membrane surface protein present on multiple types of cells in the human body to which the viral spike protein attaches. Genetic variations in the SARS-CoV-2 and ACE2 receptor can affect the transmission, clinical manifestations, mortality and the efficacy of drugs and vaccines for COVID-19. Mutations are the primary cause of genetic variations. Given the high TPR and CFR, it is necessary to understand the variations of SARS-CoV-2 and cellular receptors of SARS-CoV-2 at the molecular level. In this review, we summarize the impact of genetic and ep-igenetic variations in determining COVID-19 pathogenesis and disease outcome.

Homologous recombination deficiency (HRD) has become an important prognostic and predictive biomarker for patients with high-grade serous ovarian cancer who may benefit from poly-ADP ribose polymerase inhibitors (PARPi) and platinum-based therapies. HRD testing provides relevant information to personalize patients’ treatment options, and it has been progressively incorporated in diagnostic laboratories. In this study, we evaluated the feasibility of in-house HRD testing deployable in a diagnostic clinical setting, comparing results from two commercially available next-generation sequencing (NGS)-based tumor tests (SOPHiA DDMTM HRD Solution and AmoyDx HRD Focus Panel) with the reference assay from Myriad MyChoice CDx. A total of 85 ovarian cancer samples were subjected to HRD testing. An overall strong correlation was observed across the three assays evaluated, regardless of the different underlying methods employed to assess genomic instability, with the highest pairwise correlation between Myriad and SOPHiA (R=0.87, p-value=3.39x10-19). The comparison of the assigned HRD status to the reference Myriad’s test revealed a positive predictive value (PPV) and negative predictive value (NPV) of 90.9% and 96.3% for SOPHiA’s test, while AmoyDx’s test achieved 75% PPV and 100% NPV. This is the largest HRD testing evaluation using different methodologies and provides a clear picture of the robustness of NGS-based tests currently offered in the market. Our data show that the implementation of in-house HRD testing in diagnostic laboratories is technically feasible and it can be reliably performed with commercial assays. Also, the turnaround time is compatible with clinical needs, being an ideal alternative to offer to a broader number of patients, while still maintaining high-quality standards at more accessible price tiers.

Studies have previously described the occurrence of multidrug-resistant (MDR) Escherichia coli in human and veterinary medical settings, livestock, and, to a lesser extent, in the environment and food. While they mostly analyzed foodborne E. coli regarding phenotypic and sometimes genotypic antibiotic resistance and basic phylogenetic classification, we have limited understanding of the in vitro and in vivo virulence characteristics and global phylogenetic contexts of these bacteria. Here, we investigated an E. coli strain (PBIO3502) isolated from a pork sausage in Germany in 2021 in-depth. Whole-genome sequence analysis revealed sequence type (ST)58, which is an emerging international high-risk clonal lineage. In addition to its MDR phenotype that mostly matched the genotype, PBIO3502 demonstrated pronounced virulence features, including in vitro biofilm formation, siderophore secretion, serum resilience, and in vivo mortality in Galleria mellonella larvae. Along with the genomic analysis indicating close phylogenetic relatedness of our strain with publicly available, clinically relevant representatives of the same ST, these results suggest the zoonotic and pathogenic character of PBIO3502 with the potential to cause infection in humans and animals. Also, our study highlights the necessity of the One Health approach while integrating human, animal, and environmental health, as well as the role of meat products and food chains in the putative transmission of MDR pathogens.

Snakebite envenoming represents a critical global health challenge, particularly prevalent in regions with limited access to healthcare resources, where venomous snakes pose a significant threat to human populations. Antivenom therapy which mainly rely on antibody production by immunization of large animals with venom components is labour intensive, time consuming and associated with various ethical concerns. Consequently, access to quality and affordable antivenom remains limited in many affected regions with high mortality associated with snakebites. In traditional medicine, many plant species have been ethnobotanically reported for their antivenom properties and are used to neutralize animal toxins. Ribosome-inactivating proteins (RIPs) are a diverse group of toxins found in various organisms, including plants that possess the ability to inhibit protein synthesis by irreversibly damaging the ribosomes. Even though considered to be harmful, the biological role of RIPs has gained increasing attention in recent years due to their potential therapeutic implications. With these insights, this review underscores the potential of RIPs as promising candidates for adjunct treatments in snakebite management strategies. In silico analysis by molecular docking of RIPs with major snake venom proteins resulted in effective binding and shows the interface residues involved in the interaction. This integrative approach enhances our understanding of snakebite pathophysiology, accelerating the development of novel next-generation antivenom therapies that are safer and more effective.

Viral integration within the host genome plays a pivotal role in carcinogenesis. Various disruptive mechanisms are involved, leading to genomic instability, mutations, and DNA damage. With next-generation sequencing (NGS), we can now precisely identify viral and host genomic breakpoints and chimeric sequences, which are useful for integration site analysis. In this study, we evaluated a commercial hybrid-capture NGS panel specifically designed for detecting three key viruses: HPV, HBV, and HIV-1. We also tested workflows for Viral Hybrid Capture (VHC) and Viral Integration Site (VIS) analysis, leveraging customized viral databases in CLC Microbial Ge-nomics. By analyzing sequenced data from virally infected cancer cell lines (including SiHa, HeLa, CaSki, C-33A, DoTc2, 2A3, SCC154 for HPV; 3B2, SNU-182 for HBV; and ACH-2 for HIV-1), we precisely pinpointed viral integration sites. The workflow also highlighted disrupted and neigh-boring human genes that may play a crucial role in tumor development. Our results included in-formative virus-host read mappings, genomic breakpoints, and integration circular plots. These visual representations enhance our understanding of the integration process. In conclusion, our seamless end-to-end workflow bridges the gap in understanding viral contributions to cancer development, paving the way for improved diagnostics and treatment strategies.

Background. Gene rearrangements affecting KMT2A are frequent in acute myeloid leukemia (AML) and are often associated with a poor prognosis. KMT2A gene fusions are often detected by chromosome banding analysis and confirmed by fluorescence in-situ hybridization. However, small intragenic insertions, termed KMT2A partial tandem duplication (PTD), are particularly challenging to detect using standard molecular and cytogenetic approaches. Methods. We have validated the use of a custom hybrid capture based next-generation sequencing (NGS) panel for comprehensive profiling of AML patients seen at our institution. This NGS panel targets the entire consensus coding DNA sequence of KMT2A; to deduce the presence of a PTD we used the relative ratio of KMT2A exons coverage. We sought to corroborate the KMT2A-PTD NGS results using (1) multiplex-ligation probe amplification (MLPA) and (2) optical genome mapping (OGM). Results. We analyzed 932 AML cases and identified 41 individuals harboring a KMT2A-PTD. MLPA, NGS and OGM confirmed the presence of a KMT2A-PTD in 22 of the cases analyzed where orthogonal testing was possible. Two false positive PTD calls by NGS could be explained by the presence of cryptic structural variants impacting KMT2A and interfering with PTD analysis. OGM revealed the nature of these previously undetected gene rearrangements in KMT2A, while MLPA yielded in-conclusive results. MLPA analysis for KMT2A-PTD is limited to exon 4, whereas NGS and OGM resolved KMT2A-PTD sizes and copy number levels. Conclusion. KMT2A-PTDs are complex gene rearrangements that cannot be fully ascertained using a single genomic platform. MLPA, NGS panels and OGM are complementary technologies applied in standard of care testing for AML patients. MLPA and NGS panels are designed for targeted copy number analysis; however, our results show that integration of concurrent genomic alterations is needed for accurate KMT2A-PTD identification. Unbalanced chromosomal rearrangements overlapping with KMT2A can interfere with the diagnostic sensitivity and specificity of copy number based KMT2A-PTD detection methodologies.

Liquid biopsies are an integral part of the diagnosis of cancer. Here, we have extended previous validation studies of a new targeted NGS panel to include the detection of copy number amplifications (CNAs), fusions, and exon skipping variants. Detection of these gene classes included specimens from clinical and healthy donors and cell lines (fusions: ROS1, EML4-ALK, NTRK1), (exon skipping: MET exon 14), and (CNAs: HER2, CDK6, EGFR, MYC and MET). The limit of detection (LOD) for fusion/skipping was 42 copies (QC threshold = 3 copies) and was verified using 3 additional fusion/skipping variants. LOD for CNAs was 1.40-fold-change (QC threshold = 1.15-fold change) and was verified with 3 additional CNAs. In precision studies, all fusion/skipping variants were detected in all runs and all days of testing (n=18/18; 100%); average CV for repeatability was 20.5% (range 8.7% - 34.8%), and for reproducibility was 20.8% (range 15.7% - 30.5%). For CNAs, 28/29 (96.6%) copy gains were detected. For CNAs, average CV of 1.85% (range 0% to 5.49%) for repeatability and 6.59% (range 1.65% to 9.22%) for reproducibility. The test panel meets the criteria for being highly sensitive and specific and extends its utility for the detection of clinically relevant variants in cancer.

The field of mitochondrial genomics has advanced rapidly and revolutionized disciplines from molecular anthropology, population genetics, to medical/oncogenetics. However, mtDNA next-generation sequencing (NGS) analysis for matrilineal haplotyping and phylogeographic inference remains hindered by the lack of a consolidated, mitogenome database and efficient bioinformatics pipeline. To address this, we developed a customized human mitogenome database (hMITO DB) embedded in a CLC Genomics workflow for read mapping, variant analysis, haplotyping, and geo-mapping. The database was constructed from 4,286 mitogenomes. The macro-haplogroup (A to Z) distribution and representative phylogenetic tree were found consistent with published literature. The hMITO DB automated workflow was tested using mtDNA-NGS sequences derived from Pap smears and cervical cancer cell lines. The auto-generated read mapping, variants track, and table of haplotypes and geo-origins were completed in 15 min for 47 samples. The mtDNA workflow proved to be a rapid, efficient and accurate means of sequence analysis for translational mitogenomics.

Fanconi anemia (FA) patients display an exacerbated risk of oral squamous cell carcinoma (OSCC) and precursor lesions at young ages, mainly at the oral cavity. As patients have defects in DNA repair mechanisms, standard-of-care treatments to OSCC such as radiotherapy and chemotherapy give rise to severe toxicities. New methods for early diagnosis are urgently necessary to allow treatments in early disease stages and achieve better clinical outcomes. We have conducted a prospective, longitudinal study whereby liquid biopsies from sixteen lesion/tumor-free patients were analyzed for the presence of mutations in cancer genes. DNA from saliva and plasma were sequentially collected and deep-sequenced, and the clinical evolution followed during a median time of around 2 years. In 9/16 FA patients we detected mutations in cancer genes (mainly TP53) with molecular allele frequencies (MAF) down to 0.07 %. Importantly, all patients having mutations and clinical follow-up data after mutation detection (n=6) developed oral precursor lesions or OSCC. Lead-time between mutation detection and tumor diagnosis ranged from 23 to 630 days. Strikingly, FA patients without mutations display significantly lower risk of developing precursor lesions or OSCC. Therefore, our diagnostic approach could help to stratify FA patients into risk groups, which would allow closer surveillance for OSCC or precursor lesions.

The study of cell-free DNA (cfDNA) and other peripheral blood components (known as “liquid biopsies”) is promising and has been investigated especially in solid tumors. Nevertheless, it is increasingly showing greater utility in the diagnosis, prognosis, and response to treatment of hematological malignancies; in the future, it could prevent invasive techniques, such as bone marrow (BM) biopsy. Most of the studies about this topic have been focused on B cell lymphoid malignancies; some of them have shown that cfDNA can be used as a novel way for diagnosis and minimal residual monitoring in B cell lymphomas, using techniques such as next-generation sequencing (NGS). In myelodysplastic syndromes, multiple myeloma, or chronic lymphocytic leukemia, liquid biopsies may allow for an interesting genomic representation of the tumor clones affecting different lesions (spatial heterogeneity). In acute leukemias, it can be helpful in the monitoring of early treatment response and the prediction of treatment failure. In chronic lymphocytic leukemia, the evaluation of cfDNA permits the definition of clonal evolution and drug resistance in real-time. However, there are limitations such as the difficulty in obtaining sufficient circulating tumor DNA for achieving a high sensitivity to assess minimal residual disease or the lack of standardization of the method and clinical studies to confirm its prognostic impact. This review focuses on clinical applications of cfDNA on minimal residual disease in hematological malignancies.

The rising importance of Big Data in modern information analysis is supported by vast quantities of user data, but it is only possible to collect sufficient data for all tasks within certain data-gathering contexts. There are many cases where a domain is too novel, too niche, or too sparsely collected to adequately support Big Data tasks. To remedy this, we have created ADG Engine that allows for the generation of additional data that follows the trends and patterns of the data that’s already been collected. Using a database structure that tracks users across different activity types, ADG Engine can use all available information to maximize the authenticity of the generated data. Our efforts are particularly geared towards data analytics by identifying abnormalities in the data and allowing the user to generate normal and abnormal data at custom ratios. In situations where it would be impractical or impossible to expand the available dataset by collecting more data, it can still be possible to move forward with algorithmically expanded data datasets.

Flammability was evaluated and the results showed the values of critical flux at extinguishment (CFE) as 49.5kW/㎡ on average, total heat release (Qt) as 0.21MJ on average, and peak heat release rate (Qp) as 0.66kW on average, which satisfied all of the thresholds presented by the IMO. Therefore, flame-retardant plywood, of which the flame-retardant performance was evaluated, was determined to be an applicable finishing material that satisfies fire safety standards.

Pathogenicity is unknown for the majority of human gene variants. For prioritization of sequenced somatic and germline mutation variants, in silico approaches can be utilized. In this study, 84 million non-synonymous Single Nucleotide Variants (SNVs) in the human coding genome were annotated using consensus Variant Effect Prediction (cVEP) method. An algorithm, implemented as a stacked ensemble of supervised learners, performed combination of the 39 functional, conservation mutation impact scores from dbNSFP4.0. Adding gene indispensability score, accounting for differences in the pathogenicities of the variants in the essential and the mutation-tolerant genes, improved the predictions. For each SNV the consensus combination gives either a continuous-value pathogenicity score, or a categorical score in five classes: pathogenic, likely pathogenic, uncertain significance, likely benign, benign. The provided class database is aimed for direct use in clinical practice. The trained prediction models were 5-fold cross-validated on the evidence-based categorical annotations from the ClinVar database. The rankings of the scores based on their ability to predict pathogenicity were obtained. A two-step strategy using the rankings, scores and class annotations is suggested for filtering and prioritization of the human exome mutations in clinical and biological applications of NGS technology.

The field of 3D model generation has become essential across various industries, including gaming, virtual and augmented reality (VR/AR), architecture, and medical imaging. Traditionally reliant on manual efforts, 3D content creation is now being transformed by deep generative models, enabling more efficient, scalable, and dynamic generation of complex shapes and environments. This survey provides a comprehensive review of key backbone architectures used for 3D generation, including autoencoders, variational autoencoders (VAEs), generative adversarial networks (GANs), autoregressive models, diffusion models, normalizing flows, attention-based models, CLIP-guided models, and procedural generation techniques. We explore each model’s role in 3D generation, highlighting their strengths—such as the precision of VAEs, the realism of GANs, the stability of diffusion models, and the scalability of procedural methods—alongside their limitations, such as training instability, high computational costs, and the difficulty in handling multi-modal data. Additionally, we discuss the increasing relevance of attention-enhanced models and the integration of text-based CLIP supervision for improved semantic alignment in 3D outputs. The survey concludes with an analysis of open challenges, including balancing efficiency with expressiveness, managing training complexity, and addressing dataset limitations. It also identifies future research directions, such as few-shot learning, hybrid architectures, and neural-symbolic approaches, which promise to advance the field by improving the generalization and versatility of 3D generation models. This paper aims to guide researchers and practitioners in navigating the evolving landscape of 3D generative methods and inspire new innovations in the creation of realistic, high-quality 3D content.

this paper we present approaches for generating random numbers along with potential applications. Rather than trying to provide extensive coverage of several techniques or algorithms that have appeared in the scientific literature, we focus on some representative approaches presenting their workings and properties in detail. Our goal is to delineate their strengths and weaknesses as well as their potential application domains so as the reader can judge what would be the best approach for the application in hand, possibly a combination of the available approaches. For instance, a physical source of randomness can be used for the initial seed, then suitable preprocessing can enhance its randomness and then the output of the preprocessing can feed different types of generators, e.g. a linear congruential generator, a cryptographically secure one and one based on the combination of one way hash functions and shared key cryptoalgorithms in various modes of operation. Then, if desired, the outputs of the different generators can be combined giving the final random sequence. Moreover, we present a set of practical randomness tests which can be applied on the outputs of random number generators in order to assess their randomness characteristics. In order to demonstrate the importance of unpredictable random sequences, we present an application of cryptographically secure generators in domains where unpredictability is one of the major requirements, i.e. eLotteries and cryptographic key generation.

Background : The current corruption case in Indonesia are severely problematic since it is normalized by society. As a response, the current agenda of the Indonesian government to tackle it is by raising awareness of youth towards Indonesia’s corruption case. Aim : This study aims to knowing the importance of anti-corruption education and its applications among students. Method : The methodology of this study is the use of a qualitative survey on Faculty of Pharmacy students and interviews with representatives of students of the Faculty of Pharmacy. The population of this study is students of Airlangga University. The sample of this study is 52 students of class C the Faculty of Pharmacy Result : Students have awareness about corruption and even proposed that the current anti-corruption education is not enough to tackle corruption. Recommendation : The researcher suggests that students can be brave to campaign about the importance of anti-corruption education to the public. Limitation : The limitation of this study is that it is difficult to conduct research because in the online pandemic era, this results in delays in the research process.

Pancasila is the identity of the Indonesian nation, which means that Pancasila is a national characteristic that cannot be found in any country. Pancasila points are taken from the noble values of the Indonesian state. These principles contain culture, customs, and national character known as manners, cooperation, and high nationalism. As the successor to Indonesian culture, the young generation must be able to preserve and develop Pancasila. In this era of globalization, there are many challenges for the younger generation in preserving Pancasila. This study aims to provide an overview of the decline of Pancasila identity among the younger generation due to the impact of globalization. The research question that has been determined is how the practice behaviour of the younger generation towards the implementation of Pancasila, the solutions and efforts that all Indonesian people can make regarding the crisis of Pancasila identity in the era of globalization. This research method uses a literature review. The research sources were conducted from the latest journals and books published in 2019 until 2021 related to the theme of Pancasila and the millennial generation. The results showed that globalization has greatly facilitated foreign cultures to enter Indonesia. The younger generations use this foreign culture as a lifestyle without filtering it, whether it contains Pancasila values or not. This causes Pancasila has been forgotten and no longer applied in their everyday life. Young people can make various efforts to preserve the national identity, namely Pancasila. Apart from the role of the young generation itself, Indonesian society can also play an essential role in supporting the younger generation to continue implementing Pancasila in their lives. By conducting this research, it is hoped that Indonesia's young generation can understand, select, and stay away from the negative impacts of globalization that enter Indonesia freely. The solution that can be given from this research is that the younger generation must love their own culture more and practice every principle in Pancasila. Parents, educational institutions, and the government must also support the younger generation to maintain national identity, Pancasila values. The limitation of this research is that the researcher did not conduct any primary data, which means the data were not directly from the source. This causes the accuracy of this study to be less guaranteed. The recommendation that the researchers can give for further research is to obtain primary data to ensure accuracy and explore research topics regarding the fading of Pancasila identity in the younger generation in this era of globalization.

Technological developments, as a result of globalization, indirectly affect human lifestyles, one of which is the Indonesian people. In a situation of change towards the Industrial Revolution 4.0, Pancasila has a role as a guide for the Indonesian people to be what by the noble values ​​of the nation. However, the role of the millennial generation in maintaining these noble values ​​is important considering that they are the spearhead of the country's development. From this statement, this study aims to identify and formulate steps to maintain the noble values ​​of Pancasila among the millennial generation so that it does not eliminate national identity and understand the urgency of implementing the Pancasila lifestyle in the era of globalization. By using the literature review method, it can be seen that "grounding" and strengthening the essence of Pancasila values ​​can be a solution so that the existence of Pancasila continues to shine. The Pancasila lifestyle deserves to be implemented because it regulates all behaviors of speech, thought, behavior, action, and good manners so that it must be expressed in the life of the nation and state as well as our everyday life.

As the international community attaches importance to environmental and climate issues, carbon dioxide emissions in various countries have been subject to constraints and limits. Carbon trading market, as a market tool to reduce greenhouse gas emissions, has gone through a development process from a pilot carbon market to a national carbon market in China. At present, the industries included in the national carbon market are mainly the electric power industry, and the carbon emissions of the electric power industry account for about 40% of the national carbon emissions. According to the construction history of foreign carbon markets, China's future carbon quota allocation will gradually transition from free allocation to auction allocation,the auction mechanism will bring a heavy economic burden to the electric power industry, especially the thermal power generation industry. Therefore, this study takes Guangdong Province as an example to optimize the power generation mix with the objective of minimizing the total economic cost after the innovative introduction of the carbon quota auction mechanism and constructs an optimization model of the power generation mix based on the auction ratio by comprehensively applying the system dynamics model and the multi-objective linear programming model and systematically researches the power generation structure under different auction ratios with the time scale of months and quantitatively evaluates the economic inputs needed to reduce the greenhouse gas emissions.The results of the study show that after comprehensively comparing the total economic cost, renewable energy development and carbon emissions, it is the most scientific and reasonable to set the auction ratio of carbon allowances at 20%, which achieves the best level of economic and environmental benefits.

Paraphrase Generation is one of the most important and challenging tasks in the field of Natural Language Generation. The paraphrasing techniques help to identify or to extract/generate phrases/sentences conveying the similar meaning. The paraphrasing task can be bifurcated into two sub-tasks namely, Paraphrase Identification (PI) and Paraphrase Generation (PG). Most of the existing proposed state-of-the-art systems have the potential to solve only one problem at a time. This paper proposes a light-weight unified model that can simultaneously classify whether given pair of sentences are paraphrases of each other and the model can also generate multiple paraphrases given an input sentence. Paraphrase Generation module aims to generate fluent and semantically similar paraphrases and the Paraphrase Identification systemaims to classify whether sentences pair are paraphrases of each other or not. The proposed approach uses an amalgamation of data sampling or data variety with a granular fine-tuned Text-To-Text Transfer Transformer (T5) model. This paper proposes a unified approach which aims to solve the problems of Paraphrase Identification and generation by using carefully selected data-points and a fine-tuned T5 model. The highlight of this study is that the same light-weight model trained by keeping the objective of Paraphrase Generation can also be used for solving the Paraphrase Identification task. Hence, the proposed system is light-weight in terms of the model’s size along with the data used to train the model which facilitates the quick learning of the model without having to compromise with the results. The proposed system is then evaluated against the popular evaluation metrics like BLEU (BiLingual Evaluation Understudy):, ROUGE (Recall-Oriented Understudy for Gisting Evaluation), METEOR, WER (Word Error Rate), and GLEU (Google-BLEU) for Paraphrase Generation and classification metrics like accuracy, precision, recall and F1-score for Paraphrase Identification system. The proposed model achieves state-of-the-art results on both the tasks of Paraphrase Identification and paraphrase Generation.

This research offers an extensive analysis of a family of prime-generating polynomials \( P_k(n) = n^2 - (2k - 79)n + [41 + (k - 39)(k - 40)] \), designed to generate prime numbers for integer values of \( n \) up to \( k \) where \( k \in \{1, 2, \ldots, 80\} \). We elaborate on mathematical clarity, consistency in proofs, error bounds, prime repetition analysis, and cryptographic implications. This study provides in-depth inductive proofs, graphical error bounds analysis, prime repetition statistics, and cryptographic security assessments, offering novel insights and future directions for research in number theory and secure prime generation.

The raw material purchasing (RMP) problem is to determine the purchasing quantities of raw materials in given periods or cycles. Raw material purchasing optimization is crucial for large-scale steel plants because it is closely related to the supply of raw materials and cost savings. The raw material purchasing of large-scale steel enterprises is characterized by many varieties, large quantities, and high costs. The RMP objective is to minimize the total purchasing cost consisting of the price of raw materials, purchasing set-up costs, and inventory costs and meet product demand. We present a mixed integer linear programming (MILP) model and a column generation (CG) solution for the resulting optimization problem. The column generation algorithm is the generalization of the branch & bound algorithm with solving the linear programming (LP) relaxation of MILP using column generation (CG), and its advantage is to handle large-sized MILPs. Experimental results show the effectiveness and efficiency of the solution.

We discuss a formalism which allows for the calculation of a higher harmonic current response to an applied electric field for disordered superconducting systems described on the basis of tight-binding models. The theory is based on an expansion of the density matrix in powers of the field amplitudes where we solve the equation of motion for the individual components. This allows the evaluation of higher order response functions on significantly larger lattices than one can achieve with a previously used approach, which is based on a direct temporal integration of the equation of motion for the complete density matrix. In case of small lattices, where both methods can be applied by including also the contribution of collective modes, we demonstrate the agreement of the corresponding results.

Asphaltene is a component of crude oil that, if mishandled, may create substantial transportation and industrial concerns. It is a solid by nature, with a diverse set of molecular compositions. Furthermore, the oil industry has identified emulsion generation and clogging precipitate as key concerns. The emulsified water droplets in the process are just what give the raw oil its increased viscosity; hence, power is squandered to transport the oil around. The information has been separated into sections based on simulation, solubilization, agglomeration, precipitation, and evidence of model chemicals in the literature. We have included research that investigates the bulk rheology and interfacial rheology of asphaltene to describe its interfacial activity more clearly.

Introduction: Industrial revolution 4.0 in the current era of globalization plays an active role in the modern era in Indonesia. The influence of increasingly advanced technology affects the millennial generation as the nation's next generation to make Pancasila the basis of the State or the foundation of a State. In addition, the millennial generation has begun to shift the guidelines of life from Pancasila so that the appreciation of Pancasila values is starting to disappear along with the development of the times and in line with the progress of Science and Technology (IPTEK). If this continues, then the values of Pancasila will be replaced by a new ideology that will cause behavioural changes in society. Purpose: this study aims to provide the proper application to millennial life using Pancasila values, knowing the behaviour of the millennial generation towards the implementation of Pancasila values, and making Pancasila a way of life. Methods: the method used in this research is Literature Review and takes sources from journals published in 2019 to 2021 related to the Pancasila theme and the millennial generation. Results: the study results show that with the existence of Pancasila as the ideology and principle of the State, it is expected to strengthen the attitude and nature of the community in accepting things outside the norms that exist in Indonesia. In addition, the younger generation is expected to be able to apply Pancasila values along with the development of science and technology in the modern era and current globalization. The research question is how to make the millennial generation implement Pancasila in life in the contemporary period and what solutions can continue to instil Pancasila values ​​in the 4.0 industrial revolution. Recommendation: the conclusion drawn is that Pancasila can become a unifying tool for the nation if the current generation or millennial generation still desires to maintain its values ​​, even though they are in a modern era and many influences from its outside cultures. Limitation: the limitation of this research is not getting all the data directly from the source. The researcher recommends using primary data for the following study to ensure its truth and accuracy better because the researcher directly obtains it.

This project aims to create a virtual assistant with AI integration to improve Siemens Energy's internal processes. Using cloud-based technologies, microservice architecture, and large language models (LLMs), the project seeks to create a reliable, effective, and user-friendly assistant customized to Siemens Energy's requirements. The first significant business difficulty identified by the study was the time engineers had to spend looking for information in large volumes of company papers. The proposed virtual assistant responds with precision and context awareness to optimize productivity. The assistant uses a microservice architecture to guarantee scalability, flexibility, and integration for various use scenarios. Tasks like document retrieval, translation, summarization, and comparison can now be handled effectively. Utilizing Amazon Web Services (AWS) for cost-effectiveness and scalability, the backend is cloud-deployed, backed by a frontend created for natural user interaction. To increase precision and relevance, the system uses cutting-edge AI, such as vector databases and Retrieval Augmented Generation (RAG). The assistant expedites document management procedures, improves data accessibility, and reduces search time. The results highlight how it may enhance workflow efficiency for Siemens Energy engineers and how flexible it can be for future AI-driven applications.

While digital libraries offer essential benefits for the digital age such as constant availability and accessibility, they often fail in providing a similarly enjoyable browsing experience that benefits engagement and serendipitous exploration inherent to traditional library experiences. In this paper, we propose a virtual reality (VR) library environment that emulates a traditional library promoting an engaging browsing experience that benefits browsing enjoyment and serendipity. The implemented system uses procedural content generation to dynamically generate a library-like environment, wherein each room corresponds to a specific category of the digital library. To assess the usability of the implemented system, we conducted an A/B study comparing the VR environment with the web interface of Project Gutenberg. The results of the study suggest that while the physical demand for using the VR environment is higher, the system can significantly benefit the browsing experience and entice users’ curiosity. Furthermore, the study suggests that the system can increase user engagement and users generally felt that using the system was rewarding.

Advanced lithography requires highly sensitive photoresists to improve lithographic efficiency, and it is critical yet challenging to develop high-sensitivity photoresists and imaging strategies. Here, we report a novel strategy for ultra-high sensitivity using hexafluoroisopropanol (HFIP)-containing fluoropolymer photoresists. The incorporation of HFIP, with its strong electrophilic property and electron-withdrawing effect of the fluorine atoms, significantly increases the acidity of the photoresist after exposure, enabling imaging without conventional photoacid generators (PAGs). The HFIP-containing photoresist has been evaluated by electron beam lithography to achieve a trench of ~40 nm at an extremely low dose of 3 μC/cm2, which shows sensitivity enhancement by ~10 times to the commercial PAGs involved system, revealing its high-sensitivity and high-resolution features. Our results demonstrate a new type of PAGs and a novel approach to higher-performance imaging beyond conventional photoresist performance tuning.

Given the imperative of security, sustainability of supply, strategic considerations, and energy independence, there is a widely acknowledged need to persist in utilizing coal as the primary fuel for electricity generation in power plants. However, in order to combat the rising levels of CO2 in the atmosphere, it is crucial to advance the development of carbon capture and storage (CCS) technologies that enable fossil fuel power plants to achieve zero emissions. These technologies play a pivotal role in capturing and effectively storing CO2, thus ensuring that coal-based power generation can continue while significantly reducing its environmental impact. By implementing CCS solutions, fossil fuel power plants can transition towards a more sustainable and environmentally friendly energy future.The utilization of chemical solvents for CO2 absorption, coupled with long-term storage, presents an intriguing and commercially viable technology for CO2 capture. However, the significant energy demands of the solvent regeneration process necessitate optimization, particularly in large-scale power plants. While the current cost of CO2 capture stands at approximately #55,000.00(Naira) per ton of CO2, the objective is to reduce this cost to below #25,000.00(Naira) per ton of CO2. This reduction in cost is essential to ensure the economic feasibility and widespread adoption of CO2 capture technologies in power generation.This research paper explores various approaches to address the energy demands associated with amine scrubbing integration in a commercial power plant. It provides a comprehensive analysis, both technically and economically, of the performance of these different approaches. While some of the proposed schemes may result in minor efficiency reductions, the key objective is to calculate the specific cost per ton of CO2 captured. The primary focus is on identifying the most suitable configuration to implement large-scale, cost-effective schemes that can serve as a foundation for CO2 capture demonstration projects. By determining the optimal configuration, this research aims to pave the way for the successful implementation of efficient and economically viable CO2 capture technologies in the power generation sector.

This study investigates the impact of narrow-band terahertz pulses on the ferroelectric order parameter in Ba0.8Sr0.2TiO3 films on various substrates. The 375 nm thick BST film on an MgO (001) substrate exhibits enhanced THz-induced second harmonic generation when excited by THz pulses with a central frequency of 1.6 THz, due to the resonant excitation of the soft phonon mode. Conversely, the BST film on a Si (001) substrate shows no enhancement, due to its polycrystalline state. The 800 nm thick BST film on an MgO (111) substrate demonstrates maximum of second harmonic generation signal when excited by THz pulses at 1.8 THz, that is also very close to it`s soft mode frequency (1.9 THz). Notably, the frequency spectrum of the BST/MgO (111) film reveals peaks at both the fundamental and doubled frequencies, highlighting the quadratic dependence of SHG intensity on the THz pulse's electric field strength.

Metal halide perovskites (MHPs), in particular lead-based halide perovskites have earned a recognized fame in several fields for their outstanding optoelectronics properties including direct generation of free charge carriers, optimal ambipolar charge carrier transport properties, high absorption coefficient, point-defect tolerance, and compositional versatility. Nowadays, this class of materials represents a real and promising alternative of silica for the photovoltaic technologies. This worthy success led to a growing interest in the exploration of MHP materials in other hot research fields such as the solar-driven photocatalytic water splitting towards hydrogen production, CO2 reduction to CO and CH4, useful organic reactions such as organic synthesis (formation of C-C, C-N, and C-O bonds), and pollutants and dye degradation. Nevertheless, many of these perovskite materials showed air and moisture instability problems that considerably hinder their practical application for photocatalytic water splitting and photodegradation of CO2. Moreover, if the chemical instability is a problem that can be in part mitigated by the optimization of the lattice structure, the presence of lead represents a real problem for the practical application of these type of materials in commercial devices. To successfully overcome this problem, lead-free metal halide perovskites (LFMHPs) have gained increasing interest thanks to their promising optoelectronic properties, comparable to lead-based materials, and their eco-friendly nature. Among all the lead-free perovskite alternatives, this mini review will focus the attention on the bismuth-based perovskites and perovskite derivatives with specific focus on solar-driven photocatalysis application for H2 evolution.

The conversion of methane into chemicals is of interest to achieve a decarbonized future. Fuel cells are electrochemical devices commonly used to obtain electrical energy, but can be utilized either for chemicals production or both energy and chemicals cogeneration. In this work, the partial oxidation of methane in fuel cells for electricity generation and valuable chemicals production at the same time is reviewed. For this purpose, different types of methane-fed fuel cells, both low temperature fuel cells, such as PEMFCs and AAEMFCs, and high temperature fuel cells, such as SOFCs, have been used. Also if few works have been devoted to this topic, the promising results drive the development of fuel cells using methane as the source for the cogeneration of power and valuable chemicals.

Background: In the current era of globalization, the Indonesian government's problem today is the weakening of nationalism and patriotism among the millennial generation. The large number of foreign cultures that have entered Indonesia has caused a sense of nationalism and patriotism. In addition, Indonesia is also facing the problem of spreading the Covid-19 virus. During the pandemic, various policies set by the government received protests from some circles because they felt their freedom was restricted. Therefore, the awareness of millennial generation nationalism is needed, especially during the Covid-19 pandemic like today. This research aims to make millennials aware of nationalism sense, which mainly to prevent the spread of Covid-19. This research is used to answer the questions of what the problems that arise due to the waning of the spirit of nationalism during the pandemic are? and what efforts should be made to maintain the spirit of nationalism? Methods: This research is a qualitative study using the literature review method. The articles used are research published in 2019 to 2021 in Google Scholar, with keywords that match the topic of millennial generation nationalism in the Covid-19 pandemic. Results and Discussion: The results of the study found that the spirit of Indonesian nationalism during the Covid-19 pandemic was decreasing. The decline in the sense of nationalism is due to several government policies that impact the psychology of society and the Indonesian economy. As a result, society, particularly the millennial generation, must play a role in breaking the chain of the Covid-19 virus's propagation by following the government's health standards. Conclusion: The government and society need to work together to understand nationalism in the millennial generation, especially in dealing with problems caused by the Covid-19 pandemic. Based on this, various efforts need to be made to foster the spirit of nationalism and overcome the Covid-19 pandemic. So that later, it can produce a generation that upholds the value of nationalism in everyday life.

Introduction: Indonesia consists of a plural society with different ethnic, religious, racial, skin color, and other cultures. The difference must be understood by everyone, especially the younger generation in the digital age. If the younger generation does not have a sufficient understanding of multicultural education values, it will impact the moral crisis of the Indonesian nation. This research emphasizes the importance of multicultural education to the younger generation to understand the differences (pluralism) in society and respect them. Method: The method uses a qualitative method with the literature review method by collecting data on ten journals published in 2019, 2020, 2021. Results: The rise of socio-cultural conflicts in Indonesia is why applying multicultural values to the younger generation. Multiculturalism is needed to establish morals and sensitivity for a young person in the face of various social problems and symptoms at a growing age. Conclusion: Multicultural education is vital to the younger generation considering Indonesia has diverse nations and cultures. The efforts that can make to improve the morale of the nation's children, namely by learning citizenship education and religious education and utilizing information technology to spread things related to the values of multicultural education to the broader community

Introduction: Pancasila was born as the ideology of the Indonesian nation, which reflects the values cultural values, ethics, morals, and the spirit of the nation. Pancasila was born as a form of national character, which reflects divinity, nationality, unity, togetherness, and justice. The purpose of this research is to know the practice of Pancasila values ​​in the life of the nation and the state. Especially, the implementation of the life of the younger generation in the 21st century today. Methods: This study is literature research where data is collected using keywords in the database. Literature source search carried out with the help of Boolean operators and using the PICOS format. The results of selecting articles, using PRISMA Flow Diagrams. The search results found 125 articles that match the research theme. After checking for duplication, 82 articles were excluded because of the similarity in the results of the discussion. In the end, there were 43 articles used as a literature source. Results: Based on the analysis of research findings, which are supported by literature sources, it shows that the current values ​​of Pancasila are worrying. This can be seen, where people's behavior has been contaminated by external cultures due to technological sophistication. At present, it can be said that the ethics and morals of society do not reflect the values ​​of Pancasila. Conclusion: In order to re-install the values ​​of Pancasila in people's lives, the participation of all parties is necessary. This is especially so, for today's younger generation. Therefore, it is necessary to develop a model for teaching Pancasila to be carried out by schools and colleges. So that later, it can produce a generation of ethics and morals who love the country.

Three computer algorithms are presented. One reduces a network $\CALN$ to its interior, $\CALI$. Another counts all the triangles in the network, and the last randomly generates networks similar to $\CALN$ given just its interior $\CALI$. But these algorithms are not the usual numeric programs that manipulate a matrix representation of the network; they are set-based. Union and meet are essential binary operators; contained_in is the basic relational comparator. The interior $\CALI$ is shown to have desirable formal properties and to provide an effective way of revealing ``communities'' in social networks.

In this paper, we revisit the hydrodynamics supporting the design and development of the RiverCat class of catamaran ferries operating in Sydney Harbor since 1991. More advanced software is used here. This software accounts for the hydrodynamics of the transom demisterns which experience partial or full ventilation, depending on the vessel speed. This ventilation gives rise to the hydrostatic drag, which adds to the total drag of the vessel. The presence of the transom also creates a hollow in the water. This hollow causes an effective hydrodynamic lengthening of the vessel, which leads to a reduction in the wave resistance. Hence a detailed analysis is required in order to optimize the size of the transom. It is demonstrated that the drag of the vessel and the wave generation can be predicted with good accuracy. Finally, the software is also used to optimize the vessel further by means of affine transformations of the hull geometry.

In present work, the effects of bioflavonoids (ginkgetin and sciadopitysin) on stimulus-induced superoxide generation, tyrosyl and serine/threonine phosphorylation of proteins in human neutrophils, and the translocation of cytosolic compounds (p47phox, p67phox and Rac) to cell membrane were studied, which were isolated from the needles of Taxus media var. Hicksii. Meanwhile, three normal flavonoids (apigenin, quercetin and isoquercetin) were involved as contrasts. The results indicated that ginkgetin and sciadopitysin were capable of concentration-dependently inhibitory effects on the superoxide generation induced by N-formyl-methionyl-leucyl-phenylalanine (fMLP), arachidonic acid (AA) and phorbol-12-myristate 13-acetate (PMA). And they also suppressed fMLP- and AA- induced tyrosyl or PMA-induced serine/threonine phosphorylation and the translocation of cytosolic compounds (p47phox, p67phox and Rac) to cell membrane, which were in parallel with the suppression of the stimulus-induced superoxide generation. The effect of these compounds on the radical-scavenging was also investigated. Ginkgetin and sciadopitysin did not show remarkable effect on DPPH radical-scavenging activity, and they didn’t display the radical-scavenging activity on superoxide anion generated by phenagine methoxysulfate (PMS)-NADH system. Apparently, ginkgetin and sciadopitysin had great performance in pharmacological value and they are worthy of in-depth study.

When a microgrid is grid-tied to a distribution system, it can provide surplus power generation to the distribution system, if any abnormality or interruption occurs in the distribution system, the microgrid can operate in standalone mode to isolate the impact of the abnormality or interruption. However, if the microgrid can not collect enough information from the distribution system, it may cause the failure of generation transferring of distribution feeders, or even further influence the stability of the distribution system. In this paper, a strategy for the resilient control of a microgrid is proposed. It can solve the above-mentioned problem, reduce the duration of the outage of loads. This strategy is experimented in the microgrid in the Institute of Nuclear Energy Research (INER), the reliability is also analyzed to evaluate the unavailability of the microgrid in INER, and it is verified that the proposed strategy can reduce the duration of the outage of loads, and hence the reliability of a microgrid can be upgraded.

Modern electrical power systems integrate renewable generation, with solar generation being one of the pioneers worldwide. In Latin America, the greatest potential and development of solar generation is found in Chile through the National Electric System. However, its energy matrix faces a crisis of drought and reduction of emissions that limits hydroelectric generation and involves the definitive withdrawal of coal generation. The dispatch of these plants is carried out by the system operator, who uses a simplified mechanism, called “economic merit list” and which does not reflect the real costs of the plants to the damage of the operating and marginal cost of the system. This inefficient dispatch scheme fails to optimize the availability of stored gas and its use over time. Therefore, a real-time redispatch model is proposed that minimizes the operation cost function of the power plants, integrating the variable generation cost as a polynomial function of the net specific fuel consumption, adding gas volume stock restrictions and water reservoirs. In addition, the redispatch model uses an innovative "maximum dispatch power" restriction, which depends on the demand associated with the automatic load disconnection scheme due to low frequency. Finally, by testing real simulation cases, the redispatch model manages to optimize the operation and dispatch costs of power plants, allowing the technical barriers of the market to be broken down with the aim of integrating ancillary services in the short term, using the power reserves in primary (PFC), secondary (SCF), and tertiary (TCF) frequency control.

Large language models (LLMs) excel in providing natural language responses that sound authoritative, reflect knowledge of the context area, and can present from a range of varied perspectives. Agent Based Models and Simulation consist of simulated agents that interact within a simulated environment to explore societal, social, and ethical, among other, problems. Agents generate large volumes of data over time and discerning useful and relevant content is an onerous task. LLMs can help in communicating agents’ perspectives on key events by providing natural language narratives. However, these narratives need to be factual, transparent, and reproducible. To this end, we present a structured narrative prompt for sending queries to LLMs. Chi-square tests and Fisher’s Exact tests are applied to assess statistically significant difference in sentiment scores of the narrative messages between simulation generated narratives, ChatGPT-generated narratives, and real tweets. The narrative prompt structure effectively yields narratives with the desired components from ChatGPT. This structure is expected to be extensible across LLMs. In 14 out of 44 categories, ChatGPT generated narratives which has sentiment scores that were not discernibly different, in terms of statistical significance (alpha level of 0.05), from the sentiment expressed in real tweets. Three outcomes are provided: (1) a list of benefits and challenges for LLMs in narrative generation; (2) a structured prompt for requesting narratives of a LLM based on simulated agents’ information; and (3) an assessment of statistical significance in the sentiment prevalence of the generated narratives compared to real tweets. This indicates significant promise in the utilization of LLMs for helping to connect simulated agent’s experiences with real people.

As one of the world's most populous countries, Indonesia is struggling with the composition of its people, the majority of whom are unemployed productive-age individuals with low education levels. Another issue is the high poverty rate, which is followed by issues of gender inequality. The government cannot solve these social issues on its own. There is a need for community support to solve these problems through sustainable social entrepreneurship activities. Using the Theory of Planned Behavior and Outcome Expectation, this study will determine the factors that influence the interest of individuals known as the Z and Millennial generation, aged 18 to 34 years old, in becoming social entrepreneurs. This study applied a quantitative approach with a judgmental sampling technique to a sample of Generation Z and Millennials who have ever participated in social activities but have not yet started their own business. The study gathered 210 respondents, who were then analyzed using the PLS-SEM method with multiple group analysis generation-based comparison. According to the findings, the Millennial Generation's Attitude Toward Behavior, Subjective Norms, and Outcome Expectation all had a positive impact on Social Entrepreneurial Intention. Meanwhile, Subjective Norms had no effect on Social Entrepreneurial Intention in the Z Generation. Only attitude toward behavior, perceived behavioral control, and outcome expectation have a positive effect on the development of social entrepreneurial intentions. Furthermore, the findings of this study confirm that subjective norms influence attitude toward behavior and Perceived Behavioral Control in both Generation Z and millennials.

The energy supply for isolated systems remains a current challenge in Brazil and worldwide, particularly when known solutions are compared regarding cost, the ability to supply energy on demand, and sustainability. In this context, green hydrogen, which has been considered one of the main paths for the decarbonization of the energy chain, is also seen as a potential solution for energy supply in isolated systems. Based on a literature review of articles addressing the topics of green hydrogen and isolated systems, an analysis is conducted on the application of green hydrogen as an energy supply solution for isolated systems. The review identified and collected data on important parameters for comparing solutions, such as CAPEX, OPEX, energy cost, and the price per kilogram of hydrogen. Using these data, analyses were performed to provide an overview of the application of green hydrogen in isolated systems. One of the objectives of this work is to present a comprehensive analysis of the green hydrogen chain and its application in the context of electricity generation, particularly for isolated systems in Brazil.

The increasing demand for energy, driven by technological advances, population growth, and economic expansion, has intensified the focus on efficient energy management. Tri-generation systems, such as Combined Cooling, Heating, and Power (CCHP) systems, are of particular interest due to their efficiency and sustainability. Integrating renewable energy sources like solar power with traditional fossil fuels further optimizes CCHP systems. This study presents a novel method for enhancing CCHP system efficiency by identifying optimal design parameters and assisting decision-makers in selecting the best geometric configurations. A mathematical programming model using the Harris Hawks optimizer was developed to maximize net power and exergy efficiency while minimizing CO2 emissions in a solar-assisted CCHP system. The optimization resulted in 100 Pareto optimal solutions, offering various choices for performance improvement. This method achieved higher net power output, satisfactory exergy efficiency, and lower CO2 emissions compared to similar studies. The study shows that maximum net power and exergy efficiency, with reduced CO2 emissions, can be achieved with a system having a low compression ratio and low combustion chamber inlet temperature. The proposed approach surpassed the response surface method, achieving at least a 4.2% reduction in CO2 emissions and improved exergy values.

The objective of this research was to analyze the evolution and trend of the generation gap and nursing education from 1968 to 2021 through bibliometric indicators. The method used is a bibliometric, descriptive and retrospective study of documents in Scopus, with descriptors “Nursing educat*”, “generation* and the Boolean operator “AND”, N= 653 web documents from 1968 to 2021, and filters of the type bib, csv. The search was carried out on October 9, 2021, using the Bibliometrix R Package software and the Biblioshiny tool. The Results indicate high scientific production in 2020 (54 articles disclosed). This study demonstrates patterns and lines of research in nursing education, such as educational models, interpersonal relationships, and organizational innovation.

Boutique hotels emerged in the late 1970s since then the product has become more mainstream. The draw of boutique hotels is more personalized services. When marketing and providing services to a niche market it is smart to be able to differentiate potential guests to best meet their needs. The generational cohort theory states that a population may be divided into segments based on their generation. This research focuses on Generation X to find out what they are seeking in a boutique hotel product. Based upon how they chose their travel destination there were significant differences between culinary experience, attractions/activities, and cost/budget.

Pairs of potential and flow variables have long been established for interfacing object-oriented component models of physical systems. Recently, however, the use of triplets has been discovered for the purpose of robust modeling. New and powerful Modelica libraries have been developed such as the DLR ThermoFluid Stream library or the introduction of the Dialectic Mechanics library. Their use of triplets is entangled with a special modeling approach that uses Linear Implicit Equilibrium Dynamics. In this paper, we study the basic motivation of this approach, its benefits and drawbacks before we finally demonstrate how it beneficially impacts the generation of corresponding simulation code.

This paper aims to explore how climate change affects electricity generation (fossil-fueled power plants, hydroelectric stations, wind systems and photovoltaics) and energy demand in residential buildings in Greece for the period up to 2050. In both cases RCP scenarios results are considered in the appropriate spatial and temporal dimension. Energy supply technologies were examined through statistical regression models and/or mathematical equations correlating climatic parameters with energy productivity. With respect to energy demand, bottom-up models were developed that integrate behavioral and policy aspects. The analysis showed that climate change is expected to mainly affect electricity generation from hydro and thermal power plants, while the impacts on solar and wind energy are significantly lower. The range of impacts on hydro potential and electricity generation depends on the geographical location of the power plants and shows significant uncertainty due to the corresponding uncertainty in climate models estimates regarding precipitation and runoff changes while showing significant geographical variations. With respect to energy demand, climate change is expected to affect energy consumption, but the expected range of effects depends on both the implementation and the characteristics (deep vs. shallow renovation) of measures for upgrading the thermal performance of the building stock and the intensity of climate change.

This paper aims to study and analyze a new Peltier cell layout using a PCM in biphasic mode, with the liquid phase encapsulating the p-n junctions and the solid one playing the role of heat sink. The selected PCM is an organic compound that operates within a temperature range corresponding to the melting process. The phase change zone encompasses the working temperature of the Peltier cell, avoiding thermal gradient variation across the cell section and maintaining the operating conditions and the cell efficiency. PCM encapsulates the Peltier cell thermocouples, creating a layer that increases the power generation as the thickness reduces. We simulated the performance of the new configuration for different temperature gaps between both sides of the cell, obtaining an improvement in power generation from 1.65 to 7.93. This power increase ratio depends on the temperature gap and transient time to reach the steady working temperature. The control of the layer thickness, transient time, and working temperatures resides in the external heating power source, which varies the operating conditions regulating the heat power and the airflow. Maximum output power from the cell prototype is 36.5 W, while conventional Peltier cells operating in similar conditions produce 7.3 W, which represents a power gain of 500%.

This paper performs a comprehensive analysis of the wind energy potential of onshore regions in Greece with emphasis on quantifying the volume risk and the spatial covariance structure. Optimization techniques are employed to derive efficient wind capacity allocation plans (also known as generation portfolios) incorporating different yield aspirations. The generation profile of minimum variance and other optimal portfolios along the efficient frontier are subject to rigorous evaluation using a fusion of descriptive and statistical methods. In particular, principal component analysis is employed to estimate factor models and investigate the spatiotemporal properties of wind power generation, providing valuable insights into the persistence of volume risk. The overarching goal of the study is to employ a set of statistical and mathematical programming tools guiding investors, aggregators and policy makers in their selection of wind energy generating assets. The findings of this research challenge the effectiveness of current policies and industry practices, offering a new perspective on wind energy harvesting with a focus on the management of volume risk.

The sandwich generation caregivers provide simultaneous care for multiple generations. Caregiving is a dynamic process, and each generation has its unique set of needs. Moreover, studies have indicated a growing population within this group. However, despite extensive research efforts, the caregiving practices employed by the sandwich generation remain inadequately explained. Therefore, the objective of this study was to shed light on the caregiving practices offered by the sandwich generation. The grounded theory approach was selected as the conceptual framework for investigating the research objective. Purposive and snowball sampling methods were utilized to recruit 18 participants from the sandwich generation. The data collected through interviews and observations were analyzed using the 2015 version of Corbin and Strauss methodology. The findings revealed that sandwich generation caregivers are influenced by prominent personality traits, issues related to the elderly, and dependencies and interdependencies within the caregiving process. Sandwich caregivers employ a range of strategies influenced by intervening and background factors. Throughout this process, caregivers encounter both positive and negative consequences. Consequently, policymakers and planners should prioritize the establishment and expansion of support systems for caregiving.

It is currently unknown whether thrombin generation is associated with venous thromboembolism (VTE) recurrence, major bleeding, and mortality in the elderly. Therefore, our aim was to prospectively study the association between thrombin generation and VTE recurrence, major bleeding and mortality in elderly patients with acute VTE. Consecutive patients aged ≥65years with acute VTE were followed for 2 years starting from 1 year after the index VTE. Primary outcomes were VTE recurrence, major bleeding and mortality. Thrombin generation was assessed in 565 patients 1 year after the index VTE. At this time, 59% of patients were still anticoagulated. Thrombin generation was discriminatory for VTE recurrence, but not for major bleeding and mortality in non-anticoagulated patients. Moreover, peak ratio (adjusted subhazard ratio 4.09, 95% CI, 1.12-14.92) and normalized peak ratio (adjusted subhazard ratio 2.18, 95% CI, 1.28-3.73) in presence/absence of thrombomodulin were associated with VTE recurrence, but not with major bleeding and mortality after adjustment for potential confounding factors. In elderly patients, thrombin generation was associated with VTE recurrence, but not with major bleeding and/or mortality. Therefore, our study suggests the potential usefulness of thrombin generation measurement after anticoagulation completion for VTE to help identifying among elderly patients those at higher risk of VTE recurrence.

The paper investigates experimentally the influence of the Fused Filament Fabrication (FFF) pre-processing strategy on the tribological properties of parts made of PEI (polyetherimide), investigating the difference in the weight loss of the samples caused by abrasive wear before and after the test. The samples were tested for abrasive wear resistance on a tribometer constructed according to ASTM G65/16 (dry sand). Two types of specimen printing orientation along X-axis (lengthwise) and along Z-axis (heightwise) were tested in the study. Three types of path generation strategies were used for manufacturing "X" and "Z" specimens and totally 18 samples were made and tested. Path deposition strategy was carried out with path deposition width of 0.5mm and parallel paths in all slicing layers. In order to reduce the voids in volume, deposition path axes in subsequent layers were shifted by 0.25mm offset. Garnet abrasive Fe3Al2(SiO4)3 was used for this test. The analysis of the experimental data shows the relationship between the samples building direction (X, Z), applied deposition strategies (1X - 3X and 1Z - 3Z) on the resulting resistance to abrasive wear.Keywords: PEI, FFF, path generation strategy, abrasive wear

This survey proposed an evaluation model to analyze and examine different approaches to generativity. In addition to problem domain concepts, the following concepts were used to define this model: Complexity and complexity management, and Systematics view to achieve unified and integrated insight into disparate evaluation criteria. The research's approach to the said concepts is first introduced. Then, the evaluation model is presented.

Character education is essential for the current millennial generation and must be applied in everyday life in the surrounding community. The character of the millennial generation is different from the previous generation, which requires us to pay more attention to character education which is starting to erode along with the times. The Millennial generation is a generation that in everyday life can never be separated from science and technology. In this digital era, the millennial generation has a social function of strength in technology. In this study, there are research questions that will be discussed in this article, namely the first regarding what problems occur in the millennial generation who occupy the digital era?. Second, what is the important role and influence of the digital era on character education for the millennial generation in Indonesia?. Writing this article uses a qualitative research method, namely a literature review study, where researchers use or look for references from journals or books. Samples of respondents were taken randomly from each region, and there were ten respondents. From this research, it is found that the millennial generation in the digital era chooses to use social media to access the latest news and information rather than reading print media that contains education. This article aims to find out the importance of character education for the millennial generation, which is starting to erode with the times in the digital era. Many people underestimate character education, especially for the current millennial generation. Therefore, we must implement educational programs at any level, especially in Indonesia, because the character shows the national identity. This article will discuss the vital role and influence of character education for millennials who live in the digital era to be aware of the importance of character education in citizenship.

The increasingly used approach of combining different simulation software in testing of automated driving systems (ADS) increases the need for potential and convenient software designs. Recently developed co-simulation platforms (CSP) provide the possibility to cover the high demand on testing kilometers for ADS by combining vehicle simulation software (VSS) with traffic flow simulation software (TFSS) environments. The emphasis on the demand of testing kilometers is not enough to choose a suitable CSP. The complexity level of the used vehicle, object, sensors and environment models is essential for valid and representative simulation results. Choosing a suitable CSP raises the question of how the test procedures should be defined and constructed and what the relevant test scenarios are. Parameters of the ADS, the environments, objects, sensors in VSS as well as traffic parameters in TFSS can be used to define and generate test scenarios. In order to generate a large number of scenarios in a systematic and automated way, suitable and appropriate software designs are required. In this paper we present a software design for CSP based on the Model-View-Controller (MVC) design pattern and implementation of a complex CSP for virtual testing of ADS. Based on this design, an implementation of a CSP is presented using the VSS from IPG Automotive called CarMaker and the TFSS from PTV Group called Vissim. The results have shown that the presented CSP design and the implementation of the co-simulation can be used to generate relevant scenarios for testing of ADS.

The Convective Rainfall Rate from Cloud Physical Properties (CRPh) for Meteosat Second Generation Satellites is a day-only precipitation algorithm developed at the Spanish Meteorological Agency (AEMET) for EUMETSAT’ Satellite Application Facility in support to Nowcasting and Very Short Range Forecasting (NWC SAF). It is therefore mainly intended to provide input for monitoring and near-real-time forecasts for the next few hours. This paper critically discusses the theoretical basis of the algorithm with special emphasis in the empirical values and assumptions in the microphysics of precipitation and compares the performances of the CRPh with its antecessor, the Convective Rainfall Rate algorithm (CRR), using an object-based method. The analyses show that AEMET’s CRPh is physically consistent and that outperforms the CRR. The applicability of the algorithm for nowcasting and the challenges to evolve the product to an all-day algorithm are also presented.

An understanding of the cultural conditions that determine the factors affecting nurses’ intention to leave is important for countries suffering from nurse shortage. Aim: to examine factors influencing intention to leave among female hospital nurses in a large Japanese sample, classified into four generations by age considering economic conditions. Methods: a cross-sectional survey with convenience sampling was conducted. Anonymous self-administered questionnaires were distributed to all nurses in 30 hospitals. To assess intention to leave, basic attributes, life conditions, work characteristics, and factors of psychosocial work environment were addressed. After classifying data into four generations based on age cohorts, we conducted multivariate logistic regression analysis using the completed data (N = 5,074, mean age = 36.24). Results: regardless of generational characteristics influenced by economic conditions, effort and monetary reward were generation-common factors. Over-commitment, social support, and the presence of a role model were generation-common factors in three generations. While having children increased intention to leave in the generation born 1965–1979, having family members in need of caregiving other than children decreased the risk in the generation born in the 1980s. Conclusion: generational countermeasures considering factors of psychosocial work environment and life conditions are needed to avert female nurse turnover.

Current language models have achieved remarkable success in NLP tasks. Nonetheless, individual decoding methods face difficulties in realizing the immense potential of these models. The challenge is primarily due to the lack of a decoding framework that can integrate language models and decoding methods. We introduce the DecoStrat which bridges the gap between language modeling and the decoding process in D2T generation. By leveraging language models, DecoStrat facilitates the exploration of alternative decoding methods tailored to specific tasks. We fine-tune the model on the MultiWOZ dataset to meet task-specific requirements and employ it to generate output(s) through multiple interactive modules of the framework. The Director module orchestrates the decoding processes, engaging the Generator to produce output(s) text based on the selected decoding method and input data. The Manager module enforces a selection strategy, integrating Ranker and Selector to identify the optimal result. Evaluations on the stated dataset show that DecoStrat effectively produces diverse and accurate output, with MBR variants consistently outperforming other methods. DecoStrat with the T5-small model surpasses some baseline. Generally, the findings highlight DecoStrat's potential for optimizing decoding methods in diverse real-world applications.

This paper examines the quantitative and qualitative situation of the current fixed and mobile Internet and its expected future. First, the number of users and penetration rate of the Internet, various types of services in different countries, the ranking of countries in terms of the mean and median download and upload Internet data speeds, Internet data volume, and number and location of data centers in the world are presented. The second task introduces and details twelve performance evaluation metrics for broadband Internet access. Third, different wired and wireless Internet technologies are introduced and compared based on data rate, coverage, type of infrastructure, and their advantages and disadvantages. Based on the technical and functional criteria, in the fourth work, two popular wired and wireless Internet platforms, one based on optical fiber and the other based on the 5G cellular network, are compared in the world in general and Australia in particular. Moreover, this paper has a look at Starlink as the latest satellite Internet candidate, especially for rural and remote areas. The fifth task outlines the latest technologies and emerging broadband Internet-based services and applications in the spotlight. Sixthly, it focuses on three problems in the future Internet in the world, namely the digital divide due to the different quality of the Internet and new Internet-based services and applications of emerging technologies, the impact of the Internet on social interactions, and hacking and insecurity on the Internet. Finally, some solutions to these problems are proposed.

This paper proposes an omnidirectional conveyor as a novel alternative to existing omnidirectional conveyors. With symmetric and compact layout, this new structure ensures consistent kinematics and enhanced flexibility in trajectory planning. The kinematic model of the proposed omnidirectional conveyor is developed and verified through simulation in CoppeliaSim. Four typical classes of trajectories are generated and verified in the simulation environment. Using PID control, the actual trajectories of a package on the conveyor closely match the desired trajectories. In addition, this paper outlines the workspace and corresponding wheel patterns for the conveyor, demonstrating how different supported wheel patterns emerge when packages move across various areas of the conveyor. The discussion extends to fault tolerance and obstacle avoidance, examining the workspace and wheel patterns with one or two omni wheels failed. Furthermore, this paper provides a comprehensive analysis on the feasible desired movements for the packages on the conveyor under the constrained wheel speed. This provides insights and guidance on trajectory planning and design of the conveyor.

The use of Retrieval-augmented generation (RAG) using large language models (LLMs) has shown potential for addressing issues such as hallucinations and inadequately contextualized responses. A pivotal stage in the RAG process involves a retriever for retrieving chunks based on semantic similarity with the query. In this study, we advocate for and provide experimental evidence supporting integrating and maintaining questions and answers (QA) formatted databases to improve retrieved-context representations and response quality. Our experiments evaluate our approach on benchmark RAG datasets using standard evaluation metrics and provide comparative analyses against state-of-the-art retrieval methods, showing the potential of our approach.

Recent advancements in cognitive neuroscience, particularly in Electroencephalogram (EEG) signal processing, image generation, and brain-computer interfaces (BCI), have opened up new avenues for research. This study introduces a novel framework, Bridging Artificial Intelligence and Neurological Signals (BRAINS), which leverages the power of AI to extract meaningful information from EEG signals and generate images. The BRAINS framework addresses the limitations of traditional EEG analysis techniques, which struggle with nonstationary signals, spectral estimation, and noise sensitivity. Instead, BRAINS employs Long Short-Term Memory (LSTM) networks and contrastive learning, which effectively handle time-series EEG data and recognize intrinsic connections and patterns. The study utilizes the MNIST dataset of handwritten digits as stimuli in EEG experiments, allowing for diverse yet controlled stimuli. The data collected is then processed through an LSTM-based network employing contrastive learning and extracting complex features from EEG data. These features are fed into an image generator model, producing images as close to the original stimuli as possible. This study demonstrates the potential of integrating AI and EEG technology, offering promising implications for the future of brain-computer interfaces.

The proposed method deals with the problem of data privacy and sharing when processing personal mobility tracking data. Previous methods concentrate on producing highly detailed data on short-term and restricted areas, e.g. for autonomous driving scenarios. Another possibility consists of city-wide scales and beyond, that are used to predict general traffic flows. The presented approach takes the tracked mobility behavior of individuals to create coherent new mobility data that reflects the long-term mobility behavior of the person, guaranteeing location persistency and sound embedding within the point-of-interest structure of the observed area. After an analysis and clustering step with the original data, the area is distributed into a geospatial grid structure (H3 is used here), and the neighborhood relationships between the grids are interpreted as a graph. A feed-forward-autoencoder and a graph encoding-decoding network generate a latent space representation of the area. The original clustered data is associated with their respective H3 grids. With a greedy algorithm approach and concerning privacy strategies, new combinations of grids are top-level patterns for individual mobility behavior. Concrete locations are found and connected within the grids based on the original data. The described method is then applied to a study with 1000 participants from the city of Munich in Germany, and the results are described, showing the application of the approach in generating synthetic data, enabling further research on individual mobility behavior and patterns. The result is a sharable dataset on the same abstraction level as the input data, which makes it interesting, particularly for machine learning applications.

In this work various sample of primary aluminum dross were investigated for composition and evaluation of hydrogen generation via hydrolysis in low concentration alkali solution. Composi-tion revealed low to moderate aluminium content and hydrolysis reactions showed low hydrogen generation volumes per sample mass as well as purity, where composition plays substantial role in the hydrogen evolution, yet showing that there is possible place for dross use in hydrogen generation. It was revealed that some dross samples in addition to hydrogen also release methane via hydrolysis. Rection rate was obtained by the shrinking core model, written for spherical solid particles. Sample composition on hydrogen generation volume is compared to known literature.

Physical layer secret key (SK) generation is known to be an efficient means to achieve high secrecy rate, on the condition that dynamic channel state information (CSI) is provided. For this reason, the secrecy performance is highly degraded in a static environment. The intelligent reflecting surface (IRS) is a promising solution to create dynamic randomness, and thus lead to enhanced secrecy performance regardless of the user environments. This paper proposes an IRS-assisted physical layer SK generation scheme, by efficiently combing phase information of the direct and reflected channel information in a hybrid way. In particular, the initial SKs are obtained by adopting efficient phase quantization method with symmetric bit allocation to complex numbered channel estimates. Simulation results show that the proposed hybrid phase quantization (PQ) can improve the SK generation rate and the key disagreement probability in a static environment.

Enhanced supercontinuum generation (SCG) is experimentally demonstrated in integrated silicon nitride (Si₃N₄) waveguides incorporating highly nonlinear graphene oxide (GO) in the form of two-dimensional (2D) films. On-chip integration of the 2D GO films with precise control of their thickness is realized by using a transfer-free and layer-by-layer coating method. The control of the film length and coating position is achieved via window opening in the upper silica cladding of the photonic integrated chips. Detailed SCG measurements are performed using the fabricated devices with different waveguide geometries and GO film thicknesses, and the results are compared with devices without GO. Significantly improved spectral broadening of ultrashort optical pulses with ultrahigh peaks powers exceeding 1000 W is observed for the hybrid devices, achieving up to 2.4 times improvement in the spectral bandwidth relative to devices without GO. Theoretical analyses for the influence of GO film thickness, coating length, coating position, and waveguide geometry are also provided by fitting the experimental results with theory, showing that there is still significant room for further improvement. This work opens up a promising new avenue towards improving the SCG performance of photonic integrated devices by incorporating functional 2D materials.

Nowadays the practice of developing algorithms to maintain the confidentiality of data shows that there is a lack of some features, such as velocity, predictability, etc. Generating pseudorandom numbers is one such problem that lies in the basement of many algorithms, even in hardware microprograms. An unreliable generator can cause cyberattacks on it, despite the security in the upper layers. At the same time, the algorithm should be fast enough to provide uninterrupted circuit work for the entire system. The paper presents a new algorithm generating pseudorandom numbers on cellular automata, which is not only fast and easy-repeating, but unpredictable enough and can be used in cryptographic systems. Using the NIST statistical test suite for random and pseudorandom number generators (PRNG), it is shown that the presented algorithm is more than three times superior to the state-of-the-art methods and algorithms in terms of ? − ?????. A high level of the presented algorithm’s parallelization allows for implementation it effectively on calculators with parallel structure. CPU-based architecture, FPGA-based architecture, CUDA- based architecture of PRNG and different PRNG implementations are presented to confirm high performance of the proposed solution.

Interface solar steam generation (ISSG) are charming for its applications in desalination and wastewater treatment. Biomass is an attractive substrate for utilizing solar vapor evaporators because of its natural pore structure and water transportability. Polymers like polydopamine (PDA) and polypyrrole (PPy) with broadband spectrum absorption are fascinating in photothermal materials (PTMs). Herein, PDA coated daikon-based (PDA-DK) and PPy coated daikon-based (PPy-DK) PTMs have been exploited for solar steam generation. When polyethylene foam (PEF) was used as an insulating layer to limit heat loss from the PTMs to bulk water, the evaporation rate of PDA-DK and PPy-DK was raised from 0.82 kg m–2 h–1 and 0.96 kg m–2 h–1 to 1.50 kg m–2 h–1 and 1.60 kg m–2 h–1, respectively. Meanwhile, the corresponding photothermal conversion efficiency was increased to 89.01% and 98.97%, which was increased by nearly 40% under 1-sun irradiation. In addition, PDA-DK and PPy-DK exhibited remarkable stability for the solar steam generation without significant change through 15 cycles. Furthermore, PDA-DK and PPy-DK could effectively desalt seawater and purify dyeing wastewater. All the results indicate that PDA-DK and PPy-DK have great potential in real-world applications for solar steam generation.

Background: Currently, Indonesia is entering an era that is all digital and uses technology in all fields of activity. The internet has a lot to do with their behavior or character. Millennials are the nation's next generation. The emergence of the digital era caused by globalization has made the millennial generation less appreciative and practicing the values ​​of Pancasila. In this case, character education is something that must be applied to the millennial generation. Purpose: This study aims to analyze the characteristics of the millennial generation, the role of character education in realizing the values ​​of Pancasila in the daily life of the millennial generation, how to cultivate an attitude of patriotism and nationalism towards the nation by practicing Pancasila, and provide motivation for the millennial generation to realize these values. Pancasila values. Methods: This study uses a literature review method. The search for journals was carried out using the google scholar database with the keywords “Character Education”, “millenial generation”, and “Pancasila”. The journals selected are journals from 2019 to 2020. Results: By applying character education to the millennial generation, it will make it easier for them to practice the values ​​of Pancasila. Pancasila should be the way of life of the millennial generation. Recommendation and limitation: As millennials, we must protect the country from values ​​that are contrary to Pancasila. Character education must be applied following the nature of the millennial generation itself. Of course, the applied character education must prioritize and be based on the values ​​of Pancasila as well.

Introduction: The imbalance between technological development and ideological strengthening has resulted in the fading of the Pancasila ideological values of millennials. Technology that accompanies everyday life seems to be the main character in human life. Yet if humans themselves do not use technology wisely, technology can be a threat to both individuals and the life of the nation and state. If technology is increasingly out of control, threats to the Pancasila ideology are very likely to occur, such as in the G 30S PKI incident. Therefore, it is necessary to take action both from the government and the community as individuals to safeguard the development of technology to be used wisely. That way, we can prevent the threat to the Pancasila ideology together. Writing this article aims to analyze how the role and influence of the Pancasila ideology on the millennial generation in the 4.0 industrial revolution and to find solutions so that the values of Pancasila remain attached to the next generation of the Indonesian nation. Method: Writing This article uses a qualitative method by using literature reviews from the results of related research journals that have been published online through websites and other online media. Results: Thirteen journals and one book have met the criteria for the inclusion of a predetermined review. Research is based on the stigma of society regarding technological developments in the Industrial revolution 4.0. Therefore, the government is expected to be more severe in implementing human capital management. Conclusion: By procuring human capital management, it will be possible that Human Resources (HR) in Indonesia will be able to carry out the ideals of a golden Indonesian generation with Pancasila values inherent in the hearts of the people and the nation's future generations.

The interest in renewable energy to replace fossil fuel is increasing as the problem caused by climate change become more severe. Small hydropower (SHP) is evaluated as a resource with high development value because of its high energy density compared to other renewable energy sources. SHP may be an attractive and sustainable power generation environmental perspective because of its potential to be found in small rivers and streams. The power generation potential could be estimated based on the discharge in the river basin. Since the river discharge depends on the climate conditions, the hydropower generation potential changes sensitively according to climate variability. Therefore, it is necessary to analyze the SHP potential in consideration of future climate change. In this study, the future prospect of SHP potential is simulated for the period of 2021 to 2100 considering the climate change in three hydropower plants of Deoksong, Hanseok, and Socheon stations, Korea. As the results, SHP potential for the near future (2021 to 2040) shows a tendency to be increased and the highest increase is 23.4% at the Deoksong SPH plant. Through the result of future prospect, we have shown that hydroelectric power generation capacity or SHP potential will be increased in the future. Therefore, we believe that it is necessary to revitalize the development of SHP in order to expand the use of renewable energy. Also, a methodology presented in this study could be used for the future prospect of the small hydropower potential.

We show broadband sum-frequency generation (SFG) in the Green-Yellow-Orange-Red spectral range using bismuth triborate, BiB3O6 crystal (BIBO) as nonlinear material. We perform a noncollinear phase-matching configuration within BIBO crystal using the remaining light behind the second harmonic generation stage and the infrared idler of an optical parametric amplifier (OPA). The obtained mixing radiation of ultrafast light sources to generate femtosecond pulses across 520.5 to 742.5 nm region is observed. SFG spectrum from single-pass cross-correlation intensity over such visible range is showed. The SFG wavelengths as a function of tunable wavelength idler OPA agrees with the expectations of parametric conversion condition and open the door to practical multi-beam or multi-color sum-frequency generators.

We design an asymmetric nonlinear optical nanoantenna composed of a dielectric nanodisk and an adjacent nanobar. The proposed composite structure made of AlGaAs exhibits resonant response at both the fundamental and doubled frequencies. Being driven by the strong magnetic dipole resonance at the pump wavelength and a high-quality mode at the harmonic wavelength, the efficient second-harmonic radiation is generated predominantly along the vertical directions under the normally incident plane-wave excitation.

The lack of defect image data is one of the main factors affecting the accuracy of supervised deep learning-based defect detection models. In response to the insufficient training data of defect images with complex backgrounds such as rust and surface oil leakage in substation equipment, leading to poor performance of the detection model, this paper proposed a novel adversarial deep learning model for substation defect generation: ADD-GAN. In comparison to existing generative adversarial networks, this model generates defect images based on effectively segmented local areas of substation equipment images, avoiding image distortion caused by global style changes. Additionally, the model utilizes a joint discriminator for overall image and defect image to address the issue of low attention to local defect areas, thereby improving the loss of image features. This enhances the overall quality of generated images as well as locally generated defect images, ultimately improving image realism. Experimental results demonstrate that the YOLOV7 object detection model trained on the dataset generated using the ADD-GAN method achieves an mAP of 81.5% on the test dataset, representing an improvement of 9.6% over the original dataset, 5.7% over traditional augmentation methods, and 7% over typical adversarial deep learning methods. This confirms that the ADD-GAN method can generate a high-fidelity dataset of substation equipment defects.

Generating coherent and comprehensive responses remains a significant challenge Question-Answering (QA) systems when working with short answers especially for low-resourced languages like Farsi. We present a novel approach to expand these answers into complete, fluent responses, addressing the critical issue of limited Farsi resources and models. Our methodology employs a two-stage process: first, we develop a dataset using rule-based techniques on Farsi text, followed by a BERT-based ranking system to ensure fluency and comprehensibility. The resulting model demonstrates strong compatibility with existing QA systems, particularly those based on knowledge graphs. Notably, our system exhibits enhanced performance when integrated with large language models using Chain-of-Thought (CoT) prompting, leveraging detailed explanations rather than single-word answers. Our approach significantly improves response quality and coherence compared to baseline systems. We release our dataset to support further research in Farsi QA\footnote[1]{\href{https://huggingface.co/datasets/SLPL/syntran-fa}{https://huggingface.co/datasets/SLPL/syntran-fa}}.

Purpose: This study investigates the influence of human brands on the formation of Generation Z's digital identity. Given the increasing importance of the digital space in this generation's life, understanding how their identity is shaped by human brands in this environment is crucial. Method: Using a netnographic approach, qualitative data was collected and analyzed from 500 posts and interactions of Gen Z users on Instagram and TikTok platforms. Thematic content analysis was conducted using NVivo 12 software, with inter-coder reliability confirmed (κ = 0.85). Findings: Results indicate that human brands influence Gen Z's digital identity through three main mechanisms: 1) lifestyle emulation (β = 0.42, p < 0.001), 2) value alignment (β = 0.38, p < 0.001), and 3) self-presentation modeling (β = 0.35, p < 0.01). Multiple regression analysis showed these three factors explain 67\% of the variance in digital identity formation (R² = 0.67, F(3,496) = 335.42, p < 0.001). Conclusion: This study demonstrates that human brands play a significant role in shaping Generation Z's digital identity. Understanding this influence can help marketers and policymakers design more effective strategies for engaging with this generation.

Digital twin technology, a cutting-edge approach that creates dynamic digital replicas of physical systems, is increasingly integral to various industrial applications. However, the effective deployment of digital twins often faces challenges associated with data availability, quality, and interoperability, which can impede the development and operational efficacy of these virtual counterparts. This paper presents a systematic review of the application of artificial intelligence (AI) in enhancing and managing digital twin technologies across multiple domains such as industry, healthcare, urban planning, business, education, technology, and others. It specifically examines how machine learning models, particularly neural networks and deep generative models, are being employed to optimize the creation, maintenance, and functionality of digital twins. The review explores the role of artificial intelligence in overcoming data-related limitations by providing robust, scalable data solutions for digital twins. Additionally, it addresses critical considerations of real-time data processing and system interoperability within these applications. This survey not only identifies the prevailing challenges and opportunities within this emerging field but also highlights potential future research directions that could further the integration of AI with digital twin technology. Through a detailed exploration of the intersection between AI and digital twins, this paper aims to contribute significantly to the knowledge base and encourage further innovations in this interdisciplinary area.

Coupling faults that occur simultaneously widely exist in the operation of mechanical equipment, and coupling fault features are formed by nonlinear coupling of multiple base fault features. In this paper, hypergraph neural network is used to obtain the characteristics of coupling faults, and two coupling fault diagnosis frameworks based on hypergraph are provided. 1. Coupling fault diagnosis framework based on feature generation: The hypergraph neural network is used as a generator to provide negative samples for discriminator training. The generator outputs hyperedge vectors as fake samples, and each hyperedge connects a base fault containing a fault type. 2. Coupling fault diagnosis framework based on feature extraction: Each node is regarded as a fault type, and the fault characteristics of each type of fault are extracted through the aggregation operation of hypergraph. The attention mechanism is introduced to realize the dynamic adjustment of the hyperedge, and the dynamic vertex is used to classify the unknown faults. The results show that the diagnostic accuracy of coupling faults under the two frameworks reaches 88.6% and 86.76%, respectively.

Research in renewable energy sources and micro-grid systems is critical for the evolving power industry. Micro-grids can supply diverse loads while reducing dependency on the utility grid. This paper models both pico-grids and nano-grids in the Singapore Institute of Technology Punggol campus, incorporating solar PV, energy storage systems (ESS), power electronics converters, and both DC and AC loads, along with utility grid connections. The pico-grid includes a battery storage system, a single-phase inverter linked to a single-phase grid, and DC and AC loads. The nano-grid comprises solar PV panels, a boost converter, a battery storage system, a three-phase inverter connected to a three-phase grid, and AC loads. Both the pico-grid and nano-grid are configurable in standalone or grid-connected modes. This configuration flexibility allows for detailed operational analysis under various conditions. The study conducts subsystem-level modeling before integrating all components into the simulation environment. MATLAB/Simulink is utilized to model, simulate, and analyze the power flow in both the pico-grid and nano-grid under different operating conditions.

This paper explores the development of a sophisticated system for automatic test generation tailored specifically for Java web applications utilizing REST interfaces. Writing tests ensures the quality of the software solution event after the introduction of subsequent changes, because automated regression testing can detect potential bugs in the code event before the application is resealed into production. By utilizing and comparing various automatic test generators, the system described provides users with a visual representation of code structure and coverage, enhancing insights through call graph visualization. The paper describes the concepts of theoretical automatic test generators, gives an overview of generators, and details the design and implementation of the implemented solution, which integrates the system test generator and the unit test generator based on open source to achieve the maximum code coverage level.

Securing funding is a critical yet complex task for organizations and individuals. This study presents an innovative chatbot designed to streamline the process using advanced Natural Language Processing (NLP) techniques and, specifically, a Retrieval-Augmented Generation (RAG) pipeline optimized for real-world applications. Our chatbot assists users in identifying suitable public tenders for financial support through natural language queries and a comprehensive public data database. The chatbot operates in a two-stage interaction model, initially providing summarized tender information for exploratory brainstorming, followed by detailed data upon user selection. A custom filtering mechanism ensures that the user interface elements responsible for swapping the interaction stages are consistent with the responses generated by the conversational agent. Human-evaluation tests demonstrated an average accuracy of 90.4% in document retrieval, with an average of 2.11 interactions required to find a specific tender. User satisfaction, rated on a scale of 1 to 5, averaged 3.14 (±1.73), indicating generally positive user experience with room for improvement. This approach addresses challenges of relevance, accuracy, and conversational flow, resulting in a reliable chatbot that simplifies the process of finding funding opportunities.

Driven by mounting environmental apprehensions stemming from the over dependence on fossil fuels in energy and transportation sectors, extensive exploration into alternative energy sources, particularly biomass, has stimulated profound research on harnessing the potential of computational and mathematical techniques. This comprehensive survey delves into optimal models and strategies, unveiling their pivotal role in the design of biomass gasification processes.of hydrogen through biomass gasification. In this study, we aim to provide a comprehensive overview of the computational and mathematical techniques employed in optimizing biomass gasification processes, with a specific focus on enhancing hydrogen yield. Through an extensive literature review, various models and strategies will be examined, including thermodynamic analysis, kinetic modeling, reactor design, and process optimization. By uncovering the power of these techniques, we aim to contribute to the advancement of sustainable and efficient biomass utilization for the hydrogen-based future economy.This paper aims to provide an updated and comprehensive coverage of the investigations conducted on the potential of producing hydrogen from biomass through gasification. To achieve this, we incorporate the latest works that have utilized numerical modeling, simulation, optimization techniques, process heat integration, and co-generation in their studies. By encompassing these aspects, we aim to offer a broader and more in-depth re-appraisal of the subject matter. This will ensure that readers gain a holistic understanding of the advances made in the field and the potential for sustainable hydrogen production of biomass gasification.Through a meticulous re-appraisal and analysis of each subject, we can identify their respective strengths and areas that require further research effort. By thoroughly examining numerical modeling, simulation, optimization techniques, process heat integration, and co-generation, we can assess their effectiveness and applicability in the context of biomass gasification for hydrogen production. This analysis shed light on the areas where these techniques excel, as well as pinpoint limitations or gaps in current understanding. By identifying these areas, we can highlight the need for further research and development, enabling us to make significant advancements in biomass gasification processes and pave the way for a sustainable and efficient hydrogen-based future economy..

This research, which focuses on wave energy generating technology, suggests a brand-new oscillating float-type wave energy conversion system that is appropriate for the offshore wave environment. To accurately model the device, a three-dimensional viscous numerical pool is created based on STAR-CCM+. The array-type power generation device is numerically simulated using theoretical analysis and numerical simulation, and the wave energy power generation device is arranged as double floats in the front and rear rows, representing triangular array type and rectangular array type, respectively. The triangular array type is a superior layout and can make greater use of marine resources, according to this paper's analysis of the hydrodynamic and power generation impacts of the three array types. The findings are meant to serve as a guide for engineering practice.

Remote distributed generation control and demand response is nowadays a basic need, especially for the Distributed Network Operators (DNOs). The integration of Renewable Energy Sources (RES) with stochastic production characteristics into the grid in conjunction with the newly appeared stochastic demand consumers (i.e., electric vehicles) hardens the ef-forts of the DNOs to keep the grid’s operation within safe limits and prevent cascading black-outs while staying in compliance with the SAIDI and SAIFI indices during maintenance and re-pair operations. Taking also under consideration the aging of the existing grid infrastructure, making it year by year more prone to failure, it is yet of great significance for the DNOs to have access to real-time feedback from grid’s infrastructure, fast, with low-cost upgrade interventions, easily deployed on the field and with fast response potential, in order to be able to perform real-time grid management (RTGM). In this article, we describe the architecture, development and deployment of a hybrid real-time control system of distributed generation (DG) units, which has the potential to be applied easily to DNO’s substations, RES plants and consumers (i.e. electric vehicles’ charging stations), that combines a low-cost minimum intervention into the electricity grid and its components and eliminates all communication interoperability problems between the different vendors’ installed electrical equipment.

This paper investigates the polarization and heat generation characteristics of batteries under different ambient temperatures and discharge rates by means of using a coupled electric-thermal model. The study found that the largest percentage of polarization is ohmic polarization, followed by concentration polarization and electrochemical polarization. The values of the three types of polarization are generally small and stable under normal-temperature environments and low-discharge rates. However, they increase significantly in low-temperature environments and high-discharge rates and continue to rise during the discharge process. Additionally, ohmic heat generation and polarization generation also increase significantly under these conditions. Reversible entropy heat is less sensitive to ambient temperature but increases significantly with the increase of the discharge rate. Ohmic heat generation and polarization heat generation contribute to the total heat generation of the battery at any ambient temperature, while reversible entropy heat only contributes to the total heat generation of the battery at the end of discharge.

This study explores the potential of data science software solutions like Customer Relationship Management Software (CRM) for increasing the revenue generation of businesses. We focused on those businesses in Accommodation and Food Service sector across the European Union (EU). The investigation is contextualized within the rising trend of data-driven decision making, examining the potential correlation between data science application and business revenue. Employing a comprehensive evaluation of Eurostat datasets from 2014 to 2021, we used both univariate and multivariate analyses, we assessed e-commerce sales data across countries, focusing on the usage of big data and CRM tools. Big data utilization showed a clear, positive relationship with enhanced e-commerce sales. However, CRM tools exhibited a dualistic impact: while their use in marketing showed no significant effect on sales, their application in non-marketing functions had a negative correlation. These findings underscore the potential role of CRM and data science solutions in enhancing business performance in the EU's Accommodation and Food Service industry.

Continuous glucose monitoring devices allow diabetes condition management. However, when limited data is available, one option is to increase their size by generating synthetic samples. From a homemade wearable prototype was created a real dataset with 18 instances and 53 attributes that capture characteristics of capillary and venous blood glucose, oxygen concentration, pulse rate, skin temperature, and 24 modules and 24 phases related to bio-impedance. The objective of this article is to generate synthetic datasets, and also it investigates the ideal features subset and optimal model for non-invasive diabetes prediction. Gaussian-Copulas (GC), conditional generative adversarial networks (CG), variational autoencoders, and Copula-GAN techniques' were used to generate five synthetic datasets. Experiments show that GC1 and GC2 datasets follow min/max boundaries and are not copies of the original data. Multilayer perceptron regressor outperformed (train and test) with 2.17, 2.51 in MAE; 9.29, 13.59 in MSE; 3.05, 3.69 in RMSE, and 0.95, 0.92 in R2 in GC1, and 2.64, 3.02 in MAE; 11.43, 15.11 in MSE; 3.38, 3.89 in RMSE, and 0.94, 0.92 in R2 in GC2 with eight features. Future work is necessary to explore autoencoder and generative architectures, datasets with diverse characteristics, and the effect of the number of features.

Living beings are composite thermodynamic systems in non-equilibrium conditions. Within this context, there are a number of thermodynamic potential differences (forces) between them and the surroundings, as well as internally. These forces lead to flows, which, ultimately, are essential to life itself. Living beings are under the pressures of natural selection, thus are biological flows as well. At the same time, the maintenance of homeostatic conditions, the tenet of physiology, demands regulation of these flows by control of variables. However, due to the very nature of these systems, regulation of flows and control of variables become entangled in closed loops. Therefore, the search for adaptation in flows takes a different path than the search for adaptation in morphological traits. Being at the roots of transfer processes, thermodynamic criteria turn out to be as natural physical candidates. Likewise, being at the roots of physiology, control turns out to be as a natural biological candidate in that path. Here we show how to combine entropy generation, with respect to a generalized process, and control of parameters (in such a generalized process) in order to create a criterium of optimal ways to regulate changes in generalized flows.

This study addresses the motivations behind Generation Z's attendance at conferences and aims to contribute to a better understanding of their conference participation behavior. The study identified six latent factors that influence conference attendance motivation: Green and Digital Conference Experience, Educational and professional opportunities, Conference costs, Destination and conference stimuli, Stimulating factors, and Conference accessibility. These factors align with Generation Z's unique characteristics and preferences, such as their environmental consciousness, reliance on digital technologies, desire for continuous learning, career advancement, and experiences. The study also found that destination characteristics and educational and professional opportunities are significant motivators for Generation Z's conference attendance across different cultural contexts. Additionally, Generation Z's strong interest in sustainability and financial challenges highlights the need for eco-friendly practices and affordable registration fees to make conferences more appealing and accessible. The study further explores the impact of socio-demographic characteristics, revealing the gender and past conference attendance differences in motivation. Finally, the study discusses the implications for the conference industry and suggests areas for future research to enhance the understanding of conference attendance motivation among Generation Z.

Huge-scale video surveillance systems have become essential in crime prevention and situation recording. Traditional surveillance systems relied on human monitoring of video streams, which often led to errors and difficulties in understanding events. Furthermore, locating specific scenes within recorded videos required extensive human investigation. To overcome these challenges of inefficiency, inconvenience, and potential risks, we propose an intelligent analysis scheme that utilizes abnormal behavior recognition and metadata retrieval algorithms to replace human monitoring. The proposed method consists of three stages: i) metadata generation through object detection and tracking, ii) abnormal behavior recognition, and iii) SQL-based metadata retrieval. By incorporating specific information such as object color and aspect ratio, our technique enhances the usability of retrieval. Moreover, our abnormal behavior recognition module demonstrates robust classification capabilities for activities such as pushing, violence, falling, and crossing barriers. The proposed method can be seamlessly deployed on both edge cameras and analysis servers, making it adaptable to various surveillance setups. This approach revolutionizes the traditional surveillance paradigm, enabling more efficient, reliable, and secure video monitoring and analysis.

This research aimed to determine salient factors affecting the decision to become a beginning organic farmer. New and beginning organic farmers have unique characteristics, showcasing their dedication to environmental justice and social justice at the expense of their own businesses. This research employed a multiple-case case study design. We conducted semi-structured interviews with 40 first-generation farmers who operate organic farms in Arkansas, Florida, or Georgia. We analyzed interview transcripts using the qualitative analysis approach of coding. Our results revealed the reasons that people with little practical knowledge start farms. They are inspired by those around them who succeed and encouraged by influential characters in the field who assure them they can do something they love and be profitable. This research showed that first-generation farmers find inspiration and develop values rooted in food justice. Our findings have implications for developing and implementing current and future programmatic activities that aim to enhance beginning farmer training and workforce development. Identifying sources of inspiration will help researchers and service providers target newer and beginning farmers to support a vibrant food system, including a burgeoning market opportunity, developing strong communities around food, and building grassroots solutions.

Wearable thermoelectric generators (w-TEGs) convert thermal energy into electrical energy to realize self-powering of intelligent electronic devices, thus reducing the burden of battery replacement and charging, and improving the usage time and efficiency of electronic devices. Through finite element simulation, this study successfully designed high-performance thermoelectric generator and made it into wearable thermoelectric module by adopting “rigid device - flexible connection” method. It was found that higher convective heat transfer coefficient on cold-end leads to larger effective temperature difference and better power generation performance of device in typical wearable scenario. Meanwhile, at same convective heat transfer coefficient on the cold-end, longer TE leg length leads to larger temperature difference established at both ends of device, larger device output power and open-circuit voltage. However, when the convective heat transfer coefficient increases to a certain level, optimization effect of increasing TE leg length on device power generation performance will gradually diminish. For devices with fixed temperature difference between two ends, longer TE leg length leads to higher resistance of TEG, resulting in lower device output power but slight increase in open-circuit voltage. Finally, sixteen 16×4×2 mm2 TEGs (L=1.38 mm, W=0.6 mm) and two modules were fabricated and tested. At hot end temperature Th=33 ℃ and cold end temperature Tc=30 ℃, the actual maximum output power Pout of TEG is about 0.2 mW, and the actual maximum output power Pout of TEG module is about 1.602 mW, which is highly consistent with the simulated value. This work brings great convenience to research and development of wearable thermoelectric modules and provides new, environmentally friendly and efficient power solution for wearable devices.