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arXiv 2022年

作者： Jivanjee, Tasneem Ibrahim, Samira Nyquist, Sarah K. James Gatter, G. Bromley, Joshua D. Jaiswal, Swati Berger, Bonnie Behar, Samuel M. Christopher Love, J. Shalek, Alex K. Institute for Medical Engineering & Science Department of Chemistry Massachusetts Institute of Technology CambridgeMA United States Koch Institute for Integrative Cancer Research Massachusetts Institute of Technology CambridgeMA United States Ragon Institute of MGH MIT and Harvard CambridgeMA United States Broad Institute of MIT and Harvard CambridgeMA United States Massachusetts Institute of Technology CambridgeMA United States Program in Computational and Systems Biology Massachusetts Institute of Technology CambridgeMA United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA United States Microbiology Graduate Program Massachusetts Institute of Technology CambridgeMA02139 United States Department of Microbiology and Physiological Systems University of Massachusetts Medical School WorcesterMA United States Department of Mathematics Massachusetts Institute of Technology CambridgeMA United States Department of Chemical Engineering Massachusetts Institute of Technology CambridgeMA United States

Antigen-specific T cells play an essential role in immunoregulation and many diseases such as cancer. Characterizing the T cell receptor (TCR) sequences that encode T cell specificity is critical for elucidating the antigenic determinants of immunological diseases and designing therapeutic remedies. However, methods of obtaining single-cell TCR sequencing data are labor and cost intensive, typically requiring both cell sorting and full length single-cell RNA-sequencing (scRNA-seq). New high-throughput 3' cell-barcoding scRNA-seq methods can simplify and scale this process;however, they do not routinely capture TCR sequences during library preparation and sequencing. While 5' cell-barcoding scRNA-seq methods can be used to examine TCR repertoire at single-cell resolution, doing so requires specialized reagents which cannot be applied to samples previously processed using 3' cell-barcoding methods. Here, we outline a method for sequencing TCRα and TCRβ transcripts from samples already processed using 3' cell-barcoding scRNA-seq platforms, ensuring TCR recovery at a single-cell resolution. In short, a fraction of the 3' barcoded whole transcriptome amplification (WTA) product typically used to generate a massively parallel 3' scRNA-seq library is enriched for TCR transcripts using biotinylated probes, and further amplified using the same universal primer sequence from WTA. Primer extension using TCR V-region primers and targeted PCR amplification using a second universal primer results in a 3' barcoded single-cell CDR3-enriched library that can be sequenced with custom sequencing primers. Coupled with 3' scRNA-seq of the same WTA, this method enables simultaneous analysis of single-cell transcriptomes and TCR sequences which can help interpret inherent heterogeneity among antigen-specific T cells and salient disease biology. The method presented here can also be adapted readily to enrich and sequence other transcripts of interest from both 3' and 5' barcoded scRNA-seq

关键词： RNA

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DeePKS+ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials

arXiv

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arXiv 2022年

作者： Li, Wenfei Ou, Qi Chen, Yixiao Cao, Yu Liu, Renxi Zhang, Chunyi Zheng, Daye Cai, Chun Wu, Xifan Wang, Han Chen, Mohan Zhang, Linfeng AI for Science Institute Beijing100080 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States HEDPS CAPT College of Engineering School of Physics Peking University Beijing100871 China Department of Physics Temple University PhiladelphiaPA19122 United States Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China DP Technology Beijing100080 China

Recently, the development of machine learning (ML) potentials has made it possible to perform large-scale and long-time molecular simulations with the accuracy of quantum mechanical (QM) models. However, for high-level QM methods, such as density functional theory (DFT) at the meta-GGA level and/or with exact exchange, quantum Monte Carlo, etc., generating a sufficient amount of data for training a ML potential has remained computationally challenging due to their high cost. In this work, we demonstrate that this issue can be largely alleviated with Deep Kohn-Sham (DeePKS), a ML-based DFT model. DeePKS employs a computationally efficient neural network-based functional model to construct a correction term added upon a cheap DFT model. Upon training, DeePKS offers closely-matched energies and forces compared with high-level QM method, but the number of training data required is orders of magnitude less than that required for training a reliable ML potential. As such, DeePKS can serve as a bridge between expensive QM models and ML potentials: one can generate a decent amount of high-accuracy QM data to train a DeePKS model, and then use the DeePKS model to label a much larger amount of configurations to train a ML potential. This scheme for periodic systems is implemented in a DFT package ABACUS, which is open-source and ready for use in various applications. © 2022, CC BY.

关键词： Density functional theory

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Using Cluster Analysis and Dynamic programming for Demand Response Applied to Electricity Load in Residential Homes

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Journal of engineering for Sustainable Buildings and Cities 2020年第1期1卷 011006页

作者： Chanpiwat, Pattanun Gabriel, Steven A. Moglen, Rachel L. Siemann, Michael J. Department of Mechanical Engineering University of Maryland College Park 20742 MD United States Department of Mechanical Engineering and the Applied Mathematics Statistics and Scientific Computation Program University of Maryland College Park 20742 MD United States Department of Industrial Economics and Technology Management Norwegian University of Science and Technology Trondheim NO-7491 Norway Operations Research and Industrial Engineering The University of Texas at Austin Austin 78712 TX United States Resideo Technologies Austin 78702 TX United States

This paper develops means to analyze and cluster residential households into homogeneous groups based on the electricity load. Classifying customers by electricity load profiles is a top priority for retail electric providers (REPs), so they can plan and conduct demand response (DR) effectively. We present a practical method to identify the most DR-profitable customer groups as opposed to tailoring DR programs for each separate household, which may be computationally prohibitive. Electricity load data of 10,000 residential households from 2017 located in Texas was used. The study proposed the clustered load-profile method (CLPM) to classify residential customers based on their electricity load profiles in combination with a dynamic program for DR scheduling to optimize DR profits. The main conclusions are that the proposed approach has an average 2.3% profitability improvement over a business-as-usual heuristic. In addition, the proposed method on average is approximately 70 times faster than running the DR dynamic programming separately for each household. Thus, our method not only is an important application to provide computational business insights for REPs and other power market participants but also enhances resilience for power grid with an advanced DR scheduling tool. Copyright © 2020 by ASME.

关键词： cluster analysis customer classification demand response dynamic programming energy efficiency integrated systems occupant behavior optimization residential household

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First documented evidence of mesoplastic pollution in the Philippines: The case of the sandy beaches in Iligan City

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Regional Studies in Marine science 2024年 75卷

作者： Bacosa, Hernando P. Perpetua, Zy D. Aron, Jemwel B. Bondaug, John Christian S. Cui, Vincent T. Imperial, John Earl Monera, Caryl Mae M. Villariaz, Jay-ar Gabriel, Aiza D. Mayol, Andres Philip Shiu, Ruei-Feng Environmental Science Graduate Program Department of Biological Sciences College of Science and Mathematics Mindanao State University-Iligan Institute of Technology Iligan 9200 Philippines Chemical Engineering Department University of San Agustin Iloilo 5000 Philippines Mindanao State University- Main Campus Sindangan Extension Zamboanga del Norte Sindangan 7100 Philippines Department of Chemical Engineering Mindanao State University-Main Campus Lanao del Sur Marawi 9700 Philippines Department of Mathematics and Natural Sciences College of Arts and Sciences North Eastern Mindanao State University-Tandag Campus Surigao del Sur Tandag 8300 Philippines Department of Manufacturing Engineering and Management Gokongwei College of Engineering De La Salle University Manila Taft Avenue 2401 Philippines Institute of Marine Environment and Ecology National Taiwan Ocean University Keelung 202301 Taiwan Center of Excellence for the Oceans National Taiwan Ocean University Keelung 202301 Taiwan

The problem of marine plastic pollution is multifaceted and a substantial threat to the environment and human well-being. Despite being a leading marine plastic contributor in the world, the prevalence of mesoplastics in the Philippines is yet to be ascertained. The study aimed to quantify and characterize the mesoplastics on the 10 sandy beaches (inside and outside the city center) of Iligan City, Philippines. Samples were extracted, quantified, and characterized based on their morphology and chemical composition. A total of 166 mesoplastics were collected from the ten sampling sites. Fragment mesoplastics accounted for 66.9 % of the plastic debris found across all sites, with sizes in the range of 21–25 mm (31.3 %), and white/transparent (31.1 %) being predominant. Attenuated total reflectance–Fourier transform infrared (ATR–FTIR) spectroscopy identified polyethylene, polyethylene terephthalate, and polypropylene as the dominant mesoplastic polymer. We also found that mesoplastics were significantly more abundant on beaches within the city center (39.67 ± 3.11 items/m²) than on beaches away from the city center (13.02 ± 1.80 items/m²). This study provides a useful baseline of information on mesoplastic distributions in densely populated urban areas. Our results suggest that there is a pressing need to strengthen existing policies on solid waste management and develop new strategies to mitigate plastic pollution. Addressing this issue is of utmost importance considering the potentially harmful effects of mesoplastics on marine organisms and ecosystems. To the best of our knowledge, this is the first report on mesoplastic pollution in the Philippines. © 2024 Elsevier B.V.

关键词： Beach litter City center Coastal barangay Marine plastic litter Plastic pollution

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The potency of hydrothermally prepared sulfated silica (SO4/SiO2) as a heterogeneous acid catalyst for ethanol dehydration into diethyl ether

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Chemosphere 2023年第1期341卷

作者： Pratika, Remi Ayu Wijaya, Karna Utami, Maisari Mulijani, Sri Patah, Aep Alarifi, Saud Ram Mani, Ravishankar Kumar Yadav, Krishna Ravindran, Balasubramani Chung, Woo Jin Chang, Soon Woong Munusamy-Ramanujam, Ganesh Study Program of Chemistry Faculty of Mathematics and Natural Sciences Universitas Palangka Raya Palangka Raya Indonesia Department of Chemistry Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Chemistry Faculty of Mathematics and Natural Sciences Universitas Islam Indonesia Yogyakarta55584 Indonesia Department of Chemistry Faculty of Mathematics and Natural Sciences Institut Pertanian Bogor Bogor Indonesia Department of Chemistry Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Bandung Indonesia Department of Zoology College of Science King Saud University P.O. Box 2455 Riyadh11451 Saudi Arabia Department of Pharmaceutical Biology Faculty of Pharmaceutical Sciences UCSI University Cheras Taman Connaught Kuala Lumpur56000 Malaysia Faculty of Science and Technology Madhyanchal Professional University Ratibad Bhopal462044 India Environmental and Atmospheric Sciences Research Group Scientific Research Center Al-Ayen University Thi-Qar Nasiriyah 64001 Iraq Department of Environmental Energy and Engineering Kyonggi University Yeongtong-Gu Gyeonggi-Do Suwon16227 Korea Republic of Department of Medical Biotechnology and Integrative Physiology Institute of Biotechnology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Thandalam Tamil Nadu Chennai602105 India Molecular Biology and Immunobiology Division Interdisciplinary Institute of Indian System of Medicine SRM-IST Tamil Nadu Kattankulathur603203 India

The dehydration of ethanol into diethyl ether over a SO4/SiO2 catalyst was investigated. The SO4/SiO2 catalysts were prepared by the sulfation method using 1, 2, and 3 M of sulfuric acid (SS1, SS2, and SS3) via hydrothermal treatment. This study is focused on the synthesis of a SO4/SiO2 catalyst with high total acidity that can be subsequently utilized to convert ethanol into diethyl ether. The total acidity test revealed that the sulfation process increased the total acidity of SiO2. The SS2 catalyst (with 2 M sulfuric acid) displayed the highest total acidity of 7.77 mmol/g, whereas the SiO2 total acidity was only 0.11 mmol/g. Meanwhile, the SS3 catalyst (with 3 M sulfuric acid) has a lower total acidity of 7.09 mmol/g due to the distribution of sulfate groups on the surface having reached its optimum condition. The crystallinity and structure of the SS2 catalyst were not affected by the hydrothermal treatment or the sulfate process on silica. Furthermore, The SS2 catalyst characteristics in the presence of sulfate lead to a flaky surface in the morphology and non-uniform particle size. In addition, the surface area and pore volume of the SS2 catalyst decreased (482.56–172.26 m2/g) and (0.297–0.253 cc/g), respectively, because of the presence of sulfate on the silica surface. The SS2 catalyst's pore shape information explains the formation of non-uniform pore sizes and shapes. Finally, the activity and selectivity of SO4/SiO2 catalysts in the conversion of ethanol to diethyl ether yielded the highest ethanol conversion of 70.01% and diethyl ether product of 9.05% from the SS2 catalyst (the catalyst with the highest total acidity). Variations in temperature reaction conditions (175–225 °C) show an optimum reaction temperature to produce diethyl ether at 200 °C (11.36%). © 2023 Elsevier Ltd

关键词： Silica

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Regularity and numerical approximation of fractional elliptic differential equations on compact metric graphs

arXiv

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arXiv 2023年

作者： Bolin, David Kovács, Mihály Kumar, Vivek Simas, Alexandre B. Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Mathematical Sciences Chalmers University of Technology University of Gothenburg GothenburgSE-41296 Sweden Department of Differential Equations Budapest University of Technology and Economics Mauegyetem rkp. 3. BudapestH-1111 Hungary Faculty of Information Technology and Bionics Pázmány Péter Catholic University Práter utca 50/a. BudapestH-1083 Hungary Theoretical Statistics and Mathematics Unit Indian Statistical Institute Bangalore Centre 8th Mile Mysore Road Karnataka Bangalore560059 India

The fractional differential equation Lβu = f posed on a compact metric graph is considered, where β > 0 and L = κ2 − ∇(a∇) is a second-order elliptic operator equipped with certain vertex conditions and sufficiently smooth and positive coefficients κ, a. We demonstrate the existence of a unique solution for a general class of vertex conditions and derive the regularity of the solution in the specific case of Kirchhoff vertex conditions. These results are extended to the stochastic setting when f is replaced by Gaussian white noise. For the deterministic and stochastic settings under generalized Kirchhoff vertex conditions, we propose a numerical solution based on a finite element approximation combined with a rational approximation of the fractional power L−β. For the resulting approximation, the strong error is analyzed in the deterministic case, and the strong mean squared error as well as the L2(Γ×Γ)error of the covariance function of the solution are analyzed in the stochastic setting. Explicit rates of convergences are derived for all cases. Numerical experiments for L = κ2 − ∆, κ > 0 are performed to illustrate the *** Codes 35R02, 35A01, 35A02, 60H15, 60H40 Copyright © 2023, The Authors. All rights reserved.

关键词： Errors

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Modeling the number of confirmed cases of COVID-19 in East Java using negative binomial regression based on least square spline estimator

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AIP Conference Proceedings 2024年第1期3083卷

作者： Arip Ramadan Nur Chamidah I. Nyoman Budiantara Student of Doctoral Study Program of Mathematics and Natural Sciences Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia. Department of Information System School of Industrial and System Engineering Telkom University Surabaya 60231 Indonesia. Department of Mathematics Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia. Research Group of Statistical Modeling in Life Science Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia. Department of Statistics Faculty of Sciences and Data Analytics Sepuluh Nopember Institute of Technology Surabaya 60111 Indonesia.

The East Java Province has experienced a significant surge in number of confirmed cases of COVID-19. This study endeavors to investigate the potential correlation between weather conditions and the incidence of number of confirmed cases of COVID-19 in East Java. To achieve this, a nonparametric regression model, specifically, the Negative Binomial Regression (NBR) model based on the least squares spline estimator, was utilized. The outcomes of the study indicate that the Mean Absolute Percentage Error (MAPE) of nonparametic regression model is 0.30. Meanwhile, the MAPE for the parametric regression model is 0.34. The results show that a nonparametric regression model approach is better than parametric regression model approach. The study establishes that the truncated spline estimator based NBR model represents the best fit, with an MLCV value of -256.71. The findings of the study suggest that a temperature less than 21.75°C is associated with a decrease of 13.28 number of confirmed cases of COVID-19 per each 1°C increase, while a temperature between 21.75°C and 25.78°C is linked to an increase of 6.85 number of confirmed cases of COVID-19 per each 1°C increase. In contrast, a temperature greater than 25.78°C is associated with a decrease of 139.42 number of confirmed cases of COVID-19 per each 1°C increase. Similarly, a wind speed less than 5.57 m/s is related to a decrease of 12.99 number of confirmed cases of COVID-19 per each 1 m/s increase, whereas a wind speed between 5.57 m/s and 8.99 m/s is associated with a decrease of 10.29 number of confirmed cases of COVID-19 per each 1 m/s increase. Furthermore, a wind speed greater than 8.99 m/s is linked to a decrease of 19.16 number of confirmed cases of COVID-19 per each 1 m/s increase. The study provides evidence that higher temperatures and wind speeds result in a slower rise in the incidence of the number of confirmed cases of COVID-19. Consequently, it is recommended that the local government remains vigilant du

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A call to action to address escalating global threats to academic research

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The Innovation 2025年第4期6卷 17-19页

作者： Gaelle Piret Fun Man Fung Josie Fullerton Giuseppe Fico Dmitriy Ponkratov Wenxin Chen Daniela Latorre Kirsty Y.Wan Nima Aghaeepour Jules Welgryn Adeel Razi Patricia Silveyra Ahmet Altun Renata Z.Jurkowska Alice C.Hughes Joy Wolfram INSERM U1205 UGA38400 Saint Martin d’HèresFrance School of Chemistry University College DublinD04 C1P1 BelfieldIreland UCD Geary Institute for Public Policy University College DublinD04 N9Y1 DublinIreland School of Cardiovascular&Metabolic Health University of GlasgowG128TA GlasgowUK European Alliance for Medical and Biological Engineering and Science 3001 LeuvenBelgium Life Supporting Technologies Universidad Politécnica de Madrid28040 MadridSpain Siemens Digital Industries Software B908BG SolihullUK Department of Philosophy of Science and Technology University of Science and Technology of ChinaHefei 230026China Institute of Microbiology ETH Zurich8093 ZurichSwitzerland Living Systems Institute&Department of Mathematics and Statistics University of ExeterEX44QD ExeterUK Department of Anesthesiology Painand Perioperative MedicineDepartment of PediatricsDepartment of Biomedical Data ScienceStanford University School of MedicineStanfordCA 94305USA UniversitéParis Nanterre 92000 NanterreFrance Turner Institute for Brain and Mental Health School of Psychological Sciencesand Monash Biomedical ImagingMonash UniversityClaytonVIC 3800Australia Wellcome Centre for Human Neuroimaging University College LondonWC1N 3AR LondonUK CIFAR Azrieli Global Scholars Program TorontoON M5G 1M1Canada Department of Environmental and Occupational Health Indiana University School of Public Health BloomingtonBloomingtonIN 47408USA Department of Pharmacology Sivas Cumhuriyet UniversitySivas 58140Turkey Division of Biomedicine School of BiosciencesCardiff UniversityCF103AX CardiffUK School of Biological Sciences University of Hong KongHong Kong SAR 999077China Australian Institute for Bioengineering and Nanotechnology The University of QueenslandBrisbaneQLD 4072Australia School of Chemical Engineering The University of QueenslandBrisbaneQLD 4072Australia

This article is a call to action to address escalating threats to scientific progress that affect academic researchers across the *** threats include public mistrust of science,challenges in translating academic research to end-user applications,a disconnect between academics and policymakers,emerging barriers to international collaboration,and a reliance on conventional metrics to evaluate academic *** article presents various calls to action informed by exemplary approaches across the globe that serve as frameworks to drive beneficial transformation for researchers,academic institutions,and society.

关键词： academic research calls action global threats public mistrust sciencechallenges translating academic research disconnect academics policymakers call action translation research

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A deep potential model with long-range electrostatic interactions

arXiv

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arXiv 2021年

作者： Zhang, Linfeng Wang, Han Muniz, Maria Carolina Panagiotopoulos, Athanassios Z. Car, Roberto Weinan, E. DP Technology Beijing China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China HEDPS CAPT College of Engineering Peking University Beijing100871 China Department of Chemical and Biological Engineering Princeton University PrincetonNJ08544 United States Department of Chemistry Department of Physics Program in Applied and Computational Mathematics Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ08544 United States School of Mathematical Sciences Peking University Beijing100871 China AI for Science Institute Beijing China Department of Mathematics and Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

Machine learning models for the potential energy of multi-atomic systems, such as the deep potential (DP) model, make possible molecular simulations with the accuracy of quantum mechanical density functional theory, at a cost only moderately higher than that of empirical force fields. However, the majority of these models lack explicit long-range interactions and fail to describe properties that derive from the Coulombic tail of the forces. To overcome this limitation we extend the DP model by approximating the long-range electrostatic interaction between ions (nuclei+core electrons) and valence electrons with that of distributions of spherical Gaussian charges located at ionic and electronic sites. The latter are rigorously defined in terms of the centers of the maximally localized Wannier distributions, whose dependence on the local atomic environment is modeled accurately by a deep neural network. In the deep potential long-range (DPLR) model, the electrostatic energy of the Gaussian charge system is added to short-range interactions that are represented as in the standard DP model. The resulting potential energy surface is smooth and possesses analytical forces and virial. Missing effects in the standard DP scheme are recovered, improving on accuracy and predictive power. By including long-range electrostatics, DPLR correctly extrapolates to large systems the potential energy surface learned from quantum mechanical calculations on smaller systems. We illustrate the approach with three examples, the potential energy profile of the water dimer, the free energy of interaction of a water molecule with a liquid water slab, and the phonon dispersion curves of the NaCl crystal. © 2021, CC BY.

关键词： Electrostatics

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Innovation and utilization of coffee skin waste (Coffea robusta L) as a superior compost for the growth of pepper cuttings (Piper nigrum L.)

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AIP Conference Proceedings 2025年第1期3186卷

作者： Awalul Fatiqin Dewi Lestari R. A. Hoetary Tirta Amalia Riri Novita Sunarti Ike Apriani Anita Restu Puji Raharjeng Rr Mini Sari Wulan Thathit Suprayogi Yahya Febrianto Sudarman Rahman Shesanthi Citrariana Rokiy Alfanaar Mu’afa Purwa Arsana Yithro Serang Wansadaj Jaroenram Biology Study Program Faculty of Mathematics and Natural Sciences Palangka Raya University Palangka Raya Central Kalimantan Indonesia Biology Study Program Faculty of Science and Technology Universitas Islam Negeri Raden Fatah Palembang Palembang South Sumatra Indonesia Education Biology Study Program Faculty of Teacher Training and Education Universitas Islam Negeri Raden Fatah Palembang Palembang South Sumatra Indonesia Physics Study Program Faculty of Mathematics and Natural Sciences Palangka Raya University Palangka Raya Central Kalimantan Indonesia Pharmacy Study Program Faculty of Mathematics and Natural Sciences Palangka Raya University Palangka Raya Central Kalimantan Indonesia Chemistry Study Program Faculty of Mathematics and Natural Sciences Palangka Raya University Palangka Raya Central Kalimantan Indonesia Mathematics Study Program Faculty of Mathematics and Natural Sciences Palangka Raya University Palangka Raya Central Kalimantan Indonesia Department of applied chemistry Chaoyang University of Technology Taichung City Taiwan National center for genetic engineering and biotechnology (BIOTEC) Pathum Thani Thailand

This study aimed to determine the potential of coffee skin waste (Coffea robusta L) as a superior compost innovation for the growth of pepper-cutting seedlings (Piper nigrum L). This study used an experimental method with a completely randomized design using five treatments and five repeats, variations in the dose of coffee skin waste used P0 (control), P1 (90 g), P2 (135 g), P3 (180 g), and P4 (225 g). The shoot height, number of leaves, number of roots, and length of roots were measured. The results showed that coffee skin waste compost significantly affected shoot height, especially at P1 (90 g), resulting in an average growth height of 7.24 cm. In addition, the highest number of leaves was found at a concentration of P1 (90 g), with an average of two leaves. Although there was no significant effect on the number of roots, the concentration of P1 (90 g) yielded the best results, with an average of 33 root blades, and the best root length was obtained at the concentration of P1 (90 g), with an average of 44.6 cm. Therefore, coffee skin waste significantly influenced shoot height and the number of leaves on pepper-cut seedlings (Piper nigrum L.) in the P1 treatment (90 g).

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