Institute of Engineering Science

Background: The study of ultra-high diluted (UHD) solutions is a compelling and controversial domain in modern science. Despite numerous investigations and hypotheses, there is a lack of rigorous, reproducible empirical evidence supporting the existence of molecular footprints beyond the Avogadro's limit. Objective: This study conducted a detailed investigation of the molecular and electrical properties, focusing on hydrogen bonding and impedance to understand the fundamental nature of UHD solutions. Materials and methods: Combined impedance and infrared spectroscopic investigations were performed to probe the structural behaviour of relevant water networks at the molecular level of UHD alcoholic extract of Arnica montana (AM) and Rhus toxicodendron (RT). Results: At higher potencies (200C and 1M) of both AM and RT, an interesting correlation was observed between impedance and spectral characteristics. An increase in impedance was observed with potencies for both AM (93.9%) and RT (49%), which was attributed to changes in the hydrogen bonding network and molecular reorganisation during potentisation. This correlates with the Mid-IR spectra, showing increased hydrogen bond length and potential weakening of inter-molecular hydrogen bonds with increasing potency. Also, Far-IR spectra indicate a contrast in the system's enthalpic gain with increased potencies for both AM (29.8%) and RT (65.2%). Conclusion: Distinct variations in spectral and electrical parameters have been observed for AM and RT due to potentisation, suggesting that these solutions retain structural and molecular traits despite extreme dilutions. This notion may revisit traditional homoeopathic treatment approaches by addressing conflicting paradigms regarding the presence of molecular footprints in UHD solutions.

Lip reading is the process of comprehending speech by interpreting lip movements. Because it can be used in audiovisual speech recognition, optimization, and separation, it has drawn much attention. Traditional solutions mainly relied on CNNs like ResNet in order to extract spatial information from the video frames. However, CNNs do not perform satisfactorily in capturing temporal correlations, which will make multi-modal systems more computationally expensive and increase latency. We combine RNNs and LSTMs when modeling temporal changes, which also face scaling challenges. In this paper, we propose DeepLip, a unified CNN-RNN- LSTM architecture for end-to-end visual speech recognition. DeepLip effectively integrates the feature of spatial property extraction with temporal embeddings. These embeddings leverage the strengths of both convolutional and recurrent layers to model both local and sequential dynamics well. They therefore work well for alignment-based training with CTC Loss that enables word and sentence level recognition at high levels. Our experiments, based on two datasets, English LRW and Mandarin LRW-1000, show that DeepLip outperforms the current state-of-the-art while being more efficient and cheaper to run

Vascular networks are essential for the long-term survival of implanted grafts. Under physiological conditions, these networks include arteries, veins, and capillaries, which transport vital fluids, gases, and nutrients – such as blood, oxygen, carbon dioxide, and minerals – to the cells. However, creating vascular channels within bioengineered grafts remains a significant engineering challenge. In this study, we investigated an alternative biomanufacturing method for generating straight and curved vascular channels by employing a needle navigation process, which mimics the vascularization process inside a biocompatible polymer poly(vinyl alcohol) gel. The stability of the channels fabricated via this process was studied. Cells were seeded inside the manufactured artificial vascular channels, and their viability was measured over time. These results indicate the preliminary potential of the needle navigation method for the fabrication of viable vascular-like conduits in bioengineered grafts.

Serotonin is one of the crucial neurotransmitters that plays a vital role in human physiology and normally exists in human serum in the 200 nM to 1.1 μM range along with a wide variety of interfering agents. This makes it a quite challenging task to detect serotonin in an efficient, electrochemical way at ambient temperature in a relatively low concentration (nanomolar) of abundance. So, this work is focused on developing an enzyme-less electrochemical sensing platform that can detect serotonin with a very low limit of detection (LOD) in a highly sensitive and selective way. For this purpose, carbon quantum dots (CQDs), one of the advanced carbonaceous materials, were prepared from a natural source (corn seeds) and applied to make a nanocomposite sensing platform with an electrochemically deposited NiWO4 thin film on FTO-coated glass substrate. The prepared materials were thoroughly characterized by using sophisticated instrumentation techniques. The fabricated NiWO4/CQD nanocomposite thin film was then subjected to detailed electrochemical probing toward serotonin sensing. Fascinatingly, the developed sensor prototype yielded an LOD of 134.0 nM without compromising the sensitivity (16.9 μA μM–1 cm–2), which makes it well capable for detecting serotonin that is present in human serum in the nM to μM order.

Abstract Multi-access edge computing (MEC) has emerged as a promising paradigm to reduce communication overhead and computational burden on mobile users (MUs) by offloading resource-intensive applications to nearby multi-access edge servers (MESs). This strategy significantly lowers energy consumption and execution latency. However, most existing studies on MEC assume an ideal scenario in which MESs are already optimally deployed, typically co-located with every base station (BS) in fifth-generation (5G) networks. While deploying an MES at each BS ensures low-latency services and high quality of service (QoS), it introduces several practical limitations. These include high capital and operational expenditures for service providers, underutilised MESs in sparsely populated regions, and elevated service costs for end users due to continuous MES activation regardless of demand. To address these challenges, this work proposes a novel Delay-based Categorisation and Adaptive MES-Activation (DCAMA) approach aimed at optimising MES activation in beyond-5G (B5G) wireless networks. Rather than assuming static and full MES deployment, the DCAMA strategy dynamically determines the minimum number of MESs required based on current network workload and task urgency. This energy-aware and cost-efficient method enables scalable MES activation without compromising service performance .

Polusi udara di lingkungan industri farmasi sering didominasi oleh partikulat halus (PM) dan gas berbahaya seperti karbon monoksida (CO) yang dapat mengganggu kesehatan pekerja dan kualitas produk. Penelitian ini bertujuan mengembangkan bio-hybrid HEPA filter berbasis kertas daur ulang, serabut kelapa, dan arang aktif sebagai alternatif filter ramah lingkungan, ekonomis, dan efektif untuk pengendalian PM hingga ukuran 0,25 µm dan gas CO. Prototipe filter disusun dalam tiga lapisan: penyaring mekanik (kertas daur ulang), penyaring berpori alami (serabut kelapa), dan lapisan adsorpsi (arang aktif). Uji performa dilakukan menggunakan particle counter dan pengujian gas sederhana. Hasil menunjukkan bahwa kombinasi ketiga material menghasilkan efisiensi filtrasi PM2.5 sebesar 90,21% dan penurunan konsentrasi CO sebesar 94,88%, lebih tinggi dibandingkan filter berbahan tunggal. Peningkatan ini terutama dipengaruhi oleh luas permukaan dan volume pori arang aktif yang mendukung mekanisme adsorpsi gas. Temuan ini menunjukkan bahwa bio-hybrid filter memiliki potensi sebagai media filtrasi alternatif yang efektif, berkelanjutan, dan dapat diterapkan pada fasilitas industri farmasi skala kecil hingga menengah. Produk ini juga berpeluang dikembangkan sebagai solusi penyaring udara berbasis biomassa lokal yang mudah diproduksi dan ramah lingkungan.

Light of long wavelengths is absorbed almost immediately with depth, while remaining photons are scattered by suspended particles, producing images that are bright, color‑distorted, and structurally blurred. Underwater imaging is therefore a grand challenge across diverse marine environments. Existing enhancement methods fail to balance image fidelity and speed or depend on large, environment‑specific training sets that lack generalization. We propose a frequency‑based quantum‑classical pipeline that uses the Quantum Fourier Transform (QFT) to optimize underwater images without extensive data. The luminance and chroma channels are encoded into quantum states; QFT reveals the spectral content. In the quantum frequency domain, we suppress low‑frequency illumination artifacts and enhance high‑frequency edges and fine details. The modified spectrum is decoded back into classical space in a manner compatible with Noisy Intermediate‑Scale Quantum (NISQ) devices. Experiments on standard underwater datasets show significant improvements in visibility, color balance, and edge sharpness compared to traditional Fourier methods and deep‑learning models. This work demonstrates that quantum spectral processing is an efficient, multi‑productive tool for underwater image enhancement, providing substantial gains in underwater visual perception notably, and for marine navigation and research, in challenging visibility conditions for autonomous underwater vehicles.

Kegiatan Pengabdian kepada Masyarakat ini bertujuan untuk meningkatkan kapasitas kewirausahaan masyarakat melalui pelatihan sosial media marketing dan desain visual produk hidroponik. Sasaran kegiatan adalah kelompok masyarakat pelaku usaha hidroponik skala rumah tangga yang masih menghadapi keterbatasan dalam strategi pemasaran digital dan pengemasan visual produk. Metode pelaksanaan meliputi penyuluhan, pelatihan praktik langsung, pendampingan, serta evaluasi hasil kegiatan. Materi pelatihan mencakup pemanfaatan media sosial sebagai sarana promosi, pembuatan konten pemasaran, fotografi produk sederhana, serta perancangan desain visual yang menarik dan informatif. Hasil kegiatan menunjukkan adanya peningkatan pemahaman dan keterampilan peserta dalam mengelola akun media sosial usaha, membuat konten promosi yang kreatif, serta menyusun desain visual produk hidroponik yang lebih profesional. Peserta juga menunjukkan peningkatan motivasi dan kepercayaan diri dalam memasarkan produk secara mandiri. Kegiatan ini berkontribusi terhadap penguatan ekonomi masyarakat dan pengembangan kewirausahaan berbasis pertanian berkelanjutan. Dengan demikian, pelatihan sosial media marketing dan desain visual produk hidroponik efektif sebagai upaya pemberdayaan masyarakat dalam meningkatkan daya saing usaha dan kesejahteraan ekonomi.

Sosialisasi alur pelayanan rawat jalan bagi pasien Program Rujuk Balik (PRB) serta penggunaan aplikasi digital seperti Mobile JKN berperan penting dalam meningkatkan efisiensi dan kepuasan pasien. Namun, di RSKIA Harapan Bunda Bandung, masih ditemukan berbagai kendala, khususnya terkait pemahaman pasien terhadap alur PRB dan rendahnya pemanfaatan aplikasi Mobile JKN, meskipun sebagian besar pasien telah mengunduhnya. Kegiatan ini bertujuan untuk meningkatkan pemahaman pasien PRB mengenai alur layanan rawat jalan serta mendorong penggunaan aplikasi Mobile JKN secara optimal. Sosialisasi dilakukan melalui pendekatan edukatif seperti penyuluhan langsung, simulasi penggunaan aplikasi, dan pendampingan oleh tenaga terlatih di area layanan rumah sakit. Sebelum intervensi, hanya 15% pasien yang memanfaatkan Mobile JKN meskipun 90% telah menginstalnya. Setelah kegiatan, terjadi peningkatan pemahaman alur PRB dan antusiasme dalam penggunaan Mobile JKN, yang berdampak pada berkurangnya antrean di loket pendaftaran dan peningkatan kepuasan pasien. Sosialisasi ini menjadi langkah strategis dalam mendukung transformasi layanan digital dan penguatan tata kelola PRB di fasilitas kesehatan.

Rekam medis adalah dokumen yang memuat rincian identitas pasien, pengobatan, perawatan, tindakan, dan layanan tambahan yang diberikan kepada pasien. Unit Rekam Medis di Puskesmas Kutowinangun terdiri dari tiga petugas rekam medis dengan kualifikasi pendidikan yang beragam. Data tahun 2024 menunjukkan total pengunjung sebanyak 43.832 orang. Penelitian ini bertujuan untuk mengetahui kebutuhan sumber daya manusia di Unit Rekam Medis UPTD Puskesmas Kutowinangun. Penelitian ini menggunakan deskriptif kuantitatif dan menggunakan laporan sekunder dari pihak terkait serta instrumen kalkulator untuk menukar variabel waktu, selain itu penelitian ini Menggunakan Metode ABK-Kes. Pusat kesehatan tersebut hanya mempekerjakan 3 petugas rekam medis, dengan 1 orang memiliki latar belakang pendidikan rekam medis. Dari hasil perhitungan, Unit Rekam Medis Puskesmas Kutowinangun membutuhkan 6 sumber daya manusia untuk memenuhi standar beban kerja sesuai dengan pekerjaan. Unit Rekam Medis Puskesmas Kutowinangun membutuhkan 6 sumber daya manusia untuk memenuhi standar beban kerja sesuai dengan deskripsi pekerjaan.