Hasil untuk "Chemical technology"

M. Manning, K. Patel, R. Borchardt

L. Zeman, A. Zydney

W. Seider, J. D. Seader, D. Lewin et al.

A. Straathof, S. Panke, A. Schmid

L. Szente, J. Szejtli

A. Dalebrook, Weijia Gan, Martin Grasemann et al.

Hui Zhang, Hong Wang, Xun Zhu et al.

B. Tiss, D. Martínez-Martínez, C. Mansilla et al.

Yernazar Bolat, Iain Murray, Yifei Ren et al.

The growing adoption of IoT applications has led to increased use of low-power microcontroller units (MCUs) for energy-efficient, local data processing. However, deploying deep neural networks (DNNs) on these constrained devices is challenging due to limitations in memory, computational power, and energy. Traditional methods like cloud-based inference and model compression often incur bandwidth, privacy, and accuracy trade-offs. This paper introduces a novel Decentralized Distributed Sequential Neural Network (DDSNN) designed for low-power MCUs in Tiny Machine Learning (TinyML) applications. Unlike the existing methods that rely on centralized cluster-based approaches, DDSNN partitions a pre-trained LeNet across multiple MCUs, enabling fully decentralized inference in wireless sensor networks (WSNs). We validate DDSNN in a real-world predictive maintenance scenario, where vibration data from an industrial pump is analyzed in real-time. The experimental results demonstrate that DDSNN achieves 99.01% accuracy, explicitly maintaining the accuracy of the non-distributed baseline model and reducing inference latency by approximately 50%, highlighting its significant enhancement over traditional, non-distributed approaches, demonstrating its practical feasibility under realistic operating conditions.

Dongxing Yu, Bing Han, Xinyi Zhao et al.

Detecting dynamic and amorphous objects like fire and smoke poses significant challenges in object detection. To address this, we propose Dual-Path Cascade Stochastic DETR (Dual-Path CSDETR). Unlike Cascade DETR, our model introduces cascade stochastic attention (CSA) to model the irregular morphologies of fire and smoke through variational inference, combined with a dual-path architecture that enables bidirectional feature interaction for enhanced learning efficiency. By integrating object-centric priors from bounding boxes into each decoder layer, the model refines attention mechanisms to focus on critical regions. Experiments show that Dual-Path CSDETR achieves 94% AP50 on fire/smoke detection, surpassing deterministic baselines.

J. Britton, S. Majumdar, G. Weiss

LIU Chao, ZHAO Liangzhong, WANG Yaoqiong et al.

Objective: Obtaining hhigh-yield pprotease-pproducing bacterial resources through fermentation of advantageous protease-producing strains derived from Shiping stinky tofu. Methods: Single-factor and response surface experiments were used to optimize the culture medium and fermentation conditions. Using casein as the substrate, the effects of temperature, pH, metal ions and organic reagent on the protease activity and stability of strain JX-11 were investigated using enzymology techniques. Results: One strain of high-producing proteinase-secreting bacteria, JX-11, was isolated from Shiping stinky tofu, identified as <i>Chryseobacterium pennipullorum</i>. The optimal conditions for protease production by JX-11 strain were found to be temperature at 23.0 ℃, glucose content at 6.7 g/L, peptone content at 15.0 g/L, pH 6.4. Under these conditions, the protease activity was (39.16±3.24) U/mL. The optimal temperature for extracellular protease of JX-11 strain was 30 ℃, and it had good stability in the range of 10～40 ℃. The optimal pH was 7.0, and it had good stability in the range of pH 6.0～9.0. Mn²⁺ significantly increased the activity of JX-11 protease. The relative activity was increased by 4.33 times compared with the blank group. Zn²⁺, Cu²⁺, and K⁺ all inhibited the enzyme activity, Na⁺, Mg²⁺, and Ca²⁺ had no significant effect. Glycerol can promote the activity of the enzyme, but ethanol and acetone have little effect on it. Tween 80, acetic acid, methanol and EDTA inhibited the activity of the enzyme, among which EDTA hadd the most obvious inhibition, which further proved that the enzyme was a metalloprotease. Conclusion: A high-producing protease-secreting bacterial strain is obtained from Shiping stinky tofu, which has good application prospects.

Yulia M. T. A. Putri, Muhammad I. Syauqi, Isnaini Rahmawati et al.

Abstract The growing demand for sustainable energy sources has spurred significant studies to optimize the potency of fuel cell technology. Direct urea fuel cell (DUFC) has gained attention due to their energy density, eco‐friendliness, and potential applications in power generation using urea from various sources, including wastewater and urine. Efficient electrocatalysts are pivotal in DUFCs, and nickel‐based catalysts, particularly in the form of NiOOH, have demonstrated cost effectiveness, excellent stability in alkaline media, and good activity for urea oxidation reaction. However, low‐density and durability are still the major limitation in the overall DUFC performance. This review provides a comprehensive analysis of the latest development in Ni‐based catalysts, covering synthesis methods, factors influencing the catalytic activity as well as their implications for DUFC performance durability and commercial viability. In addition, another important factor including the use of different oxidant and electrolyte medium is also elaborated. Based on this review, 2D‐3D Ni‐based materials with the addition of other metals and the use of non‐oxide nickel binary compound are predicted to be the future evolution of the effective nickel‐based catalysts.

Xinye Sun, Yanzhe Shang, Binghao Zhang et al.

Abstract Poly(3-hydroxybutyrate-co-lactate) [P(3HB-co-LA)] is a highly promising valuable biodegradable material with good biocompatibility and degradability. Vibrio natriegens, owing to its fast-growth, wide substrate spectrum characteristics, was selected to produce P(3HB-co-LA). Herein, the crucial role of acetyltransferase PN96-18060 for PHB synthesis in V. natriegens was identified. Heterologous pathway of P(3HB-co-LA) was introduced into V. natriegens successfully, in addition, overexpression of the dldh gene led to 1.84 fold enhancement of the lactate content in P(3HB-co-LA). Finally, the production of P(3HB-co-LA) was characterized under different carbon sources. The lactate fraction in P(3HB-co-LA) was increased to 28.3 mol% by the modification, about 1.84 times of that of the control. This is the first successful case of producing the P(3HB-co-LA) in V. natriegens. Collectively, this study showed that V. natriegens is an attractive host organism for producing P(3HB-co-LA) and has great potential to produce other co-polymers.

Matteo Iaiani, Alessandro Tugnoli, Valerio Cozzani

The increasing interconnectivity with external networks and the higher reliance on digital systems make chemical and process industries, including waste and drinking water treatment plants, more vulnerable to cyber-attacks. Historical evidence shows that these attacks have the potential to cause events with severe consequences on property, people, and the surrounding environment, posing a serious threat. While the risks deriving from the malicious manipulation of the Basic Process Control System (BPCS) and the Safety Instrumented System (SIS) in chemical and Oil&Gas facilities have been systematically analysed in the available literature, including previous works of the Authors, the analysis of the consequences of cyber-attacks to drinking water treatment plants has not been conducted to date. To fill this gap, in the present study the methodology POROS 2.0 (Process Operability Analysis of Remote manipulations through the cOntrol System) developed by the Authors was applied to a drinking water treatment plant, providing valuable insights on possible critical scenarios originated by cyber-attacks in these facilities.

Qinhao Liu, Siyu Yao, Siyuan Ma et al.

Few studies are concerned with the effect of the conjugat protein on the bioactivities of the abalone gonad polysaccharide (AGP). In this study, a series of treatments, including raw material (female and male) defatting, extraction temperature (25–121 °C), proteolysis, ultrafiltration, and ethanol precipitation, was conducted to investigate the role of the conjugate protein on AGP anticoagulant activity. All AGP extracts significantly prolonged activated partial thromboplastin time (APTT) and thrombin time (TT). The strongest was observed in the female AGPs prepared at 50 and 121 °C. The most active is located at 30–300 kDa by ultrafiltration. After being exposed to neutral protease, quick shortening of APTT and TT was found in all AGPs. Further ethanol precipitating of found the longest APTT in the sediment, which contains most polysaccharides and proteins. Defatting lowered the activity of female AGP but increased that of males. Proteolysis also significantly weakened the clotting factor inhibition effect of the 50 °C female AGP, but heating seemed not affect the effect. Five fractions were obtained after the 50 °C female AGP was subjected to ion exchange column. Fraction V, with the highest protein and medium polysaccharide content, showed the strongest anticoagulant effect and was also much higher than AGSP, which was obtained by multi-step proteolysis. The findings supported positive effect of the conjugate protein in AGP anticoagulant activity.

LI Peixuan, GE Xinsheng, TIAN Yadong et al.

Purposes To investigate the influence of different tamping methods on the reinforcement effect of sand dynamic compaction, this research has been done from the perspective of soil stress distribution characteristics. Methods The dynamic compaction model test was carried out in the outdoor field to monitor the vertical and radial soil pressures in the soil and falling weight acceleration during tamping process, and the displacements in the soil were analyzed by numerical simulation method. Findings It is concluded that in the construction of dynamic compaction method, the vertical soil pressure waveform is the shock wave waveform or the vibration attenuation wave shape, and the radial earth pressure waveform is the impact waveform; There are two reinforcement modes in the process of dynamic compaction, the soil under tamping point is mainly vertically compacted, while the soil side of tamping point also has significant radial compaction; At the same level of dynamic compaction, heavy falling weight is suitable for deep soil and radial soil reinforcement, while light falling weight is suitable for rapid reinforcement of shallow soil. Conclusions The research results have a certain guiding significance for the selection of tamping methods of the same energy level dynamic compaction.

Anastasiia Kudriavtseva, Stefan Jarić, Nikita Nekrasov et al.

Graphene-based materials are actively being investigated as sensing elements for the detection of different analytes. Both graphene grown by chemical vapor deposition (CVD) and graphene oxide (GO) produced by the modified Hummers’ method are actively used in the development of biosensors. The production costs of CVD graphene- and GO-based sensors are similar; however, the question remains regarding the most efficient graphene-based material for the construction of point-of-care diagnostic devices. To this end, in this work, we compare CVD graphene aptasensors with the aptasensors based on reduced GO (rGO) for their capabilities in the detection of NT-proBNP, which serves as the gold standard biomarker for heart failure. Both types of aptasensors were developed using commercial gold interdigitated electrodes (IDEs) with either CVD graphene or GO formed on top as a channel of liquid-gated field-effect transistor (FET), yielding GFET and rGO-FET sensors, respectively. The functional properties of the two types of aptasensors were compared. Both demonstrate good dynamic range from 10 fg/mL to 100 pg/mL. The limit of detection for NT-proBNP in artificial saliva was 100 fg/mL and 1 pg/mL for rGO-FET- and GFET-based aptasensors, respectively. While CVD GFET demonstrates less variations in parameters, higher sensitivity was demonstrated by the rGO-FET due to its higher roughness and larger bandgap. The demonstrated low cost and scalability of technology for both types of graphene-based aptasensors may be applicable for the development of different graphene-based biosensors for rapid, stable, on-site, and highly sensitive detection of diverse biochemical markers.

Fen Li, Yu-Ting Si, Jia-Wei Tang et al.

WHO classified Helicobacter pylori as a Group I carcinogen for gastric cancer as early as 1994. However, despite the high prevalence of H. pylori infection, only about 3 % of infected individuals eventually develop gastric cancer, with the highly virulent H. pylori strains expressing cytotoxin-associated protein (CagA) and vacuolating cytotoxin (VacA) being critical factors in gastric carcinogenesis. It is well known that H. pylori infection is divided into two types in terms of the presence and absence of CagA and VacA toxins in serum, that is, carcinogenic Type I infection (CagA+/VacA+, CagA+/VacA-, CagA-/VacA+) and non-carcinogenic Type II infection (CagA-/VacA-). Currently, detecting the two carcinogenic toxins in active modes is mainly done by diagnosing their serological antibodies. However, the method is restricted by expensive reagents and intricate procedures. Therefore, establishing a rapid, accurate, and cost-effective way for serological profiling of carcinogenic H. pylori infection holds significant implications for effectively guiding H. pylori eradication and gastric cancer prevention. In this study, we developed a novel method by combining surface-enhanced Raman spectroscopy with the deep learning algorithm convolutional neural network to create a model for distinguishing between serum samples with Type I and Type II H. pylori infections. This method holds the potential to facilitate rapid screening of H. pylori infections with high risks of carcinogenesis at the population level, which can have long-term benefits in reducing gastric cancer incidence when used for guiding the eradication of H. pylori infections.

Dániel Gosztola, Peter Grubits, János Szép et al.

The growing importance of numerical simulations in the welding industry stems from their ability to enhance structural performance and sustainability by ensuring optimal manufacturing conditions. The use of the finite element method (FEM) allows for detailed and precise calculations of the mechanical and material changes caused by the welding process. Acquiring knowledge of these parameters not only serves to augment the quality of the manufacturing process but also yields consequential benefits, such as reducing adverse effects. Consequently, the enhancement of structural performance and prolonged lifespan becomes achievable, aligning with overarching sustainability goals. To achieve this goal, this paper utilizes numerical simulations of welding processes based on experimental tests, with a specific focus on analyzing temperatures generated within the structures. In the finite element analysis (FEA), a total of 12 welding cycles were systematically modeled to align with experimental conditions, incorporating cooling intervals, preheating considerations, and the relevant section of the connecting concrete structure with studs. The outcomes of this research exemplify the potential of numerical simulation in the welding industry, demonstrating a diverse range of results achieved through FEA to enhance the quality of structures within the context of sustainability.