Minimal Processing Research Articles

Simulation of a liquid drop on a soft substrate.

A liquid drop resting on a soft substrate is numerically simulated as an energy minimization problem. The elastic substrate is modeled as a cubic lattice of mass-springs, to which an energy term controlling the change of volume is associated. The interfacial energy between three phases of solid, liquid, and vapor is also introduced. Under the constant volume constraint of the liquid drop, the total energy of the system is subjected to a numerical minimization process by which profiles of both substrate and drop are obtained. The numerical simulation enables the modeling of the wetting setup with various parameters associated with solid and liquid phases, including the Young's modulus and Poisson's ratio of the solid, the surface tension of the three phases, and geometrical parameters such as the contact radius or thickness of the solid. The direct outputs of the minimization process are the displacements of the solid lattice and boundary points of the liquid, by which the behavior of all relevant quantities, such as contact angles in the three phases, as well as the effective surface tension of the solid, can be quantitatively studied. The resulting displacements of the solid are compared with the exact solution of the elasticity equation under the assumption of no tangential traction, and a quite satisfactory agreement is observed. However, at larger Young's modulus or lower Poisson's ratios the agreement between the numerical results and the analytical solutions is lost in the vicinity of the contact points. Interestingly, a non-zero tangential traction in the vicinity of contact points is calculated by the numerical outputs, indicating that the assumption of zero tangential traction is not valid generally around the contact points. The effective surface tension at the contact points is calculated for an incompressible solid substrate, showing a linear increase with respect to the Young's modulus of the solid, as .

Resource recovery from wastewater by directing microbial metabolism toward production of value-added biochemicals.

Dynamic oxygen fluctuations in activated sludge were investigated to enhance valuable biochemical production during wastewater treatment. Batch experiments compared constant aeration with rapid cycling between oxygen-rich and oxygen-poor states. Fluctuating oxygen concentrations (0-2 mg/L) significantly increased production of valuable biochemicals compared to constant oxygen concentration (2 mg/L). Continuous oxygen perturbations increased free amino acids by 35.7 ± 7.6 % and free fatty acids by 76.4 ± 13.0 %, while intermittent perturbations with anoxic periods enhanced free amino acids by 42.4 ± 8.1 % and free fatty acids by 39.3 ± 7.7 %. Fourteen standard amino acids showed significant increases, and most fatty acids had carbon chain lengths between C12-C22. Mechanistically, oxygen perturbations activated FNR and ArcA regulons, resulting in lower relative abundances of TCA cycle enzymes and higher abundances of amino acid and fatty acid biosynthetic enzymes. These findings demonstrate that controlled oxygen fluctuations in wastewater treatment can enhance the biochemical value of activated sludge with minimal process modifications, facilitating resource recovery.

A transactive energy cooperation scheduling for hydrogen-based community microgrid with refueling preferences of hydrogen vehicles

Hydrogen, as a high specific energy green carrier, is promising for power generation and transportation. This paper proposes a transactive energy cooperation framework for an end-user-oriented hydrogen-based community microgrid considering refueling preferences of hydrogen vehicles (HVs) and industrial hydrogen demand to minimize social cost. In this framework, an aggregator obtains stacking profits in both the electricity and hydrogen markets through flexible interconversion, storage, and interaction of electricity and hydrogen. For resident users, the refueling preferences of HVs are analyzed to reduce cost through demand response (DR). A novel energy density-weighted asymmetric Nash bargaining (DANB) method is designed to fairly assess the combined contribution, considering both electricity and hydrogen bargaining metrics for end-users. An alternating optimization procedure with the adaptive alternating direction method of multipliers (ADMM) algorithm is designed to solve the mixed-integer linear programming (MILP) problem in the social operation cost minimization process. Comparison results show the algorithm convergence advantage, the cost-effectiveness and scalability of the cooperation framework, as well as the rationality of the DANB and the flexibility of HVs in DR. Besides, the optimal configuration of the battery, the hydrogen tank, and the electrolyzer in the framework is explored, alongside the economic feasibility of electricity-hydrogen-electricity technology.

Spatial specificity of radical scavenging ability in Moringa oleifera varieties from selected countries

Moringa oleifera (MO) is widely recognized for its rich content of bioactive compounds, which possess significant antioxidant and anti-inflammatory properties. However, variations in the phytochemical composition of MO seeds, influenced by geographical origin and processing methods, have not been fully explored. This study investigates the radical scavenging abilities of MO seeds from Nigeria, Ghana, Haiti, and India, all grown under controlled conditions at the Winfred Thomas Agricultural Research Station (WTARS) at Alabama A and M University. The primary objective of this study was to assess the variation in radical scavenging abilities, specifically DPPH (1,1-diphenyl-2-picrylhydrazyl) activity, across different MO seed varieties and processing methods. This was done to determine if spatially specific dose-response behaviors exist and to identify which combinations of seed origin and preparation yield the highest antioxidant activity. MO seeds were collected from four countries: Nigeria, Ghana, Haiti, and India, and subjected to three different processing methods—raw, boiled, and fermented. The seeds were then extracted using two solvents, 70% ethanol and 80% methanol. The DPPH radical scavenging activity was measured using the Brand-Williams method with slight modifications, and the results were expressed as micromoles of Trolox Equivalent (TE) per gram of sample. The study revealed significant variability in the antioxidant activity of MO seeds based on their geographical origin and processing methods. Among the four countries, MO seeds from India exhibited the highest average DPPH value (8018.87 µmol/g), followed by Haiti (6741.10 µmol/g), Nigeria (6349.98 µmol/g), and Ghana (6068.87 µmol/g). Raw seeds consistently showed the highest radical scavenging ability across all locations and solvents, with the highest recorded value in Haitian raw seeds extracted with 70% ethanol (9720 µmol/g). In contrast, fermented seeds exhibited the lowest antioxidant activity, with the least value observed in Ghanaian seeds extracted with 80% methanol (306.33 µmol/g). The choice of solvent also played a crucial role, with 70% ethanol outperforming 80% methanol in preserving or extracting antioxidants, particularly in raw and boiled seeds. The study also found significant dose-response relationships, with raw seeds showing the steepest increase in DPPH activity with higher extract concentrations. In contrast, fermented seeds demonstrated a weaker dose-response, indicating diminished radical scavenging ability after fermentation. The ANOVA results further highlighted the significant effects of the country of origin, treatment method, and their interaction on antioxidant activity. The interaction between geographical origin and processing methods was particularly noteworthy, suggesting that the optimal method for maximizing antioxidant activity varies depending on the seed's origin. The findings underscore the importance of considering both the geographical origin and the processing method when evaluating the antioxidant properties of MO seeds. Indian and Haitian MO seeds, particularly in their raw form and extracted with 70% ethanol, offer the highest antioxidant potential. The study also demonstrates that minimal processing is crucial for preserving the radical scavenging ability of MO seeds. Further research should focus on the specific phytochemical compounds responsible for the antioxidant activity in MO seeds from different regions. Additionally, farmers and producers should optimize cultivation and processing methods based on local environmental conditions to maximize the nutritional and medicinal benefits of MO seeds. Finally, public awareness programs should be initiated to promote the health benefits of MO seeds, particularly those with high antioxidant activity.

Microservices, a Definition Analyzed by ßMACH

Microservices are an answer to various scalability challenges. They enable building large and complex systems by scaling the number of services, and development teams. Microservices allow self-management and agile processes such as Scrum. Nevertheless, a minimal management process should also be defined and documented. Documentation must be easily understandable and applicable to the developing teams to foster efficiency. We propose to use the ßMACH method, a software management guidance. The result is a minimal and systematic description. Based on the definition obtained using ßMACH, we can state that the service and team isolation is essential for a scalable microservice system. In addition, we introduce ßMACH and show how to document a software management process. The documentation is easy to understand by software developers and interesting for software engineers.

DFT Calculations of 1H and 13C NMR Chemical Shifts of Hydroxy Secondary Oxidation Products of Geometric Isomers of Conjugated Linoleic Acid Methyl Esters: Structures in Solution and Revision of NMR Assignments.

Detailed DFT studies of 1H and 13C NMR chemical shifts of hydroxy secondary oxidation products of various geometric isomers of conjugated linolenic acids methyl esters are presented. Several low energy conformers were identified for model compounds of the central dienenol OH moiety, which were found to be practically independent on the various functionals and basis sets used. This greatly facilitated the minimization process of the geometric isomers of conjugated linolenic acids methyl esters. Several regularities of the literature experimental 1H and 13C chemical shifts were reproduced very accurately with the computational chemical shifts of the Gibbs low energy DFT optimized conformers, after a Boltzmann analysis. δ(13C) and δ(1H) of the methine CH-OH group are highly diagnostic for the trans/trans and cis/trans geometric isomerism of the adjacent double bond. δ(13C) of the -CH2- group adjacent to the terminal double bond of the conjugated system strongly depend on the cis/trans geometric isomerism of this bond and, thus, could be of importance in structural analysis. Ambiguities in the reported literature resonance assignments of olefinic carbons had been resolved. Computational δ(1H) and δ(13C) can be utilized for the identification of geometric isomerism and structural and conformational elucidation of hydroxy derivatives of conjugated linoleic acids and their ester derivatives.

Selection of the Process of Arc Welding of Sealing Surfaces of Power Valves with a Consumable Electrode in the Shielding Gas

Introduction. One of the main requirements to the methods of weld overlay of sealing surfaces of power valve trim parts is to obtain a high-quality wear-resistant pad with minimal penetration and optimal process performance. Currently, arc, electroslag, plasma, beam, induction and other surfacing techniques have been developed and introduced into production. However, the influence of various arc welding processes with a consumable electrode in shielding gas on the geometric parameters of weld beads and metal hardness of sealing surfaces is understudied. The presented research is intended to fill this gap. The objective of its authors is to select such a process of arc welding of beads on parts of the power valve trim with a consumable electrode in shielding gases, which would provide the best workability of the deposited metal.Materials and Methods. Arc surfacing with a consumable electrode in a mixture of gases was performed on steel plates. The welding torch was moved in a straight line, without transverse oscillations, using the FRC-9 mechanism (Fronius). A microprocessor-controlled inverter-type digital current source TransPulsSynergic 3200 CMT (Fronius) was used as the power supply. The following welding processes were analyzed: MIG/MAG process with self-regulation (Standard mode), synergic process of MIG/MAG method (Synergic mode), short arc process with mechanical separation of electrode metal droplets (CMT-ColdMetalTransfer), and synergic pulse-arc process (PulseSynergic). The short-cut process of bead surfacing was evaluated by the stability of the values of the energy parameters of the bead surfacing mode in time at the same electrode wire feed rates, which were recorded by oscilloscopes, as well as by comparing the geometric characteristics of the deposited beads and the hardness of the deposited metal.Results. The analysis of experimental data of the geometrics of the weld beads and their complex dimensional characteristics made it possible to establish that the welding engineering requirements for the welded beads are most fully met by long-arc surfacing by the PulseSynergic pulse-arc process.Discussion and Conclusion. The study and the resulting data make a certain contribution to solving the problem of the influence of arc welding processes on the parameters of weld beads and on the hardness of the metal of sealing surfaces. A detailed analysis of the modes of arc surfacing of beads with a consumable electrode in shielding gases on the trim parts of power valves can be used in further research on this topic. The conclusions of the authors will not only provide considerable theoretical assistance to scientists, but will also make adjustments to the activities of practitioners.

Design and Evaluation of a Cloud Computing System for Real-Time Measurements in Polarization-Independent Long-Range DAS Based on Coherent Detection.

CloudSim is a versatile simulation framework for modeling cloud infrastructure components that supports customizable and extensible application provisioning strategies, allowing for the simulation of cloud services. On the other hand, Distributed Acoustic Sensing (DAS) is a ubiquitous technique used for measuring vibrations over an extended region. Data handling in DAS remains an open issue, as many applications need continuous monitoring of a volume of samples whose storage and processing in real time require high-capacity memory and computing resources. We employ the CloudSim tool to design and evaluate a cloud computing scheme for long-range, polarization-independent DAS using coherent detection of Rayleigh backscattering signals and uncover valuable insights on the evolution of the processing times for a diverse range of Virtual Machine (VM) capacities as well as sizes of blocks of processed data. Our analysis demonstrates that the choice of VM significantly impacts computational times in real-time measurements in long-range DAS and that achieving polarization independence introduces minimal processing overheads in the system. Additionally, the increase in the block size of processed samples per cycle results in diminishing increments in overall processing times per batch of new samples added, demonstrating the scalability of cloud computing schemes in long-range DAS and its capability to manage larger datasets efficiently.

An Insight into Chip and Surface Texture Shaping Under Finish Turning of Powder Steels Infiltrated with Tin Bronze.

The manufacturing of work parts made of powder (sintered) steels is currently widespread in industry, as it provides minimal processing allowances and high dimensional accuracy, as well as the required properties and unconventional chemical composition. At the same time, their low tensile or bending strength must be considered a serious disadvantage. In order to minimize these disadvantages, a number of strengthening technologies are used, among which is the infiltration of porous base materials with metal alloys. In this study, the details of finish turning of sintered iron-graphite-based steel infiltrated with tin bronze with molybdenum disulfide addition are considered. Changes in the shape of chips and their geometric features, as well as the 3D parameters and topography features of the surface machined, are presented after finish turning with AH8015 carbide inserts. The cutting speed (vc) and feed rate (f) were used as variable parameters. It was found that when turning the powder steels under study, the chips took the shape of small fragments or element chips, including segmented chips. For quenching steel, the formation of irregular lamellae was observed and for the initial state, a serrated chip was registered. For the initial state, a reduction in Kb values was observed in the range of the vc of 50-100 m/min and f of 0.05-0.075 mm/rev, and for quenching in the range of 225-250 m/min and 0.05-0.075 mm/rev. Compared to the initial state, for quenching, depending on the cutting parameters, a 14% reduction in the chip spreading ratio Kb or an increase from 2 to 32% was registered. For the initial state and quenching, a decrease in the Sp and Sv parameters was achieved in the range of the vc of 200-250 m/min and f of 0.05-0.075 mm/rev, and there was an increase in the range of 50-150 m/min and 0.125-0.15 mm/rev. Compared to the initial state, an increase in the Sz parameter from 10 to 35% was observed for quenching. On the surfaces machined with vc = 50 m/min and f = 0.05 mm/rev, waves and single significant peaks were observed. On the other hand, vc = 250 m/min and f = 0.15 mm/rev provided classical feed tracks in the form of valleys and irregular ridges on the surfaces machined. The test results can be useful in the design and manufacturing of industrial parts made of powder steels.

CLOUD-BASED PREDICTIVE MOBILE APPLICATION FOR ASSESSING HONEY PURITY FROM STINGLESS BEES

Honey bees have various types and characteristics, one of which is the stingless bee. This type has limitations in producing honey, the selling value of its nest is quite expensive, and the water content in the honey produced is relatively high. The high water content affects the shelf life of this type of honey product, making it a challenge for honey farmers in marketing it. In addition, the dominant sour taste also makes its market increasingly limited or vulnerable to falsification of its purity by irresponsible producers. The use of spectrophotometers is increasingly developing in the food sector, especially in detecting the purity of a food product. The portable type of spectrophotometer also makes it easier to obtain spectrum data for a particular product. A simpler technique that is directly connected to a computer device allows it to be developed into a cloud-based application by providing minimal raw data processing (pre-processing). This study produces an android-based application and a simple cloud-based application architecture, which aims to facilitate the application of a honey purity prediction model from stingless bees. The Android-based application was successfully created by applying 'raw' spectrum data processing from the results of scanning a portable spectrophotometer, and data experiments with the SVM classification model produced an accuracy of 95%. The application of PCA techniques to cloud-based mobile application architecture results in efficient preprocessing of spectrum data.

Neuro-Fuzzy Logic for Automatic Animation Scene Generation in Movie Arts in Digital Media Technology

Animation scene generation (ASG) is the best digital media tool for lifelike scenes, particularly for movies. Traditional animation methods are laborious, computationally intensive, and scalable. Thus, this work addresses animation production issues using NFL-ASG. Combining fuzzy logic with a convolution neural network may create more realistic animated situations with less human interaction and better learning. Convolutional model training uses animation scenarios’ complicated motion patterns, character interactions, and ambient factors. Deep learning and fuzzy logic might change animation by boosting production techniques and releasing digital media technological creativity. After testing the system on the Moana Island scene dataset, it achieved a perception analysis success rate of 0.981% and a minimal processing complexity of (n logn).

Machine-Learning-Based Spectral Modeling: A Biomimetic Guide for Enhancing Esthetics.

To provide guidelines and means for optimal coverage and distribution of computer models with 1-10 clusters, designed based on an invivo extensive dental colorimetric database and compare the findings with some reputable shade guides. The Fuzzy C-Means (FCM) algorithm was used to optimize the tab position in the CIELAB color space, while MATLAB Fuzzy Logic was used to implement the codes. A minimization process was performed to fine-tune the cluster centers, minimizing Coverage Error (CE00) and Maximum Error (ME00). Spectral reconstruction was performed, and the results were compared with the corresponding data for VITA classical A1-D4 (VC), Linearguide 3D-Master (LG), and Bleachedguide 3D-Master (BG) shade guide. Paired t-test was employed to assess the significance of CE00 differences. CE00 and ME00 ranges for 1-to-10-tab models were 3.8 (14.2) to 1.7 (4.3), while the corresponding values for shade guides were 3.1 (9.1) for VC, 2.3 (6.0) for LG, and 2.9 (9.3) for BG. Hence, the 4-tab-model with CE00 of 2.2 and ME00 of 7.1, outperformed CE00 of LG (the lowest/best CE00 among shade guides), while the 6-tab-model, with CE00 of 2.0 and ME00 of 5.6, outperformed its ME00. A paired t-test confirmed that increasing the number of tabs models resulted in significant CE improvement in all cases (p ≤ 0.05). Shade guide models with only 4 to 6 tabs, optimized using computational techniques, outperformed physical dental shade guides tested, exhibiting significantly lower (better) Coverage Error and Maximum Error. Given that the spectral modeling was performed on an extensive invivo database of natural teeth, this approach was validated as a biomimetic guide for shade guides and corresponding materials of the future, offering a possibility for better shade matching with fewer shades/shade tabs.

Relevance Theory and Irony

The scientific article aims at elucidating Relevance theory, put forward by Dan Sperber and Deirdre Wilson in 1986, within the context of cognitive science and linguistics. It focuses on cognitive and communicative principles, its difference from Grice's cooperative principle and its role in deciphering irony by means of echoic expressions. The theory of relevance is set forth through key aspects such as cognitive and communicative principles, contextual effects, processing efforts, and its role in decoding irony through echoic expressions. The objective of the scientific article is to provide a detailed analysis of Relevance theory, underscoring its advantages over Grice's cooperative principle and its applicability in deciphering irony. The article centers on displaying how relevance theory strikes the balance of contextual effects and processing efforts, making communication more predictable and comprehensible. In addition, it targets at displaying how echoic expressions allow for comprehending ironic utterances and shape relationships between conversationalists. Main provisions of the article: Relevance is determined through the balance between contextual effects and processing efforts required to process information. A message is considered more relevant when it generates significant contextual effects with minimal processing efforts. Unlike the Cooperative principle, which calls for high cooperation and adherence to all maxims (Quality, Quantity, Manner, Relation), relevance theory centers on attaining mutual understanding without the need for excessive details. Irony is viewed as a form of echoic utterance, where the addresser echoes a thought or utterance with a critique or disapproving attitude. The addressee is required to identify the contrast between the literal meaning and the addresser’s attitude, making the ironic message relevant through its layered interpretation. This approach allows for a profound comprehension of irony beyond merely implying the opposite meaning.

Emotional intelligence and the dark triad: a latent profile analysis to investigate the Jekyll and Hyde of the emotionally intelligent manager.

Managers sometimes have a bad reputation as they are often perceived as more manipulative than other employees. This study focuses on the Dark Triad (DT)-comprising psychopathy, Machiavellianism, and narcissism-and its connection with managers' "Ability" Emotional Intelligence (AEI). The link between DT (measured through the Dirty Dozen) and managers' AEI (measured through QEPro, an AEI performance test) was examined through a Latent Profile Analysis (LPA). We identified two AEI latent profiles within a heterogeneous population of 231 French managers. Our results show that managers with the FEP (Full Emotional Processing) profile are less Machiavellian (relative to the MEP -Minimal Emotion Processing- profile). Our results show that identifying AEI profiles may be a practical way to prevent toxic Management.

Engaging the Space Between Minimal Processing and Inclusive Description to Create More Sustainable and Ethical Workflows

ABSTRACT Archival literature includes two prominent theoretical currents: the implementation of minimal descriptive practices and the need for more inclusive, reparative practices. Although prominent, these threads are not often considered together to explore how minimal processing frameworks may impact efforts for a diverse and representative archival record documenting marginalized communities and experiences. This article discusses these two frameworks, the apparent tension between them, and possible methods for reconciling the two to integrate more inclusive practices directly into standard archival workflows. This article argues that implementing inclusive practices into archival work is necessary to create a more representative historical record. Balancing both perspectives is necessary to create access points discoverable to a broad range of archival researchers. Creating access points for marginalized researchers is part of the core of archivists’ professional responsibilities and cannot be performed as a specialized project or contingent upon specialized funding—it must be embedded directly into all accessioning and processing workflows. By simultaneously considering these two theoretical currents, archivists can incorporate inclusive and reparative practices within an overarching extensible processing workflow.

Thermal stability of stigmasterol and β-sitosterol glucosides in fresh-cut bitter melon fruit.

Charantin in M. charantia fruit comprises stigmasterol glucoside (STG) and β-sitosterol glucoside (BSG). Despite numerous studies on charantin quantification and health beneficial bioactivity, thermal stability of two components remains unclear. This study investigated the thermal stability of BSG and STG in the fruit growth stages (S1-S5). Freeze-dried fruit contained approximately twice higher BSG than STG, with the content tending to decrease during fruit maturation. A dramatic decrease in content was observed between S2 and S3, with a reduction of 70% for BSG and 75% for STG. In thermal processing, BSG significantly degraded above 30°C treatments, while STG remained stable. It was found that the thresholds of thermal stability were approximately 10°C for BSG and 30°C for STG. Therefore, minimal thermal processing or freeze-drying is recommended for preserving charantin in the fruit.

Lightweight Machine Learning-Based DOS Attack Detection in Wireless Sensor Networks Using Decision Tree and Information Gain

Abstract—Wireless Sensor Networks (WSNs) are rapidly expanding across various domains due to their unique characteristics and performance capabilities. However, these networks are highly vulnerable to a range of security threats, particularly Denial-of-Service (DoS) attacks, which are among the most common in WSNs. This paper explores the vulnerabilities of WSNs, focusing on DoS threats, and reviews current techniques for their detection. It introduces a lightweight machine learning-based approach using a decision tree (DT) algorithm with the Information Gain (IG) feature selection method for efficient DoS detection. Tested on an enhanced WSN-DS dataset, the proposed method demonstrated high accuracy and minimal processing time compared to other classifiers, such as XGBoost, and RF. This efficiency makes the proposed method well-suited for real- time DoS attack detection in resource-constrained WSNs. Keywords—Machine Learning,Decision Tree (DT),Information Gain (IG),DoS attack detection

On the Redundancy of Hessian Non-Singularity for Linear Convergence Rate of the Newton Method Applied to the Minimization of Convex Functions

A new property of convex functions that makes it possible to achieve the linear rate of convergence of the Newton method during the minimization process is established. Namely, it is proved that, even in the case of singularity of the Hessian at the solution, the Newtonian system is solvable in the vicinity of the minimizer; i.e., the gradient of the objective function belongs to the image of the matrix of second derivatives and, therefore, analogs of the Newton method may be used.

Acute change in resting energy expenditure and vital signs in response to white tea consumption in females: a pilot study

BackgroundWhite tea, derived from the Camellia sinensis plant like other teas, uses tender buds and young leaves and undergoes minimal processing. This results in higher levels of antioxidants and bioactive substances, which may enhance thermogenesis more effectively than other teas. This first human study aimed to investigate the acute effects of white tea consumption on resting energy expenditure (REE) and some vital signs, including blood pressure (BP), heart rate (HR), and body temperature (BT).MethodsThirty-two healthy female volunteers with normal initial BP and whose caffeine intakes were < 300 mg/d were enrolled in the study. The caffeine and total phenolic content of white tea samples were determined by the high-performance liquid chromatography method and the Folin-Ciocalteu colorimetric method, respectively. After baseline measurements, participants consumed white tea containing 6 mg of caffeine per kilogram of lean body mass, and the white tea was prepared with bottled drinking water at 80 °C and brewed for 3 min. REE, BP, and BT were assessed at various intervals (baseline, 30 min, 120 min, and 180 min) post-consumption of the white tea.ResultsThe results revealed a significant increase in REE by 8.7% at 180 min after the consumption. In particular, there was a substantial difference in both values between the intervals of 30 min to 180 min and baseline to 180 min for REE (p < 0.05). Maximal oxygen consumption and BT also increased significantly over time (p < 0.05) and the observed increment in BT suggests a thermogenic effect associated with white tea consumption. However, systolic BP, diastolic BP, and heart rate showed no significant difference.ConclusionsThese findings suggest white tea consumption may acutely enhance REE and maximal oxygen consumption, so the results are promising for body weight management. This study is the first human study in the literature about the effects of white tea on energy expenditure and vital signs.

Enhanced Curvature-Based Fabric Defect Detection: A Experimental Study with Gabor Transform and Deep Learning

Quality control at every stage of production in the textile industry is essential for maintaining competitiveness in the global market. Manual fabric defect inspections are often characterized by low precision and high time costs, in contrast to intelligent anomaly detection systems implemented in the early stages of fabric production. To achieve successful automated fabric defect identification, significant challenges must be addressed, including accurate detection, classification, and decision-making processes. Traditionally, fabric defect classification has relied on inefficient and labor-intensive human visual inspection, particularly as the variety of fabric defects continues to increase. Despite the global chip crisis and its adverse effects on supply chains, electronic hardware costs for quality control systems have become more affordable. This presents a notable advantage, as vision systems can now be easily developed with the use of high-resolution, advanced cameras. In this study, we propose a discrete curvature algorithm, integrated with the Gabor transform, which demonstrates significant success in near real-time defect classification. The primary contribution of this work is the development of a modified curvature algorithm that achieves high classification performance without the need for training. This method is particularly efficient due to its low data storage requirements and minimal processing time, making it ideal for real-time applications. Furthermore, we implemented and evaluated several other methods from the literature, including Gabor and Convolutional Neural Networks (CNNs), within a unified coding framework. Each defect type was analyzed individually, with results indicating that the proposed algorithm exhibits comparable success and robust performance relative to deep learning-based approaches.