bluuebunny/crossref_metadata_2025 huggingface dataset:
Positive p53 Expression Is Associated with Primary Endocrine Therapy Resistance in Locally Advanced Stage Luminal B HER2-Negative Breast Cancer Patients: A Cross-Sectional Study in Indonesia
Authors: Halim Freda , Azhar Yohana , Suwarman Suwarman , Wahjoepramono Eka Julianta , Hernowo Bethy
Published: May 2023

Luminal B HER2-negative breast cancer (BC) is the most common type in Indonesian BC patients, and frequently manifests with locally advanced staging. Recurrence often occurs within two years of the endocrine therapy course (primary endocrine therapy (ET) resistance). p53 mutation often exists in luminal B HER2-negative BC, but its application as an ET resistance predictor in those populations is still limited. The primary purpose of this research is to evaluate p53 expression and its association with primary ET resistance in luminal B HER2-negative BC. This cross-sectional study compiled 67 luminal B HER2-negative patients’ clinical data during their pre-treatment period until they completed a two-year course of endocrine therapy. They were divided into two groups: 29 patients with primary ET resistance and 38 without primary ET resistance. Pre-treatment paraffin blocks from each patient were retrieved, and the p53 expression difference between the two groups was analyzed. Positive p53 expression was significantly higher in patients with primary ET resistance [odds ratio (OR) of 11.78 (95% CI: 3.72–37.37, p-value < 0.0001)]. We conclude that p53 expression could be a beneficial marker for primary ET resistance in locally advanced luminal B HER2-negative BC.
Efficient Discretization of Optimal Transport
Authors: Wang Junqi , Wang Pei , Shafto Patrick
Published: May 2023

Obtaining solutions to optimal transportation (OT) problems is typically intractable when marginal spaces are continuous. Recent research has focused on approximating continuous solutions with discretization methods based on i.i.d. sampling, and this has shown convergence as the sample size increases. However, obtaining OT solutions with large sample sizes requires intensive computation effort, which can be prohibitive in practice. In this paper, we propose an algorithm for calculating discretizations with a given number of weighted points for marginal distributions by minimizing the (entropy-regularized) Wasserstein distance and providing bounds on the performance. The results suggest that our plans are comparable to those obtained with much larger numbers of i.i.d. samples and are more efficient than existing alternatives. Moreover, we propose a local, parallelizable version of such discretizations for applications, which we demonstrate by approximating adorable images.
Outlier Detection with Reinforcement Learning for Costly to Verify Data
Authors: Nijhuis Michiel , van Lelyveld Iman
Published: May 2023

Outliers are often present in data and many algorithms exist to find these outliers. Often we can verify these outliers to determine whether they are data errors or not. Unfortunately, checking such points is time-consuming and the underlying issues leading to the data error can change over time. An outlier detection approach should therefore be able to optimally use the knowledge gained from the verification of the ground truth and adjust accordingly. With advances in machine learning, this can be achieved by applying reinforcement learning on a statistical outlier detection approach. The approach uses an ensemble of proven outlier detection methods in combination with a reinforcement learning approach to tune the coefficients of the ensemble with every additional bit of data. The performance and the applicability of the reinforcement learning outlier detection approach are illustrated using granular data reported by Dutch insurers and pension funds under the Solvency II and FTK frameworks. The application shows that outliers can be identified by the ensemble learner. Moreover, applying the reinforcement learner on top of the ensemble model can further improve the results by optimising the coefficients of the ensemble learner.
The Effects of Inequality on the Substitution of Essential Goods for Tobacco Smoking in South Africa
Authors: Nkomo Nomusa Yolanda , Biyase Mduduzi , Simo-Kengne Beatrice D.
Published: May 2023

Tobacco consumption contributes to a substantial amount of household expenditures, which might lead to decreased spending on other essentials. This study examines household head tobacco expenditures in various inequality settings. In this study, we investigated the impact of gender, race, and educational inequality and the substitution effect of tobacco expenditure on essentials such as children’s education and household food. We looked at how much of the resources household heads spend on tobacco in different inequality settings that replace households’ essentials. The panel setting of the National Income Dynamics Study (NIDS), South Africa’s first nationally representative household panel survey, is used as a data collection source for this study. These are household surveys conducted by the Presidency’s Office of Planning, Monitoring, and Evaluation. The panel data are subject to attrition in longitudinal research. We compared the conditional expenditure shares of various types of households using econometric models such as moment quantile regression. A negative and statistically significant estimated coefficient of tobacco expenditure and the coefficient of the interacted term (inequality and tobacco expenditure) demonstrated the substitution effect. The findings reveal that low-income households whose heads smoke tobacco invest less in their children’s education, while well-educated heads of high-income households’ place as much value on their children’s education as they do on cigarette expenditure. The study also points out that the share of income spent on cigarettes by black household heads is negatively connected to their children’s education across all quantiles compared to non-blacks. We conclude that low-income households are more likely to experience the substitution impact than high-income households. This study recommends, among other things, that low-income households should prioritize needs over non-essentials in order to maximize household satisfaction, and government should implement policies that will limit tobacco consumption expenditure.
Preliminary Evaluation of a Mobile, Web-Based Coaching Tool to Improve Pre-K Classroom Practices and Enhance Learning
Authors: Christopher Caroline , Wilson Sandra Jo , Fuhs Mary Wagner , Layzer Carolyn , Litschwartz Sophie
Published: May 2023

Educators rely on professional development to improve instruction. Research suggests that instructional coaching which utilizes specific coaching practices, such as classroom observation followed by debriefing and goal setting, and integrated strategies such as co-teaching, bring about significant change in instructional practices. The goal of this study was to gauge whether or not the use of a web-based data collection and coaching tool led to changes in focal classroom practices and whether or not improving those practices was, in turn, related to students’ academic and self-regulation gains across the prekindergarten year. To examine the implementation and impact of the coaching app, researchers conducted a cluster-randomized trial, comparing the classroom practices of teachers receiving business-as-usual coaching to those being coached with the app. Classroom observation data showed no significant differences in teachers’ practices across the school year, and student achievement did not differ between conditions. Qualitative data from coach interviews, however, revealed that coaches using the app were more likely to employ integrated coaching strategies associated with improving instruction. The lack of differences in terms of teachers’ practices and students’ assessment gains may be due to a lack of statistical power and inconsistent professional development implementation associated with ongoing disruptions due to the pandemic. Further research examining the effectiveness of educational technologies supporting professional development is needed.
Automated Bilateral Trading of Energy by Alliances in Multi-Agent Electricity Markets
Authors: Algarvio Hugo
Published: May 2023

In liberalized markets, consumers can choose their electricity suppliers or be part of an energy community. The problem with communities is that they may not have enough weight to trade in markets, which can be overcome by forming coalitions. Electricity is traded in spot markets or through bilateral contracts involving consumers and suppliers. This paper is devoted to bilateral contracting, modeled as a negotiation process involving an iterative exchange of offers and counter-offers. It focuses on coalitions of energy communities. Specifically, it presents team and single-agent negotiation models, where each consumer has strategies, tactics, and decision models. Coalition agents are equipped with intra-team strategies and decision protocols. It also describes a study of bilateral contracts involving a seller agent and a coalition of energy communities. By allying into a coalition, members of energy communities reduced their average costs for electricity by between 2% (large consumers) and 64% (small consumers) according to their consumption. Their levelized cost reduction was 19%. The results demonstrate the power of coalitions when negotiating bilateral contracts and the benefit of a low-consumption members alliance with larger players.
k-NN Query Optimization for High-Dimensional Index Using Machine Learning
Authors: Choi Dojin , Wee Jiwon , Song Sangho , Lee Hyeonbyeong , Lim Jongtae , Bok Kyoungsoo , Yoo Jaesoo
Published: May 2023

In this study, we propose three k-nearest neighbor (k-NN) optimization techniques for a distributed, in-memory-based, high-dimensional indexing method to speed up content-based image retrieval. The proposed techniques perform distributed, in-memory, high-dimensional indexing-based k-NN query optimization: a density-based optimization technique that performs k-NN optimization using data distribution; a cost-based optimization technique using query processing cost statistics; and a learning-based optimization technique using a deep learning model, based on query logs. The proposed techniques were implemented on Spark, which supports a master/slave model for large-scale distributed processing. We showed the superiority and validity of the proposed techniques through various performance evaluations, based on high-dimensional data.
A Collaborative Multi-Granularity Architecture for Multi-Source IoT Sensor Data in Air Quality Evaluations
Authors: Li Wantong , Zhang Chao , Cui Yifan , Shi Jiale
Published: May 2023

Air pollution (AP) is a significant environmental issue that poses a potential threat to human health. Its adverse effects on human health are diverse, ranging from sensory discomfort to acute physiological reactions. As such, air quality evaluation (AQE) serves as a crucial process that involves the collection of samples from the environment and their analysis to measure AP levels. With the proliferation of Internet of Things (IoT) devices and sensors, real-time and continuous measurement of air pollutants in urban environments has become possible. However, the data obtained from multiple sources of IoT sensors can be uncertain and inaccurate, posing challenges in effectively utilizing and fusing this data. Meanwhile, differences in opinions among decision-makers regarding AQE can affect the outcome of the final decision. To tackle these challenges, this paper systematically investigates a novel multi-attribute group decision-making (MAGDM) approach based on hesitant trapezoidal fuzzy (HTrF) information and discusses its application to AQE. First, by combining HTrF sets (HTrFSs) with multi-granulation rough sets (MGRSs), a new rough set model, named HTrF MGRSs, on a two-universe model is proposed. Second, the definition and property of the presented model are studied. Third, a decision-making approach based on the background of AQE is constructed via utilizing decision-making index sets (DMISs). Lastly, the validity and feasibility of the constructed approach are demonstrated via a case study conducted in the AQE setting using experimental and comparative analyses. The outcomes of the experiment demonstrate that the presented architecture owns the ability to handle multi-source IoT sensor data (MSIoTSD), providing a sensible conclusion for AQE. In summary, the MAGDM method presented in this article is a promising scheme for solving decision-making problems, where HTrFSs possess excellent information description capabilities and can adequately describe indecision and uncertainty information. Meanwhile, MGRSs serve as an outstanding information fusion tool that can improve the quality and level of decision-making. DMISs are better able to analyze and evaluate information and reduce the impact of disagreement on decision outcomes. The proposed architecture, therefore, provides a viable solution for MSIoTSD facing uncertainty or hesitancy in the AQE environment.
Tracking and Data Association Based on Reinforcement Learning
Authors: Xiong Wei , Gu Xiangqi , Cui Yaqi
Published: May 2023

Currently, most multi-target data association methods require the assumption that the target motion model is known, but this assumption is clearly not valid in a real environment. In the case of an unknown system model, the influence of environmental clutter and sensor detection errors on the association results should be considered, as well as the occurrence of strong target maneuvers and the sudden appearance of new targets during the association process. To address these problems, this paper designs a target tracking and data association algorithm based on reinforcement learning. First, this algorithm combines the dynamic exploration capability of reinforcement learning and the long-time memory function of LSTM network to design a policy network that predicts the probability of associating a point with its various possible source targets. Then, the Bayesian network and the multi-order least squares curve fitting method are combined to predict the location of target, and the results are fed into the Bayesian recursive function to obtain the reward. Simultaneously, some corresponding mechanisms are proposed for possible problems that interfere with the association process. Finally, the simulation experimental results show that this algorithm associates the results with higher accuracy compared to other algorithms when faced with the above problem.
A Survey on the Shortcomings of the Current Rate of Penetration Predictive Models in Petroleum Engineering
Authors: Khalilidermani Mitra , Knez Dariusz
Published: May 2023

In drilling engineering, the rate of penetration (ROP) is a prevalent indicator to evaluate the energy efficiency of drilling operations. Nowadays, ROP prediction has become more critical since the production from deeper hydrocarbon resources is unprecedentedly escalating. So far, a wealth of theoretical and practical investigations has been conducted to develop ROP models; however, the existing models have not been adequately updated with the new technological advancements or geological restrictions. This research strives to integrate the latest advancements, restrictions, and future requirements in ROP prediction. To do this, the existing empirical and data-driven ROP models are elaborated and compared. From the conducted research, it was deduced that four uncontrollable factors, including the rock permeability, wellbore inclination, temperature, and rock hardness, have not been adequately considered in ROP models. Moreover, although data-driven ROP models deliver more accurate results than the empirical models, the determination of the number and type of the input parameters is still challenging. To tackle this issue, it is recommended to develop a formation-based classification system of input parameters for future investigations. This inclusive review can be adopted by the companies and engineers involved in drilling operations to update and reform their drilling strategies.
Impact of Stearic Acid as Heat Storage Material on Energy Efficiency and Economic Feasibility of a Vacuum Tube Solar Water Heater
Authors: Chopra K. , Tyagi V. V. , Pathak Sudhir Kumar , Khajuria Apaar , Pandey A. K. , Rahman Nazaruddin Abd , Mansor Muhamad , Sari Ahmet
Published: May 2023

The overheating of heat pipes, poor transfer of heat across the absorber and finned heat pipes, and inability to provide hot water in the late evening hours are major problems associated with conventional heat pipe vacuum collector systems. The amalgamation of highly conductive storage material between the absorber tube (heat collecting surface) and the heat pipe is an effective way to overcome these problems. In this study, a stearic acid amalgamated vacuum tube solar collector system was designed and fabricated and its thermal output compared with a conventional vacuum tube system without storage material under the same environmental conditions. The experimental results showed that the amalgamation of stearic acid as storage material enhanced the thermal output of the solar system compared to the conventional one. The desired heat gain of the solar system with storage material increased by 31.30, 23.34, and 18.78% for Test 1_40 °C, Test 2_45 °C, and Test 3_50 °C, respectively. The technoeconomic analysis showed that almost 118.80 USD in revenue could be earned by the proposed solar system at the end of 15 years. The total running cost of ELG and the developed solar system was observed to be 202.62 and 86.70 USD, respectively. On average, the cost of hot water production using the solar system and ELG was found to be 0.0016 and 0.004 USD/L, respectively. The value of LEC was found to be 0.062 USD/electricity unit, which was much lower than the LEC value of ELG (0.116 USD/electricity unit). The value of NPW (73.73 USD) indicated high acceptability of the proposed system. The payback time is lower than the life of the system, indicating its suitability for use in the commercial sector. Therefore, the proposed solar system is highly recommended over conventional water heating systems in urban and rural areas.
Exergy Analysis of a Shell and Tube Energy Storage Unit with Different Inclination Angles
Authors: Peng Li , Wu Hongjun , Cao Wenlong , Mao Qianjun
Published: May 2023

To optimize the utilization of solar energy in the latent heat thermal energy storage (LHTES) system, this study conducts exergy analysis on a paraffin-solar water shell and tube unit established in the literature to evaluate the effects of different inclination angles, inlet temperatures, original temperatures, and fluid flow rates on the exergy and exergy efficiency. Firstly, the thermodynamic characteristics of the water and the natural convection effects of the paraffin change with different inclination angles. When the inclination angle of the heat storage tank is less than 30°, the maximum exergy inlet rate rises from 0 to 144.6 W in a very short time, but it decreases to 65.7 W for an inclination angle of 60°. When the inclination angle is increased from 0° to 30°, the exergy efficiency rises from 86% to 89.7%, but it decreases from 94% to 89.9% with the inclination angle from 60° to 90°. Secondly, under the condition that the inclination angle of the energy storage unit is 60°, although increasing the inlet temperature of the solar water enhances the exergy inlet and storage and reduces the charging time, it increases the heat transfer temperature difference and the irreversible loss of the system, thus reducing the exergy efficiency. As the inlet water temperature is increased from 83 to 98 °C, the exergy efficiency decreases from 94.7% to 93.6%. Moreover, increasing the original temperature of the LHTES unit not only reduces the exergy inlet and storage rates but also decreases the available work capacity and exergy efficiency. Finally, increasing the inlet water flow rate increases the exergy inlet and storage rates slightly. The exergy efficiency decreases from 95.6% to 93.3% as the unit original temperature is increased from 15 to 30 °C, and it is enhanced from 94% to 94.6% as the inlet flow rate is increased from 0.085 to 0.34 kg/s with the unit inclination angle of 60°. It is found that arranging the shell and tube unit at an inclination angle is useful for improving the LHTES system’s thermal performance, and the exergy analysis conducted aims to reduce available energy dissipation and exergy loss in the thermal storage system. This study provides instructions for solar energy utilization and energy storage.
Energy Production from Biomass Valorization
Authors: Yangin-Gomec Cigdem , Sárvári Horváth Ilona , Martín Carlos
Published: May 2023

This Special Issue of Energies contains successful submissions [...]
Submodule Capacitor Voltage Ripple Reduction of Full-Bridge Submodule-Based MMC (FBSM-MMC) with Non-Sinusoidal Voltage Injection
Authors: Huang Ming
Published: May 2023

The full-bridge submodule (FBSM)-based modular multilevel converter (FBSM-MMC) offers promising performance due to its ability to output negative FBSM voltage. This paper studies this ability of the FBSM-MMC under zero-sequence voltage injection (ZSVI) and second-order harmonic current injection (SHCI). The focus of the research is to redistribute the FBSM powers by injecting an additional power degree of freedom, resulting in a smaller FBSM capacitor voltage ripple, while keeping the maximum AC output voltage for a given fundamental frequency component of the arm voltage reference. Accordingly, a control strategy was developed, based on non-sinusoidal ZSVI, and SHCI is proposed for further improving the performance of the FBSM-MMC. The proposed non-sinusoidal ZSVI contains a higher sinusoidal third-order harmonic component than that of pure sinusoidal third-order voltage injection (THVI) when operating under the same maximum AC output voltage. By implementing this solution, a smaller amplitude of the injected second-order harmonic current can be achieved, producing a lower power loss in the FBSM-MMC. Considering the proposed solution, the relationship of the arm powers and FBSM capacitor voltages are also discussed. Finally, the simulation results and experimental results are presented to verify the effectiveness of the theoretical analysis of the proposed method.
Geographic Information Systems for Facilitating Audits of the Urban Built Environment
Authors: Balaras Constantinos A. , Theodoropoulos Andreas I. , Dascalaki Elena G.
Published: May 2023

Data collection and large-scale urban audits are challenging and can be time consuming processes. Geographic information systems can extract and combine relevant data that can be used as input to calculation tools that provide results and quantify indicators with sufficient spatial analysis to facilitate the local decision-making process for building renovations and sustainability assessment. This work presents an open-access tool that offers an automated process that can be used to audit an urban area in order to extract relevant information about the characteristics of the built environment, analyze the building characteristics to evaluate energy performance, assess the potential for the installation of photovoltaics on available building rooftops, and quantify ground permeability. A case study is also presented to demonstrate data collection and processing for an urban city block, and the relevant results are elaborated upon. The method is easily replicable and is based on open data and non-commercial tools.
Rooftop Solar Photovoltaic in Saudi Arabia to Supply Electricity Demand in Localised Urban Areas: A Study of the City of Abha
Authors: Shaher Abdullah , Alqahtani Saad , Garada Ali , Cipcigan Liana
Published: May 2023

This paper explores the potential of rooftop solar PV to meet the electricity demand in the urban areas of Abha city, Saudi Arabia (KSA), minimising imports from the grid. A localised energy system for Abha is proposed that considers two types of loads: (i) residential loads with a monthly aggregated energy consumption of 172,440 MWh and an electric demand of 239.5 MW, and (ii) commercial loads with a monthly aggregated energy consumption of 179,280 MWh and an electric demand of 249 MW. The grid currently supplies this load. This paper proposes a PV development planning tool for residential and commercial areas to calculate the total PV production for each type of load to achieve a balanced energy area, considering (i) the number of buildings, (ii) the type of load, (iii) the peak load, and (iv) the total PV array area in m2 per building. The results of the modelling study using real data demonstrate that the anticipated total PV production in residential and commercial areas is sufficient to meet local peak demand, and there is an excess of power that can either be stored locally or exported to the grid.
Effects of Ramp Rate Limit on Sizing of Energy Storage Systems for PV, Wind and PV–Wind Power Plants
Authors: Talvi Micke , Roinila Tomi , Lappalainen Kari
Published: May 2023

As the share of highly variable photovoltaic (PV) and wind power production increases, there is a growing need to smooth their fast power fluctuations. Some countries have set power ramp rate (RR) limits that the output powers of power plants may not exceed. In this study, the effects of RR limit on the sizing of energy storage systems (ESS) for PV, wind, and PV–wind power plants are examined. These effects have been studied prior for PV power plants. However, for the wind and PV–wind power plants, the effects of the RR limit are studied comprehensively for the first time. In addition, the effects of the size of the power plant are considered. The study is based on climatic measurements carried out with a sampling frequency of 10 Hz for a period of 153 days. The modeling of the PV and wind powers and the simulation of the RR-based control algorithm of the ESS were completed using MATLAB. The results show that as the applied RR limit increased from 1%/min to 20%/min, the required relative energy capacities of the ESSs of the PV, wind, and PV–wind power plants decreased roughly 88%, 89%, and 89%, respectively. The required relative power capacities of the ESSs of the PV, wind, and PV–wind power plants decreased roughly 15%, 12%, and 20%, respectively. The utilization of the ESSs was found to decrease as the applied RR limit increased and as the size of the power plant grew.
Distributed Optimal Coordination of a Virtual Power Plant with Residential Regenerative Electric Heating Systems
Authors: Yang Guixing , Liu Haoran , Wang Weiqing , Chen Junru , Lei Shunbo
Published: May 2023

Renewable energy sources play a key role in the transition towards clean and affordable energy. However, grid integration of renewable energy sources faces many challenges due to its intermittent nature. The controllability of aggregated regenerative electric heating load provides a method for the consumption of renewable energy sources. Based on the concept of a virtual power plant (VPP), this paper considers the cooperative energy management of aggregated residential regenerative electric heating systems. First, considering physical constraints, network constraints, and user comfort, comprehensive modeling of a VPP is given to maximize its social benefits. In addition, this VPP is investigated as a participant in day-ahead energy and reserve markets. Then, to solve this problem, a distributed coordination approach based on an alternating direction method of multipliers (ADMM) is proposed, which can respect the independence of users and preserve their privacy. Finally, the simulation results illustrate the effectiveness of our algorithm.
Techno-Economic Assessment of a Hybrid Offshore Wind–Wave Farm: Case Study in Norway
Authors: Rönkkö Jaan , Khosravi Ali , Syri Sanna
Published: May 2023

Recent years have seen the development of cutting-edge technology, such as offshore wind turbines and wave energy converters. It has previously been investigated whether integrating offshore wind turbines with wave energy converters is feasible. Diversifying the sources of offshore renewable energy also lowers investment costs and power fluctuation. This paper focuses on the development of a hybrid wind–wave energy system as well as the development of a techno-economic model to assess the system performance for a case study. A levelized cost of energy is calculated for the hybrid system by the Norwegian North Sea based on current knowledge about the technology costs. The economic benefits of sharing the common components of a wind-wave hybrid farm are inspected. Combinations of different wind–wave offshore hybrid systems are presented. Three technologies for both offshore wind turbines and wave energy converters are compared to find the most cost-efficient device pairing. The potential benefits of a shared infrastructure and the operational expenses are included in the evaluation. The combination yielding the lowest production cost of the cases studied is a combination of 160 MW of wind power and 40 MW of wave power, with a levelized cost of energy of EUR 107/MWh when the shared costs are 15%. In the study region, the average electricity price in Autumn 2022 was over EUR 300/MWh due to the European energy crisis.
A Comprehensive Analysis of Cinnamon, Flaxseed, and Lemon Seed Essential Oils’ Effects on In Vitro Gas Formation and Nutrient Degradability in Diets
Authors: Besharati Maghsoud , Palangi Valiollah , Niazifar Masoumeh , Esen Selim , Lackner Maximilian
Published: May 2023

This study aimed to assess the impact of essential oils (EOs) on in vitro gas formation and the degradability of dairy and beef cattle diets. This study also aimed to investigate the effects of different types of EOs on nutrient utilization and rumen microbial activity. The current study was conducted using a fully randomized design consisting of eight experimental treatments, including two control treatments without any additives, and treatments with cinnamon essential oil (CEO), flaxseed essential oil (FEO), and lemon seed essential oil (LEO) at a concentration of 60 mg/kg fresh mass. Two control treatments were used, one with alfalfa silage and dairy concentrate (DC, CON-DC) and the other with alfalfa silage and fattening concentrate (FC, CON-FC). Gas formation, dry matter (DM) digestibility, crude protein (CP) digestibility, effective degradability (ED), and soluble fractions of DM and organic matter (OM) were evaluated. CEO had a substantial effect on gas formation (p < 0.05). When EOs were added to the diets, they increased dry matter digestibility after 24 h of incubation as compared to control treatments. After 24 h of incubation, FCCEO and FCFEO had the highest CP digestibility among the diets. FCLEO considerably enhanced ED, as well as the soluble fraction of DM (a) at a passage rate of 2% per hour. Treatment with FCCEO resulted in a significant increase in soluble fractions compared to the control diets. At a passage rate of 2% h, DCCEO had the maximum ED value. When EOs were introduced to the diet, they dramatically decreased the insoluble portion of CP (b). Compared to the control treatments, gas production was significantly lower in the presence of LEO (FCLEO; p < 0.05). The addition of EOs to cattle diets may increase nutrient utilization and enhance rumen microbial activity. EOs extracted from lemon seeds (at a dose of 60 mg/kg of diet) lowered gas production in both dairy cattle and fattening diets.
Microbial Exploitation of Feather Wastes for Sustainable Production of Keratinase and Collagenase Enzymes by Didymella keratinophila AUMC 15399 in Submerged Fermentation
Authors: Al-Bedak Osama Abdel-Hafeez Mohamed , Moharram Ahmed Mohamed , Hussein Nemmat Abdel-Gawad , Taha Doaa Mohamed , Stephenson Steven L. , Ameen Fuad
Published: May 2023

A distinctive isolate was discovered and visually recognized as a member of the genus Didymella during a routine examination of Coelomycetes isolated from diverse fruit juices. Based on sequencing of the internal transcribed spacer (ITS), the fungus was identified as Didymella keratinophila since it showed a 100% identity to the type strain. The strain thrived and produced keratinase and collagenase enzymes by hydrolyzing native chicken feathers in submerged fermentation (SmF). After 10 days of fermentation at 30 °C, pH 9 using sodium nitrate as a nitrogen supply produced the highest keratinase activity of 8780 ± 620 U/mL/min, while pH 6 and beef extract produced the maximum collagenase activity of 11,230 ± 1290 U/mL/min. The partially-purified keratinase enzyme worked best at pH 7.0 and 45 °C, exhibiting a specific activity of 44,903 ± 1555 U/mg protein. The activity of the partially-purified collagenase enzyme was excellent at pH 6.0 at 35 °C, generating 15,753 ± 110 U/mg enzyme-specific activity. Mn2+ and K+ were the most efficient inhibitors of keratinases and collagenase, respectively. Both EDTA and metal ions significantly decreased the activity of keratinase and collagenase. This report identified a workable supplier of collagenase and keratinase enzymes derived from chicken feathers, offering a reliable way to exploit and manage these wastes for obtaining high-value products.
Impact of Hydrothermal Pretreatment Parameters on Mesophilic and Thermophilic Fermentation and Anaerobic Digestion of Municipal Sludge
Authors: Kakar Farokh Laqa , Aqeel Hussain , Liss Steven N. , Elbeshbishy Elsayed
Published: May 2023

Four parameters affecting hydrothermal pretreatment (HTP) of municipal sludge prior to anaerobic digestion and fermentation were investigated. Partial factorial design including several key HTP parameters at two distinct levels, including temperature (170 and 190 °C), retention time (RT) (10 and 30 min), pH (4 and 10), and solid content (SC) (4% and 16%), were studied. Further, the impact of HTP parameters on mesophilic and thermophilic fermentation was explored and compared. Results revealed a significant effect of all HTP parameters on COD solubilization, VFA, and methane yield. There were correlations between HTP parameters and process responses such as VFA yield and methane yield. HTP was found to increase COD solubilization and VFA production between 15 and 20% during thermophilic fermentation in relation to mesophilic treatment. All parameters, including SC, temperature, pH, and RT, were important contributing factors affecting methane production during anaerobic digestion. The highest methane production yield of 269 mL CH4/g TCOD added was observed at the highest SC (16%) and pH (10) and at the lower temperature (170 °C) and RT (10). HTP is expected to be combined with other intensification routes to treat waste with high solid contents improving the fermentation and anaerobic digestion processes.
Prediction of Small Bubble Holdups in Bubble Columns Operated with Various Organic Liquids at Both Ambient and Elevated Pressures and Temperatures
Authors: Nedeltchev Stoyan
Published: May 2023

This article focuses on the prediction of the small bubble holdups (assuming the existence of two major bubble classes) in two bubble columns (0.289 m in ID and 0.102 m in ID), operated with organic liquids under various conditions (including high temperature and pressure). A new correction factor has been established in the existing model for the prediction of the gas holdups in the homogeneous regime. The correction parameter is a single function of the Eötvös number (gravitational forces to surface tension forces), which characterizes the bubble shape. In addition, the behavior of small bubble holdups in 1-butanol (selected as a frequently researched alcohol) aerated with nitrogen, in a smaller BC (0.102 m in ID), at various operating pressures, is presented and discussed. The ratio of small bubble holdup to overall gas holdup, as a function of superficial gas velocity and operating pressure, has been investigated. All small bubble holdups in this work have been measured by means of the dynamic gas disengagement technique.
Effect of Horizontal Quasi-Periodic Oscillation on the Interfacial Instability of Two Superimposed Viscous Fluid Layers in a Vertical Hele-Shaw Cell
Authors: Assoul Mouh , El jaouahiry Abdelouahab , Bouchgl Jamila , Echchadli Mourad , Aniss Saïd
Published: May 2023

We investigate the effect of horizontal quasi-periodic oscillation on the stability of two superimposed immiscible fluid layers confined in a horizontal Hele-Shaw cell. To approximate real oscillations, a quasi-periodic oscillation with two incommensurate frequencies is considered. Thus, the linear stability analysis leads to a quasi-periodic oscillator, with damping, which describes the evolution of the amplitude of the interface. Two types of quasi-periodic instabilities occur: the low-wavenumber Kelvin-Helmholtz instability and the large-wavenumber resonances. We mainly show that, for equal amplitudes of the superimposed accelerations, and for a low irrational frequency ratio, there is competition between several resonance modes allowing a very large selection of the wavenumber from lower to higher values. This is a way to control the sizes of the waves. Furthermore, increasing the frequency ratio has a stabilizing effect for both types of instability whose thresholds are found to correspond to quasi-periodic solutions using the frequency spectrum. For a ratio of the two superimposed displacement amplitudes equal to unity and less than unity, the number of resonances and competition between their modes also become significant for the intermediate values of the ratio of frequencies. The effects of other physical and geometrical parameters, such as the damping coefficient, density ratio, and heights of the two fluid layers, are also examined.
Smelling Peppers and Pout Submitted to Convective Drying: Mathematical Modeling, Thermodynamic Properties and Proximal Composition
Authors: Moura Rodrigo Leite , Figueirêdo Rossana Maria Feitosa de , Queiroz Alexandre José de Melo , Santos Francislaine Suelia dos , Lima Antônio Gilson Barbosa de , Rego Junior Pedro Francisco do , Gomes Josivanda Palmeira , Silva Wilton Pereira da , Paiva Yaroslávia Ferreira , Moura Henrique Valentim , Silva Eugênia Telis de Vilela , Costa Caciana Cavalcanti , Gregório Mailson Gonçalves
Published: May 2023

Pepper (Capsicum spp.) is among the oldest and most cultivated crops on the planet. Its fruits are widely used as natural condiments in the food industry for their color, flavor, and pungency properties. Peppers have abundant production; on the other hand, their fruits are perishable, deteriorating within a few days after harvesting. Therefore, they need adequate conservation methods to increase their useful life. This study aimed to mathematically model the drying kinetics of smelling peppers (Capsicum chinense) and pout peppers (Capsicum chinense Jacq.) to obtain the thermodynamic properties involved in the process and to determine the influence of drying on the proximal composition of these peppers. Whole peppers, containing the seeds, were dried in an oven with forced air circulation, at temperatures of 50, 60, 70, and 80 °C, with an air speed of 1.0 m/s. Ten models were adjusted to the experimental data, but the Midilli model was the one that provided the best values of coefficient of determination and lowest values of the mean squared deviation and chi-square value in most of the temperatures under study. The effective diffusivities were well represented by an Arrhenius equation, appearing in the order of 10−10 m2·s−1 for both materials under study, since the activation energy of the smelling pepper was 31.01 kJ·mol−1 and was 30.11 kJ·mol−1 in the pout pepper, respectively. Thermodynamic properties in both processes of drying the peppers pointed to a non-spontaneous process, with positive values of enthalpy and Gibbs free energy and negative values of entropy. Regarding the influence of drying on the proximal composition, it was observed that, with the increase in temperature, there was a decrease in the water content and the concentration of macronutrients (lipids, proteins, and carbohydrates), providing an increase in the energy value. The powders obtained in the study were presented as an alternative for the technological and industrial use of peppers, favoring obtaining a new condiment, rich in bioactives, providing the market with a new option of powdered product that can be consumed directly and even adopted by the industry as a raw material in the preparation of mixed seasonings and in the formulation of various food products.
Antifungal Activity of Menthol, Eugenol and Their Combination against Aspergillus ochraceus and Aspergillus niger In Vitro and in Stored Cereals
Authors: Ben Miri Yamina , Nouasri Ahmed , Herrera Marta , Djenane Djamel , Ariño Agustín
Published: May 2023

Aspergillus ochraceus and Aspergillus niger are spoilage and mycotoxin-producing fungi that can contaminate agricultural commodities and derived products. In the present study, menthol, eugenol, and their combination (mix 1:1) were tested to determine their contact and fumigation toxicity against the two fungi. Menthol, eugenol, and their mixture significantly reduced mycelial growth and spore germination at concentrations from 300 to 600 µg/mL, and the inhibitory effects showed clear dose dependence. The minimum inhibitory concentration (MIC) values against A. ochraceus were 500 µg/mL (menthol), 400 µg/mL (eugenol), and 300 µg/mL (mix 1:1), while the MIC values for A. niger were 500 µg/mL (menthol), 600 µg/mL (eugenol), and 400 µg/mL (mix 1:1). Additionally, the analyzed compounds exhibited more than 50% protection against A. ochraceus and A. niger by fumigation of stored cereal grains (maize, barley, and rice) in sealed containers. The binary mixture of menthol and eugenol showed synergistic effects against both fungi in both in vitro direct contact and stored grain fumigation trials. The results of the present study provide a scientific basis for the application of a combination of natural antifungals in food preservation.
The Impact of Pulsed Electric Fields on Milk’s Macro- and Micronutrient Profile: A Comprehensive Review
Authors: Mohamad Azizah , Shah Nor Nadiah Abdul Karim , Sulaiman Alifdalino , Mohd Adzahan Noranizan , Arshad Rai Naveed , Aadil Rana Muhammad
Published: May 2023

Consumers around the world are attracted to products with beneficial effects on health. The stability, functionality, and integrity of milk constituents are crucial determinants of product quality in the dairy industry. Milk contains macronutrients and micronutrients that aid in a wide range of physiological functions in the human body. Deficiencies of these two types of nutrients can confine growth in children and increase the risk of several diseases in adults. The influence of pulsed electric fields (PEF) on milk has been extensively reviewed, mostly concentrating on the inactivation of microbes and enzymes for preservation purposes. Therefore, the information on the variations of milk macro- and micronutrients treated by PEF has yet to be elucidated and it is imperative as it may affect the functionality, stability, and integrity of the milk and dairy products. In this review, we describe in detail the introduction, types, and components of PEF, the inactivation mechanism of biological cells by PEF, as well as the effects of PEF on macro- and micronutrients in milk. In addition, we also cover the limitations that hinder the commercialization and integration of PEF in the food industry and the future outlook for PEF. The present review consolidates the latest research findings investigating the impact of PEF on the nutritional composition of milk. The assimilation of this valuable information aims to empower both industry professionals and consumers, facilitating a thorough understanding and meticulous assessment of the prospective adoption of PEF as an alternative technique for milk pasteurization.
Apple Grading Based on Multi-Dimensional View Processing and Deep Learning
Authors: Ji Wei , Wang Juncheng , Xu Bo , Zhang Tong
Published: May 2023

This research proposes an apple quality grading approach based on multi-dimensional view information processing using YOLOv5s network as the framework to rapidly and accurately perform the apple quality grading task. The Retinex algorithm is employed initially to finish picture improvement. Then, the YOLOv5s model, which is improved by adding ODConv dynamic convolution and GSConv convolution and VoVGSCSP lightweight backbone, is used to simultaneously complete the detection of apple surface defects and the identification and screening of fruit stem information, retaining only the side information of the apple multi-view. After that, the YOLOv5s network model-based approach for assessing apple quality is then developed. The introduction of the Swin Transformer module to the Resnet18 backbone increases the grading accuracy and brings the judgment closer to the global optimal solution. In this study, datasets were made using a total of 1244 apple images, each containing 8 to 10 apples. Training sets and test sets were randomly created and divided into 3:1. The experimental results demonstrated that in the multi-dimensional view information processing, the recognition accuracy of the designed fruit stem and surface defect recognition model reached 96.56% after 150 iteration training, the loss function value decreased to 0.03, the model parameter was only 6.78 M, and the detection rate was 32 frames/s. After 150 iteration training, the average grading accuracy of the quality grading model reached 94.46%, the loss function value decreased to 0.05, and the model parameter was only 3.78 M. The test findings indicate that the proposed strategy has a good application prospect in the apple grading task.
EL V.2 Model for Predicting Food Safety Risks at Taiwan Border Using the Voting-Based Ensemble Method
Authors: Wu Li-Ya , Liu Fang-Ming , Weng Sung-Shun , Lin Wen-Chou
Published: May 2023

Border management serves as a crucial control checkpoint for governments to regulate the quality and safety of imported food. In 2020, the first-generation ensemble learning prediction model (EL V.1) was introduced to Taiwan’s border food management. This model primarily assesses the risk of imported food by combining five algorithms to determine whether quality sampling should be performed on imported food at the border. In this study, a second-generation ensemble learning prediction model (EL V.2) was developed based on seven algorithms to enhance the “detection rate of unqualified cases” and improve the robustness of the model. In this study, Elastic Net was used to select the characteristic risk factors. Two algorithms were used to construct the new model: The Bagging-Gradient Boosting Machine and Bagging-Elastic Net. In addition, Fβ was used to flexibly control the sampling rate, improving the predictive performance and robustness of the model. The chi-square test was employed to compare the efficacy of “pre-launch (2019) random sampling inspection” and “post-launch (2020–2022) model prediction sampling inspection”. For cases recommended for inspection by the ensemble learning model and subsequently inspected, the unqualified rates were 5.10%, 6.36%, and 4.39% in 2020, 2021, and 2022, respectively, which were significantly higher (p < 0.001) compared with the random sampling rate of 2.09% in 2019. The prediction indices established by the confusion matrix were used to further evaluate the prediction effects of EL V.1 and EL V.2, and the EL V.2 model exhibited superior predictive performance compared with EL V.1, and both models outperformed random sampling.