bluuebunny/crossref_metadata_2025 huggingface dataset:
Technoeconomic Analysis of Intensified PEGylated Biopharmaceutical Recombinant Protein Production: Alpha Antitrypsin as a Model Case
Authors: Alkanaimsh Salem , Alsalal Abdullah M. , El-Touney Hesham
Published: May 2024

Alpha-1 antitrypsin deficiency (AATD) is a genetic disorder characterized by the insufficient production of the AAT protein. Due to availability limitations, not all AATD patients receive protein therapy treatment. In this study, the technoeconomic analysis of different processes (conventional and intensified) producing 200 kg/year of PEGylated recombinant AAT (PEG-AAT) using a Chinese hamster ovary cell line was investigated. All bioprocesses consist of upstream, downstream, and PEGylation sections. A base-case model (process A) of the conventional fed-batch production bioreactor was developed using SuperPro Designer software (Version 13) to evaluate the economic feasibility of the process. The cost of goods (COG) was estimated to be approximately USD 387.6/g. Furthermore, an intensified process (B) was modeled and evaluated to reduce the COG. Process intensification was implemented in the process (N-1 perfusion bioreactor). The specific operating COG for process B was found to be 10% less than that of process A. Scenario analysis was performed to assess the impact of process capacity (100–1000 kg/year) and cell-specific productivity (30–90 pg/cell/day). With an increase in process capacity, the specific operating COG was reduced for all processes. Increasing cell-specific productivity decreases the specific operating COG at different rates for each process, depending on the titer level. Future investigations into the PEGylation section are required since it has the highest COG of all the sections.
Numerical Investigation of Confining Pressure Effects on Microscopic Structure and Hydraulic Conductivity of Geosynthetic Clay Liners
Authors: Hou Juan , Sun Yinyu , Chu Chenxi , Sun Rui
Published: May 2024

A series of COMSOL numerical models were developed to explore how confining pressure impacts the microscopic structure and hydraulic conductivity of Geosynthetic Clay Liners (GCLs), taking into account the bentonite swelling ratio, mobile porosity, pore size, and tortuosity of the main flow path. The study reveals that the mobile porosity and pore size are critical factors affecting GCL hydraulic conductivity. As confining pressure increases, the transition of mobile water to immobile water occurs, resulting in a reduction in mobile water volume, the narrowing of pore channels, decreased flow velocity, and diminished hydraulic conductivity within the GCL. Mobile porosity undergoes a slight decrease from 0.273 to 0.104, while the ratio of mobile porosity to total porosity in the swelling process decreases significantly from 0.672 to 0.256 across the confining pressure range from 50 kPa to 500 kPa, which indicates a transition of mobile water toward immobile water. The tortuosity of the main flow path shows a slight increase, fluctuating within the range of 1.30 to 1.36, and maintains a value of around 1.34 as the confining pressure rises from 50 kPa to 500 kPa. At 50 kPa confining pressure, the minimum pore width measures 5.2 × 10−5 mm, with a corresponding hydraulic conductivity of 6.2 × 10−11 m/s. With an increase in confining pressure to 300 kPa, this compression leads to a narrower minimum pore width of 1.81 × 10−5 mm and a decrease in hydraulic conductivity to 5.11 × 10−12 m/s. The six-fold increase in confining pressure reduces hydraulic conductivity by one order of magnitude. A theoretical equation was derived to compute the hydraulic conductivity of GCLs under diverse confining pressure conditions, indicating a linear correlation between the logarithm of hydraulic conductivity and confining pressure, and exhibiting favorable agreement with experimental findings.
Construction and Investigation of a Filtration Efficiency Test System for High-Efficiency Filter Materials Based on Mass Concentration
Authors: Wei Fang , Liang Yun , Wang Hao , Hu Mengxiang , Wang Lingyun , Wang Desheng , Tang Min
Published: May 2024

Protection from nuclear biochemical aerosol and air pollution pays attention to aerosol mass concentration. The concentration of upstream aerosol of the commonly used filtration efficiency detection device for high-efficiency filter materials is low, making it insufficient for detecting the filtration efficiency of high-efficiency filter materials. This study designed and built a set of filtration efficiency detection devices for high-efficiency filter materials based on mass concentration. By adjusting the oil bath temperature, injection pressure, the degree of spiral-separator separation, as well as the number and size of nozzles, we investigated the effects of each condition on the concentration and particle size distribution of aerosol generation. As a result, the oil mist generator of the device can stably generate high-concentration aerosol with a mass concentration of up to 1587.9 mg/m3 and a number concentration of up to 107–108 P/cm3. The high-concentration aerosol generated can meet the E11–U15 filter material performance requirements.
Local Path Planner for Mobile Robot Considering Future Positions of Obstacles
Authors: Ou Xianhua , You Zhongnan , He Xiongxiong
Published: May 2024

Local path planning is a necessary ability for mobile robot navigation, but existing planners are not sufficiently effective at dynamic obstacle avoidance. In this article, an improved timed elastic band (TEB) planner based on the requirements of mobile robot navigation in dynamic environments is proposed. The dynamic obstacle velocities and TEB poses are fully integrated through two-dimensional (2D) lidar and multi-obstacle tracking. First, background point filtering and clustering are performed on the lidar points to obtain obstacle clusters. Then, we calculate the data association matrix of the obstacle clusters of the current and previous frame so that the clusters can be matched. Thirdly, a Kalman filter is adopted to track clusters and obtain the optimal estimates of their velocities. Finally, the TEB poses and obstacle velocities are associated: we predict the obstacle position corresponding to the TEB pose through the detected obstacle velocity and add this constraint to the corresponding TEB pose vertex. Then, a pose sequence considering the future positions of obstacles is obtained through a graph optimization algorithm. Compared with the original TEB, our method reduces the total running time by 22.87%, reduces the running distance by 19.23%, and increases the success rate by 21.05%. Simulations and experiments indicate that the improved TEB enables robots to efficiently avoid dynamic obstacles and reach the goal as quickly as possible.
Fault Diagnosis of Wind Turbine Gearbox Using Vibration Scatter Plot and Visual Geometric Group Network
Authors: Wang Meng-Hui , Hung Chun-Chun , Lu Shiue-Der , Chen Fu-Hao , Su Yu-Xian , Kuo Cheng-Chien
Published: May 2024

This study aims to develop a fault detection system designed specifically for wind turbine gearboxes. It proposes a hybrid fault diagnosis algorithm that combines scatter plot analysis with the visual geometric group (VGG) technique to identify various fault types, including gear rust, chipping, wear, and aging. To capture vibration signals, a three-axis vibration sensor was integrated with a NI-9234 DAQ card. Digital signal processing techniques were employed to actively filter out noise from the captured signals. Gaussian white noise was incorporated into the training data to enhance the noise resistance of the network model, which was then utilized for scatter plot generation. The VGG technique was subsequently applied to identify faults. The testing data were collected at two different speeds, with 1500 samples taken at each speed, totaling 3000 samples. For both training and testing, 400 samples of each fault type were employed for training, while 200 samples were allocated for testing. The test results demonstrated an overall identification accuracy of 97.7% for both the no-fault gearbox and the four-fault states, underscoring the effectiveness of the proposed methodology.
Sustainability Assessment of 2G Bioethanol Production from Residual Lignocellulosic Biomass
Authors: Correia Bárbara , Matos Henrique A. , Lopes Tiago F. , Marques Susana , Gírio Francisco
Published: May 2024

The development of sustainable biofuels can help to reduce the reliance on fossil fuels and mitigate the impact of climate change. This study analyzes bioethanol production from agro-forestry residual biomass, namely eucalyptus residues and corn stover. The study includes process simulation using Aspen Plus software, followed by economic analysis and life cycle assessment (LCA) with the help of SimaPro software and by applying the environmental footprint (EF) 3.0 method. The economic analysis on the biorefinery’s economic viability, equipment, and production costs reveals a positive decision for bioethanol production from eucalyptus residues due to logistical and transportation costs. The minimum ethanol selling price (MESP) obtained was 2.19 €/L and 2.45 €/L for eucalyptus residues and corn stover, respectively. From the LCA with a functional unit of 1 MJ of ethanol, bioethanol production from eucalyptus residues results in a single score impact of 37.86 µPt, whereas for corn stover, it is 33.47 µPt. In the climate change impact category, the eucalyptus residues scenario has an impact of 0.264 kg CO2 eq/MJ ethanol while corn stover leads to 0.254 kg CO2 eq/MJ ethanol. In-situ enzyme production, heat integration, and the use of renewable energy sources were also analyzed. Combining in situ enzyme production with renewable energy sources lowers CO2 equivalent emissions by 89% for both feedstocks, in comparison to the base-case scenario.
Enhancing Low-Fat Probiotic Yogurt: The Role of Xanthan Gum in Functionality and Microbiological Quality
Authors: Alsaleem Khalid A. , Hamouda Mahmoud E. A.
Published: May 2024

The objective of this study was to determine the effect of XG addition on low-fat yogurt (LFY) properties. Pasteurized skimmed buffalo milk (SBM) was heated to 95 ± 2 °C for 16 s, cooled to 40 ± 1 °C, and then divided into six treatment lots. The treatments included the following: T1 (control), T2 (0.2% XG), T3 (0.4% XG), T4 (0.6% XG), T5 (0.8% XG), and T6 (1% XG). A proportion of 2% of a mixed starter culture from Streptococcus thermophilus (ST), Lactobacillus bulgaricus (LB), and Bifidobacterium bifidum (BB) in the ratio 1:1:1 was added. Yogurt was manufactured following the standard manufacturing protocol. Chemical composition and texture were determined at fresh time, while water-holding capacity (WHC), viscosity, and syneresis % were determined at 0, 7, 14, and 21 days of storage. Total bacterial counts (TBC), lactobacilli, streptococci, and bifidobacteria counts were determined at 0, 7, 14, and 21 days of storage. Sensory analysis was performed immediately upon the cooling stage (time zero) and then after 14 and 21 days of storage. The experiment was performed in trice. The results obtained showed that the addition of XG in LFY significantly (p < 0.05) decreases the pH, total protein (TP), and ash, and significantly (p < 0.05) increased the total solids (TS). Additionally, the addition of XG led to a significant (p < 0.05) increase in hardness, WHC, and viscosity; however, syneresis significantly (p < 0.05) decreased. The addition of higher amounts of XG led to a significant (p < 0.05) decrease in the TBC and led to a significant (p < 0.05) increase in counts of ST, LB, and BB during the first two weeks of the storage period. Sensory evaluation revealed that increasing the XG concentration up to 0.8% increased the product’s acceptability among panelists; however, further increasing the concentration to 1% had a detrimental impact on its acceptability. To conclude, this study showed that XG can be used as a stabilizer in the manufacturing of LFY as well as a prebiotic for starter culture and improve the quality of LFY.
Machine Learning Algorithms That Emulate Controllers Based on Particle Swarm Optimization—An Application to a Photobioreactor for Algal Growth
Authors: Mînzu Viorel , Arama Iulian , Rusu Eugen
Published: May 2024

Particle Swarm Optimization (PSO) algorithms within control structures are a realistic approach; their task is often to predict the optimal control values working with a process model (PM). Owing to numerous numerical integrations of the PM, there is a big computational effort that leads to a large controller execution time. The main motivation of this work is to decrease the computational effort and, consequently, the controller execution time. This paper proposes to replace the PSO predictor with a machine learning model that has “learned” the quasi-optimal behavior of the couple (PSO and PM); the training data are obtained through closed-loop simulations over the control horizon. The new controller should preserve the process’s quasi-optimal control. In identical conditions, the process evolutions must also be quasi-optimal. The multiple linear regression and the regression neural networks were considered the predicting models. This paper first proposes algorithms for collecting and aggregating data sets for the learning process. Algorithms for constructing the machine learning models and implementing the controllers and closed-loop simulations are also proposed. The simulations prove that the two machine learning predictors have learned the PSO predictor’s behavior, such that the process evolves almost identically. The resulting controllers’ execution time have decreased hundreds of times while keeping their optimality; the performance index has even slightly increased.
Plasma Processing of Rubber Powder from End-of-Life Tires: Numerical Analysis and Experiment
Authors: Messerle Vladimir , Ustimenko Alexandr
Published: May 2024

Tire recycling is becoming an increasingly important problem due to the growing number of end-of-life tires (ELTs). World-wide, ELTs account for more than 80 million tons. ELTs contribute to environmental pollution in the long term. They are flammable, toxic and non-biodegradable. At the same time, ELTs contain rubber, metal and textile cord, which are valuable raw materials. ELTs are buried in landfills, burned, crushed and restored. Most of these methods have a negative impact on the environment. From an environmental point of view, the most preferred ways to recycle tires are retreading and shredding. Rubber powder (RP) or crumb is mainly used for rubber pavers production, waterproofing, curbs, road slabs and various surfaces. An alternative method for RP processing, eliminating the disadvantages of the above approaches, is plasma gasification and pyrolysis. The paper presents a thermodynamic and kinetic analysis and an experiment on plasma processing of RP from worn tires to produce flammable gas. At a mass-average temperature of 1750 K, the yield of synthesis gas from plasma-air gasification of RP was 44.6% (hydrogen—19.1, carbon monoxide—25.5), and 95.6% of carbon was gasified. The experimental and calculated results satisfactorily agreed. It was found that plasma products from RP did not contain harmful impurities, either in calculations or experiments. Plasma gasification allows for recycling ELTs in an environmentally friendly way while also generating flammable gases that are valuable commodities. In this research, plasma technology was demonstrated to be effective for gasifying RP to produce flammable gas.
Comparative Review on the Production and Purification of Bioethanol from Biomass: A Focus on Corn
Authors: Assaf Jean Claude , Mortada Zeinab , Rezzoug Sid-Ahmed , Maache-Rezzoug Zoulikha , Debs Espérance , Louka Nicolas
Published: May 2024

In the contemporary era, conventional energy sources like oil, coal, and natural gas overwhelmingly contribute 89.6% to global CO2 emissions, intensifying environmental challenges. Recognizing the urgency of addressing climate concerns, a pivotal shift towards renewable energy, encompassing solar, wind, and biofuels, is crucial for bolstering environmental sustainability. Bioethanol, a globally predominant biofuel, offers a versatile solution, replacing gasoline or integrating into gasoline–ethanol blends while serving as a fundamental building block for various valuable compounds. This review investigates the dynamic landscape of biomass generations, drawing insightful comparisons between the first, second, third, and fourth generations. Amid the drive for sustainability, the deliberate focus on the initial generation of biomass, particularly corn, in bioethanol production is grounded in the current dependence on edible crops. The established utilization of first-generation biomass, exemplified by corn, underscores the necessity for a comprehensive examination of its advantages and challenges, allowing for a nuanced exploration of existing infrastructure and practices. To produce bioethanol from corn feedstock, various milling methods can be employed. Thus, this paper delves into a comparative assessment of dry-milling and wet-milling processes scrutinizing their efficiency, environmental impact, and economic feasibility.
A Study on the Mechanism of Fracture Initiation and Propagation under Multi-Perforation Conditions in Hydraulic Fracturing
Authors: Ding Dongwei , Xiong Wei , Guo Wei , Yu Haiqing , Wang Keyuan
Published: May 2024

To reveal the mechanism of hydraulic fracture initiation and propagation under the conditions of multiple perforations during horizontal well fracturing, we creatively conducted dual-hole fracturing experiments on small rock samples and established a two-dimensional model of a single cluster with multiple perforations in a horizontal well based on the extended finite element method using the fluid–solid coupling equation, which was combined with the basic theory of damage mechanics. The biggest difference from previous research is that this model does not consider the hypothesis of stress shadows and only focuses on studying the initiation and propagation of multiple perforations in one cluster. We studied the effects of perforation parameters, stress state, and injection flow rate on the initiation and propagation of hydraulic fractures using this model. The experimental and simulation results indicate that under multi-perforation conditions, the number of fractures depends on the number of perforations. The simulation results show that when the spacing between perforations increases or the number of perforations reduces, the initiation time of perforation is advanced and the interference between fractures weakens, which is conducive to the initiation and propagation of hydraulic fractures. As the stress difference increases, the initiation time of perforation becomes earlier and the deflection angle of the outermost fractures becomes smaller, which is conducive to the parallel expansion of the fractures. Moreover, although this has little impact on the morphology of fractures with the rise in flow rate in simulation, it is beneficial for improving the initiation and propagation speed of fractures. The length of fractures also increases significantly at the same time point. In addition, both the experiments and simulations revealed that an increase in the flow rate could accelerate the initiation time of fractures. The proposed model can guide fracturing construction to optimize the design of perforation spacing during horizontal well fracturing, which can contribute to reducing development costs and improving the final production.
Correction: AL-Aoh, H.A. Removal of the Pigment Congo Red from Synthetic Wastewater with a Novel and Inexpensive Adsorbent Generated from Powdered Foeniculum Vulgare Seeds. Processes 2023, 11, 446
Authors: AL-Aoh Hatem A.
Published: May 2024

In the original publication [...]
Screening of Metal Reduction Potential for Thermochemical Hydrogen Storage
Authors: Voglar Jure , Likozar Blaž
Published: May 2024

The screening of all non-radioactive metals without lanthanides for thermochemical hydrogen storage was performed based on physical chemistry calculations. The thermodynamic data were collected from the NIST (National Institute of Standards and Technology) public data repository, which was followed by calculations regarding the change in enthalpy, entropy, Gibbs free energy and equilibrium reaction temperature. The results were critically evaluated based on thermodynamic parameters, viable metals were identified, and their hydrogen storage densities and energy–enthalpy ratios were evaluated. The elements viable for controlled thermochemical hydrogen storage via the reversible reduction and oxidation of metal oxides and metals are manganese (Mn), iron (Fe), molybdenum (Mo) and tungsten (W). Manganese has the largest theoretical potential for hydrogen storage with reversible reduction and oxidation of metal oxides and metals. The second candidate is iron, while the other two (Mo and W) have much lower potential. More research efforts should be dedicated to experimental testing of the identified metals (Mn, Fe, Mo and W) and their different oxides for thermochemical hydrogen storage capabilities both on laboratory and pilot scales. Ferromanganese alloy(s) might also prove itself as an efficient and affordable thermochemical hydrogen storage material. Our theoretical investigation expanded the knowledge on thermochemical hydrogen storage and is accompanied with a brief literature review revealing the lack of experimental studies, especially on oxidation of metals with water vapor occurring during the hydrogen release phase of the cycle. Consequently, accurate modelling of transport, kinetics and other phenomena during hydrogen storage and release is scarce.
Production Forecasting at Natural Gas Wells
Authors: Prundurel Alina Petronela , Stan Ioana Gabriela , Pană Ion , Eparu Cristian Nicolae , Stoica Doru Bogdan , Ghețiu Iuliana Veronica
Published: May 2024

In Romania, natural gas production is concentrated in two large producers, OMV Petrom and Romgaz. However, there are also smaller companies in the natural gas production area. In these companies, the deposits are mostly mature, or new deposits have low production capacity. Thus, the production forecast is very important for the continued existence of these companies. The model is based on the pressure variation in the gas reservoir, and the exponential model with production decline is currently used by gas and oil producers. Following the variation in the production of the gas wells, we found that in many cases, the Gaussian and Hubbert forecast models are more suitable for simulating the production pattern of gas wells. The models used to belong to the category of poorly conditioned models, with little data, usually called gray models. Papers published in this category are based on data collected over a period of time and provide a forecast of the model for the next period. The mathematical method can lead to a very good approximation of the known data, as well as short-term forecasting in the continuation of the time interval, for which we have these data. The neural network method requires more data for the network learning stage. Increasing the number of known variables is conducive to a successful model. Often, we do not have this data, or obtaining it is expensive and uneconomical for short periods of possible exploitation. The network model sometimes captures a fairly local pattern and changing conditions require the model to be remade. The model is not valid for a large category of gas wells. The Hubbert and Gauss models used in the article have a more comprehensive character, including a wide category of gas wells whose behavior as evolutionary stages is similar. The model is adapted according to practical observations by reducing the production growth period; the layout is asymmetric around the production peak; and the production range is reduced. Thus, an attempt is made to replace the exponential model with the Hubbert and Gauss models, which were found to be in good agreement with the production values. These models were completed using the Monte Carlo method and matrix of risk evaluation. A better appreciation of monthly production, which is an important aspect of supply contracts, and cumulative production, which is important for evaluating the utility of the investment, is ensured. In addition, we can determine the risk associated with the realization of production at a certain moment of exploitation, generating a complete picture of the forecast over the entire operating interval. A comparison with production results on a case study confirms the benefits of the forecasting procedure used.
Modifications to Enhance Outcomes of Family-Based Treatment for Anorexia Nervosa: A Scoping Review
Authors: Pedersen Signe Holm , Carlsson Lasse , Bentz Mette
Published: May 2024

Family-based treatment (FBT) is recommended for anorexia nervosa (AN) in young people (YP). However, a substantial proportion of YP undergoing FBT do not recover. Several modifications to standard FBT have been tested to improve recovery rates. This review provides an updated overview of empirically tested modifications to FBT for AN in YP and estimates whether such modifications increase the percentage recovering. Computerized searches performed in five databases resulted in the inclusion of 43 papers (representing 40 original studies), highlighting that a variety of modifications to standard FBT have been tested and appear promising. However, only 11 studies compared the results of a modification to standard FBT. In conclusion, some modifications, such as parent-focused treatment, the addition of home treatment, or interventions for families at risk of non-response, appear to have the potential to improve the recovery rate, either at the group or subgroup level. Other modifications, such as FBT-based guided self-help, virtually delivered FBT, or FBT delivered during in- or day-patient stays, enable the dissemination of FBT principles to other contexts and to patient groups with limited access. Small additions to FBT, such as a workshop or parent-to-parent consultation, do not seem to improve the recovery rate.
Research Data Management in the Croatian Academic Community: A Research Study
Authors: Vrana Radovan
Published: May 2024

This paper presents the results of an empirical research study of Croatian scientists’ use and management of research data. This research study was carried out from 28 June 2023 until 31 August 2023 using an online questionnaire consisting of 28 questions. The answers of 584 respondents working in science were filtered out for further analysis. About three-quarters of the respondents used the research data of other scientists successfully. Research data were mostly acquired from colleagues from the same department or institution. Roughly half of the respondents did not ask other scientists directly for their research data. Research data are important to the respondents mostly for raising the quality of research. Repeating someone else’s research by using their research data is still a problem. Less than one-third of the respondents provided full access to their research data mostly due to their fear of misuse. The benefits of research data sharing were recognized but few of the respondents received any reward for it. Archiving research data is a significant problem for the respondents as they dominantly use their own computers prone to failure for that activity and do not think about long-term preservation. Finally, the respondents lacked deeper knowledge of research data management.
Fisher Information for a System Composed of a Combination of Similar Potential Models
Authors: Onate Clement Atachegbe , Okon Ituen B. , Eyube Edwin Samson , Omugbe Ekwevugbe , Emeje Kizito O. , Onyeaju Michael C. , Ajani Olumide O. , Akinpelu Jacob A.
Published: May 2024

The solutions to the radial Schrödinger equation for a pseudoharmonic potential and Kratzer potential have been studied separately in the past. Despite different reports on the Kratzer potential, the fundamental theoretical quantities such as Fisher information have not been reported. In this study, we obtain the solution to the radial Schrödinger equation for the combination of the pseudoharmonic and Kratzer potentials in the presence of a constant-dependent potential, utilizing the concepts and formalism of the supersymmetric and shape invariance approach. The position expectation value and momentum expectation value are calculated employing the Hellmann–Feynman Theory. These expectation values are then used to calculate the Fisher information for both position and momentum spaces in both the absence and presence of the constant-dependent potential. The results obtained revealed that the presence of the constant-dependent potential leads to an increase in the energy eigenvalue, as well as in the position and momentum expectation values. Additionally, the constant-dependent potential increases the Fisher information for both position and momentum spaces. Furthermore, the product of the position expectation value and the momentum expectation value, along with the product of the Fisher information, satisfies both Fisher’s inequality and Cramer–Rao’s inequality.
Doing the Word: Reawakening the Church to Save Society in Southern Africa
Authors: Tagwirei Kimion
Published: May 2024

Southern African societies are presently beleaguered by manifold socio-economic, political, and environmental challenges. Ordinary people long for answers to questions about how to mitigate these challenges. Meanwhile, the Church mostly preaches the gospel and establishes and grows denominations across the world. Proclaiming the gospel in word is good; however, without demonstrating the gospel with transformational deeds, the Church remains Salvationist and partially missionary. Bearing in mind that the integral mission of the Church is advancing the gospel holistically, fractional mindfulness of the gospel, hearing the words without performing the corresponding deeds, is defacing its identity. Today, this situation is problematic and helpless, as society is in dire need of a wholesome Church that acts in accordance with its own faith and values and attends to the soul, the body, and all other facets of life. Much has been published about the integral mission of the Church, though little has been said about its role in social action. By qualitatively reviewing the literature and observing the Southern African context and some biblical examples, this paper finds the integral mission to be the predominant and comprehensive purpose of the existence of the Church. While the Church could be aware of its mandate, it should be reawakened so that it becomes a doer of the Word and stands out as the salt and light of the world by contributing to addressing the needs of society.
Board Characteristics and Bank Stock Performance: Empirical Evidence from the MENA Region
Authors: Awad Antoine B. , Gharios Robert , Abu Khalaf Bashar , Seissian Lena A.
Published: May 2024

This study examined the relationship between the board characteristics and stock performance of commercial banks. Our analysis is based on a sample of 65 banks across 10 MENA countries and their quantitative data extracted between 2013 and 2022. This research employed pooled OLS, and fixed and random effect regression to confirm the association between board size, board independence, number of board meetings, and CEO duality with stock performance measured by the bank’s share price and market-to-book ratio. Further, several control variables were utilized such as the bank’s capital adequacy, profitability, and size. The empirical findings reveal that board independence positively affects the bank stock performance while the board size shows a negative relationship. This suggests that banks with fewer board members and high independence levels have their shares outperforming others. However, we found that having frequent board meetings per year and separate roles for the CEO and chairman have no impact on bank stock performance. Moreover, the findings indicate that the bank’s capital adequacy, size, and profitability have a positive effect on the stock performance. To test the robustness of our analysis, we implemented a one-limit Tobit model, which enables lower-bound censoring, and obtained similar findings thus confirming our hypotheses. From a practical perspective, our findings highlight the importance of the board size and the directors’ independence to MENA regulators and policymakers in an effort to implement an effective corporate governance system. Specifically, MENA banks are advised to decrease the number of board members, and this should reduce the number of annual board meetings which, in turn, should maximize performance.
Enhancing Semi-Supervised Few-Shot Hyperspectral Image Classification via Progressive Sample Selection
Authors: Zhao Jiaguo , Zhang Junjie , Huang Huaxi , Zhang Jian
Published: May 2024

Hyperspectral images (HSIs) provide valuable spatial–spectral information for ground analysis. However, in few-shot (FS) scenarios, the limited availability of training samples poses significant challenges in capturing the sample distribution under diverse environmental conditions. Semi-supervised learning has shown promise in exploring the distribution of unlabeled samples through pseudo-labels. Nonetheless, FS HSI classification encounters the issue of high intra-class spectral variability and inter-class spectral similarity, which often lead to the diffusion of unreliable pseudo-labels during the iterative process. In this paper, we propose a simple yet effective progressive pseudo-label selection strategy that leverages the spatial–spectral consistency of HSI pixel samples. By leveraging spatially aligned ground materials as connected regions with the same semantic and similar spectrum, pseudo-labeled samples were selected based on round-wise confidence scores. Samples within both spatially and semantically connected regions of FS samples were assigned pseudo-labels and joined subsequent training rounds. Moreover, considering the spatial positions of FS samples that may appear in diverse patterns, to fully utilize unlabeled samples that fall outside the neighborhood of FS samples but still belong to certain connected regions, we designed a matching active learning approach for expert annotation based on the temporal confidence difference. We identified samples with the highest training value in specific regions, utilizing the consistency between predictive labels and expert labels to decide whether to include the region or the sample itself in the subsequent semi-supervised iteration. Experiments on both classic and more recent HSI datasets demonstrated that the proposed base model achieved SOTA performance even with extremely rare labeled samples. Moreover, the extended version with active learning further enhances performance by involving limited additional annotation.
Glacier Changes from 1990 to 2022 in the Aksu River Basin, Western Tien Shan
Authors: Ren Pei , Pan Xiaohui , Liu Tie , Huang Yue , Chen Xi , Wang Xiaofei , Chen Ping , Akmalov Shamshodbek
Published: May 2024

Mountain glaciers are considered natural indicators of warming and a device for climatic change. In addition, it is also a solid reservoir of freshwater resources. Along with climate change, clarifying the dynamic changes of glacier in the Aksu River Basin (ARB) are important for hydrological processes. The study examined the variations in glacier area, elevation, and their reaction to climate change in the ARB between 1990 and 2022. The glacier melt on the runoff is explored from 2003 to 2020. This investigation utilized Landsat and Sentinal-2 images, ICESat, CryoSat, meteorological and hydrological data. The findings suggest that: (1) The glacier area in the ARB retreated by 309.40 km2 (9.37%, 0.29%·a−1) from 1990 to 2022. From 2003 to 2021, the ARB glacier surface elevation retreat rate of 0.38 ± 0.12 m·a−1 (0.32 ± 0.10 m w.e.a−1). Comparison with 2003–2009, the retreat rate is faster from 2010 to 2021. (2) From 1990 to 2022, the Toxkan and the Kumalak River Basin’s glacier area decreases between 61.28 km2 (0.28%·a−1) and 248.13 km2 (0.30%·a−1). Additionally, the rate of glacier surface elevation declined by −0.34 ± 0.11 m·a−1, −0.42 ± 0.14 m·a−1 from 2003 to 2021. (3) The mass balance sensitivities to cold season precipitation and ablation-phase accumulated temperatures are +0.27 ± 0.08 m w.e.a−1(10%)−1 and −0.33 ± 0.10 m w.e.a−1 °C−1, respectively. The mass loss is (962.55 ± 0.57) × 106 m3 w.e.a−1, (1087.50 ± 0.68) × 106 m3 w.e.a−1 during 2003–2009, 2010–2021 respectively. Warmer ablation-phase accumulated temperatures dominate glacier retreat in the ARB. (4) Glacier meltwater accounted for 34.57% and 41.56% of the Aksu River’s runoff during the ablation-phase of 2003–2009 and 2010–2020, respectively. The research has important implications for maintaining the stability of water resource systems based on glacier meltwater.
Effect of the One-to-Many Relationship between the Depth and Spectral Profile on Shallow Water Depth Inversion Based on Sentinel-2 Data
Authors: Huang Erhui , Chen Benqing , Luo Kai , Chen Shuhan
Published: May 2024

In shallow water, Sentinel-2 multispectral imagery has only four visible bands and limited quantization levels, which easily leads to the occurrence of the same spectral profile but different depth (SSPBDD) phenomenon, resulting in a one-to-many relationship between water depth and spectral profile. Investigating the impact of this relationship on water depth inversion models is the main objective of this paper. The Stumpf model and three machine learning models (Random Forest, Support Vector Machine, and Mixture Density Network) are employed, and the performance of these models is analysed based on the spatial distribution of the training dataset and the input information composition of these models. The results show that the root mean square errors (RMSEs) of the depth inversion of Random Forest and Support Vector Machine are significantly affected by the spatial distribution of the training dataset, while minimal effects are observed for the Stumpf model and the Mixture Density Network model. The SSPBDD phenomenon is widespread in Sentinel-2 images at all depths, particularly between 5 m and 15 m, with most of the depth maximum difference of the SSPBDD pixels ranging from 0 to 5 m. The SSPBDDs phenomenon can significantly reduce the inversion accuracy of any model. The number and the depth maximum difference of the SSPBDDs pixels are the main influencing factors. However, by increasing the visible spectral information and the spatial neighbourhood information in the input layer of machine learning models, the inversion accuracy and stability of the models can be improved to a certain extent. Among the models, the Mixture Density Network achieves the best inversion accuracy and stability.
Fine-Grained Cross-Modal Semantic Consistency in Natural Conservation Image Data from a Multi-Task Perspective
Authors: Tao Rui , Zhu Meng , Cao Haiyan , Ren Honge
Published: May 2024

Fine-grained representation is fundamental to species classification based on deep learning, and in this context, cross-modal contrastive learning is an effective method. The diversity of species coupled with the inherent contextual ambiguity of natural language poses a primary challenge in the cross-modal representation alignment of conservation area image data. Integrating cross-modal retrieval tasks with generation tasks contributes to cross-modal representation alignment based on contextual understanding. However, during the contrastive learning process, apart from learning the differences in the data itself, a pair of encoders inevitably learns the differences caused by encoder fluctuations. The latter leads to convergence shortcuts, resulting in poor representation quality and an inaccurate reflection of the similarity relationships between samples in the original dataset within the shared space of features. To achieve fine-grained cross-modal representation alignment, we first propose a residual attention network to enhance consistency during momentum updates in cross-modal encoders. Building upon this, we propose momentum encoding from a multi-task perspective as a bridge for cross-modal information, effectively improving cross-modal mutual information, representation quality, and optimizing the distribution of feature points within the cross-modal shared semantic space. By acquiring momentum encoding queues for cross-modal semantic understanding through multi-tasking, we align ambiguous natural language representations around the invariant image features of factual information, alleviating contextual ambiguity and enhancing model robustness. Experimental validation shows that our proposed multi-task perspective of cross-modal momentum encoders outperforms similar models on standardized image classification tasks and image–text cross-modal retrieval tasks on public datasets by up to 8% on the leaderboard, demonstrating the effectiveness of the proposed method. Qualitative experiments on our self-built conservation area image–text paired dataset show that our proposed method accurately performs cross-modal retrieval and generation tasks among 8142 species, proving its effectiveness on fine-grained cross-modal image–text conservation area image datasets.
Decentralized Navigation with Optimality for Multiple Holonomic Agents in Simply Connected Workspaces
Authors: Kotsinis Dimitrios , Bechlioulis Charalampos P.
Published: May 2024

Multi-agent systems are utilized more often in the research community and industry, as they can complete tasks faster and more efficiently than single-agent systems. Therefore, in this paper, we are going to present an optimal approach to the multi-agent navigation problem in simply connected workspaces. The task involves each agent reaching its destination starting from an initial position and following an optimal collision-free trajectory. To achieve this, we design a decentralized control protocol, defined by a navigation function, where each agent is equipped with a navigation controller that resolves imminent safety conflicts with the others, as well as the workspace boundary, without requesting knowledge about the goal position of the other agents. Our approach is rendered sub-optimal, since each agent owns a predetermined optimal policy calculated by a novel off-policy iterative method. We use this method because the computational complexity of learning-based methods needed to calculate the global optimal solution becomes unrealistic as the number of agents increases. To achieve our goal, we examine how much the yielded sub-optimal trajectory deviates from the optimal one and how much time the multi-agent system needs to accomplish its task as we increase the number of agents. Finally, we compare our method results with a discrete centralized policy method, also known as a Multi-Agent Poli-RRT* algorithm, to demonstrate the validity of our method when it is attached to other research algorithms.
Microcontroller-Optimized Measurement Electronics for Coherent Control Applications of NV Centers
Authors: Stiegekötter Dennis , Pogorzelski Jens , Horsthemke Ludwig , Hoffmann Frederik , Gregor Markus , Glösekötter Peter
Published: May 2024

Long coherence times at room temperature make the NV center a promising candidate for quantum sensors and quantum computers. The necessary coherent control of the electron spin triplet in the ground state requires microwave π pulses in the nanosecond range, obtained from the Rabi oscillation of the mS spin states of the magnetic resonances of the NV centers. Laboratory equipment has a high temporal resolution for these measurements but is expensive and, therefore, uninteresting for fields such as education. In this work, we present measurement electronics for NV centers that are optimized for microcontrollers. It is shown that the Rabi frequency is linear to the output of the digital-to-analog converter (DAC) and is used to adapt the time length π of the electron spin flip, to the limited pulse width resolution of the microcontroller. This was achieved by breaking down the most relevant functions of conventional laboratory devices and replacing them with commercially available integrated components. The result is a cost-effective handheld setup for coherent control applications of NV centers.
Safe Autonomous Driving with Latent Dynamics and State-Wise Constraints
Authors: Wang Changquan , Wang Yun
Published: May 2024

Autonomous driving has the potential to revolutionize transportation, but developing safe and reliable systems remains a significant challenge. Reinforcement learning (RL) has emerged as a promising approach for learning optimal control policies in complex driving environments. However, existing RL-based methods often suffer from low sample efficiency and lack explicit safety constraints, leading to unsafe behaviors. In this paper, we propose a novel framework for safe reinforcement learning in autonomous driving that addresses these limitations. Our approach incorporates a latent dynamic model that learns the underlying dynamics of the environment from bird’s-eye view images, enabling efficient learning and reducing the risk of safety violations by generating synthetic data. Furthermore, we introduce state-wise safety constraints through a barrier function, ensuring safety at each state by encoding constraints directly into the learning process. Experimental results in the CARLA simulator demonstrate that our framework significantly outperforms baseline methods in terms of both driving performance and safety. Our work advances the development of safe and efficient autonomous driving systems by leveraging the power of reinforcement learning with explicit safety considerations.
Intelligent Vehicle Path Planning Based on Optimized A* Algorithm
Authors: Chu Liang , Wang Yilin , Li Shibo , Guo Zhiqi , Du Weiming , Li Jinwei , Jiang Zewei
Published: May 2024

With the rapid development of the intelligent driving technology, achieving accurate path planning for unmanned vehicles has become increasingly crucial. However, path planning algorithms face challenges when dealing with complex and ever-changing road conditions. In this paper, aiming at improving the accuracy and robustness of the generated path, a global programming algorithm based on optimization is proposed, while maintaining the efficiency of the traditional A* algorithm. Firstly, turning penalty function and obstacle raster coefficient are integrated into the search cost function to increase the adaptability and directionality of the search path to the map. Secondly, an efficient search strategy is proposed to solve the problem that trajectories will pass through sparse obstacles while reducing spatial complexity. Thirdly, a redundant node elimination strategy based on discrete smoothing optimization effectively reduces the total length of control points and paths, and greatly reduces the difficulty of subsequent trajectory optimization. Finally, the simulation results, based on real map rasterization, highlight the advanced performance of the path planning and the comparison among the baselines and the proposed strategy showcases that the optimized A* algorithm significantly enhances the security and rationality of the planned path. Notably, it reduces the number of traversed nodes by 84%, the total turning angle by 39%, and shortens the overall path length to a certain extent.