bluuebunny/crossref_metadata_2025 huggingface dataset:
Fog-Assisted Deep-Learning-Empowered Intrusion Detection System for RPL-Based Resource-Constrained Smart Industries
Authors: Attique Danish , Wang Hao , Wang Ping
Published: December 2022

The Internet of Things (IoT) is a prominent and advanced network communication technology that has familiarized the world with smart industries. The conveniently acquirable nature of IoT makes it susceptible to a diversified range of potential security threats. The literature has brought forth a plethora of solutions for ensuring secure communications in IoT-based smart industries. However, resource-constrained sectors still demand significant attention. We have proposed a fog-assisted deep learning (DL)-empowered intrusion detection system (IDS) for resource-constrained smart industries. The proposed Cuda–deep neural network gated recurrent unit (Cu-DNNGRU) framework was trained on the N-BaIoT dataset and was evaluated on judicious performance metrics, including accuracy, precision, recall, and F1-score. Additionally, the Cu-DNNGRU was empirically investigated alongside state-of-the-art classifiers, including Cu-LSTMDNN, Cu-BLSTM, and Cu-GRU. An extensive performance comparison was also undertaken among the proposed IDS and some outstanding solutions from the literature. The simulation results showed ample strength with respect to the validation of the proposed framework. The proposed Cu-DNNGRU achieved 99.39% accuracy, 99.09% precision, 98.89% recall, and an F1-score of 99.21%. In the performance comparison, the values were substantially higher than those of the benchmarked schemes, as well as competitive security solutions from the literature.
Efficient Methodology for Detection and Classification of Short-Circuit Faults in Distribution Systems with Distributed Generation
Authors: Santos Andréia da Silva , Faria Lucas Teles , Lopes Mara Lúcia M. , Lotufo Anna Diva P. , Minussi Carlos R.
Published: December 2022

Fault detection and classification are crucial procedures for electric power distribution systems because they can minimize the occurrence of faults. The methods for fault detection and classification have become more problematic because of the significant expansion of distributed energy resources in distribution systems and the change in their currents due to the action of short-circuiting. In this context, to fill this gap, this study presents a robust methodology for short-circuit fault detection and classification with the insertion of distributed generation units. The proposal methodology progresses in two stages: in the former stage, the detection is based on the continuous analysis of three-phase currents, whose characteristics are extracted through maximal overlap discrete wavelet transform. In the latter stage, the classification is based on three fuzzy inference systems to identify the phases with disturbance. The short-circuit type is identified by counting the shorted phases. The algorithm for short-circuit fault detection and classification is developed in MATLAB programming environment. The methodology is implemented in a modified IEEE 34-bus test system and modeled in ATPDraw with three scenarios with and without distributed generation units and considering the following parameters: fault type (single-phase, two-phase, and three-phase), angle of incidence, fault resistance (high impedance fault and low impedance fault), fault location bus, and distributed generation units (synchronous generators and photovoltaic panels). The accuracy is greater than 94.9% for the detection and classification of short-circuit faults for more than 20,000 simulated cases.
Evolution of Sensor Research for Clarifying the Dynamics and Properties of Future Directions
Authors: Coccia Mario , Roshani Saeed , Mosleh Melika
Published: December 2022

The principal goal of this study is to analyze the evolution of sensor research and technologies from 1990 to 2020 to clarify outlook and future directions. This paper applies network analysis to a large dataset of publications concerning sensor research covering a 30-year period. Results show that the evolution of sensors is based on growing scientific interactions within networks, between different research fields that generate co-evolutionary pathways directed to develop general-purpose and/or specialized technologies, such as wireless sensors, biosensors, fiber-optic, and optical sensors, having manifold applications in industries. These results show new directions of sensor research that can drive R&D investments toward promising technological trajectories of sensors, exhibiting a high potential of growth to support scientific, technological, industrial, and socioeconomic development.
SecMDGM: Federated Learning Security Mechanism Based on Multi−Dimensional Auctions
Authors: Chen Qian , Yao Lin , Wang Xuan , Lin Jiang Zoe , Wu Yulin , Ma Tianzi
Published: December 2022

As a newly emerging distributed machine learning technology, federated learning has unique advantages in the era of big data. We explore how to motivate participants to experience auctions more actively and safely. It is also essential to ensure that the final participant who wins the right to participate can guarantee relatively high−quality data or computational performance. Therefore, a secure, necessary and effective mechanism is needed through strict theoretical proof and experimental verification. The traditional auction theory is mainly oriented to price, not giving quality issues as much consideration. Hence, it is challenging to discover the optimal mechanism and solve the privacy problem when considering multi−dimensional auctions. Therefore, we (1) propose a multi−dimensional information security mechanism, (2) propose an optimal mechanism that satisfies the Pareto optimality and incentive compatibility named the SecMDGM and (3) verify that for the aggregation model based on vertical data, this mechanism can improve the performance by 2.73 times compared to that of random selection. These are all important, and they complement each other instead of being independent or in tandem. Due to security issues, it can be ensured that the optimal multi−dimensional auction has practical significance and can be used in verification experiments.
Data Filtering Method for Intelligent Vehicle Shared Autonomy Based on a Dynamic Time Warping Algorithm
Authors: Gao Zhenhai , Yu Tong , Sun Tianjun , Zhao Haoyuan
Published: December 2022

Big data already covers intelligent vehicles and is driving the autonomous driving industry’s transformation. However, the large amounts of driving data generated will result in complex issues and a huge workload for the test and verification processes of an autonomous driving system. Only effective and precise data extraction and recording aimed at the challenges of low efficiency, poor quality, and a long-time limit for traditional data acquisition can substantially reduce the algorithm development cycle. Based on the premise of driver-dominated vehicle movement, the virtual decision-making of autonomous driving systems under the accompanying state was considered as a reference. Based on a dynamic time warping algorithm and forming a data filtering approach under a dynamic time window, an automatic trigger recording control model for human-vehicle difference feature data was suggested. In this method, the data dimension was minimized, and the efficiency of the data mining was improved. The experimental findings showed that the suggested model decreased recorded invalid data by 75.35% on average and saved about 2.65 TB of data storage space per hour. Compared with industrial-grade methods, it saves an average of 307 GB of storage space per hour.
A Fault-Diagnosis Method for Railway Turnout Systems Based on Improved Autoencoder and Data Augmentation
Authors: Li Mengyang , Hei Xinhong , Ji Wenjiang , Zhu Lei , Wang Yichuan , Qiu Yuan
Published: December 2022

In recent years, with the rapid increase in coverage and lines, security maintenance has become one of the top concerns with regard to railway transportation in China. As the key transportation infrastructure, the railway turnout system (RTS) plays a vital role in transportation, which will cause incalculable losses when accidents occur. The traditional fault-diagnosis and maintenance methods of the RTS are no longer applicable to the growing amount of data, so intelligent fault diagnosis has become a research hotspot. However, the key challenge of RTS intelligent fault diagnosis is to effectively extract the deep features in the signal and accurately identify failure modes in the face of unbalanced datasets. To solve the above two problems, this paper focuses on unbalanced data and proposes a fault-diagnosis method based on an improved autoencoder and data augmentation, which realizes deep feature extraction and fault identification of unbalanced data. An improved autoencoder is proposed to smooth the noise and extract the deep features to overcome the noise fluctuation caused by the physical characteristics of the data. Then, synthetic minority oversampling technology (SMOTE) is utilized to effectively expand the fault types and solve the problem of unbalanced datasets. Furthermore, the health state is identified by the Softmax regression model that is trained with the balanced characteristics data, which improves the diagnosis precision and generalization ability. Finally, different experiments are conducted on a real dataset based on a railway station in China, and the average diagnostic accuracy reaches 99.13% superior to other methods, which indicates the effectiveness and feasibility of the proposed method.
Distributed Assessment of Virtual Insulin-Pump Settings Using SmartCGMS and DMMS.R for Diabetes Treatment
Authors: Ubl Martin , Koutny Tomas , Della Cioppa Antonio , De Falco Ivanoe , Tarantino Ernesto , Scafuri Umberto
Published: December 2022

Diabetes is a heterogeneous group of diseases that share a common trait of elevated blood glucose levels. Insulin lowers this level by promoting glucose utilization, thus avoiding short- and long-term organ damage due to the elevated blood glucose level. A patient with diabetes uses an insulin pump to dose insulin. The pump uses a controller to compute and dose the correct amount of insulin to keep blood glucose levels in a safe range. Insulin-pump controller development is an ongoing process aiming at fully closed-loop control. Controllers entering the market must be evaluated for safety. We propose an evaluation method that exploits an FDA-approved diabetic patient simulator. The method evaluates a Cartesian product of individual insulin-pump parameters with a fine degree of granularity. As this is a computationally intensive task, the simulator executes on a distributed cluster. We identify safe and risky combinations of insulin-pump parameter settings by applying the binomial model and decision tree to this product. As a result, we obtain a tool for insulin-pump settings and controller safety assessment. In this paper, we demonstrate the tool with the Low-Glucose Suspend and OpenAPS controllers. For average ± standard deviation, LGS and OpenAPS exhibited 1.7 ± 0.6% and 3.2 ± 1.8% of local extrema (i.e., good insulin-pump settings) out of all the entire Cartesian products, respectively. A continuous region around the best-discovered settings (i.e., the global extremum) of the insulin-pump settings spread across 4.0 ± 1.1% and 4.1 ± 1.3% of the Cartesian products, respectively.
Usability of the REHOME Solution for the Telerehabilitation in Neurological Diseases: Preliminary Results on Motor and Cognitive Platforms
Authors: Ferraris Claudia , Ronga Irene , Pratola Roberto , Coppo Guido , Bosso Tea , Falco Sara , Amprimo Gianluca , Pettiti Giuseppe , Lo Priore Simone , Priano Lorenzo , Mauro Alessandro , Desideri Debora
Published: December 2022

The progressive aging of the population and the consequent growth of individuals with neurological diseases and related chronic disabilities, will lead to a general increase in the costs and resources needed to ensure treatment and care services. In this scenario, telemedicine and e-health solutions, including remote monitoring and rehabilitation, are attracting increasing interest as tools to ensure the sustainability of the healthcare system or, at least, to support the burden for health care facilities. Technological advances in recent decades have fostered the development of dedicated and innovative Information and Communication Technology (ICT) based solutions, with the aim of complementing traditional care and treatment services through telemedicine applications that support new patient and disease management strategies. This is the background for the REHOME project, whose technological solution, presented in this paper, integrates innovative methodologies and devices for remote monitoring and rehabilitation of cognitive, motor, and sleep disorders associated with neurological diseases. One of the primary goals of the project is to meet the needs of patients and clinicians, by ensuring continuity of treatment from healthcare facilities to the patient’s home. To this end, it is important to ensure the usability of the solution by elderly and pathological individuals. Preliminary results of usability and user experience questionnaires on 70 subjects recruited in three experimental trials are presented here.
CUDM: A Combined UAV Detection Model Based on Video Abnormal Behavior
Authors: Cai Hao , Song Zhiguang , Xu Jianlong , Xiong Zhi , Xie Yuanquan
Published: December 2022

The widespread use of unmanned aerial vehicles (UAVs) has brought many benefits, particularly for military and civil applications. For example, UAVs can be used in communication, ecological surveys, agriculture, and logistics to improve efficiency and reduce the required workforce. However, the malicious use of UAVs can significantly endanger public safety and pose many challenges to society. Therefore, detecting malicious UAVs is an important and urgent issue that needs to be addressed. In this study, a combined UAV detection model (CUDM) based on analyzing video abnormal behavior is proposed. CUDM uses abnormal behavior detection models to improve the traditional object detection process. The work of CUDM can be divided into two stages. In the first stage, our model cuts the video into images and uses the abnormal behavior detection model to remove a large number of useless images, improving the efficiency and real-time detection of suspicious targets. In the second stage, CUDM works to identify whether the suspicious target is a UAV or not. Besides, CUDM relies only on ordinary equipment such as surveillance cameras, avoiding the use of expensive equipment such as radars. A self-made UAV dataset was constructed to verify the reliability of CUDM. The results show that CUDM not only maintains the same accuracy as state-of-the-art object detection models but also reduces the workload by 32%. Moreover, it can detect malicious UAVs in real-time.
Photostability Enhancement of Dual-Luminophore Pressure-Sensitive Paint by Adding Antioxidants
Authors: Uchida Kazuki , Ozawa Yuta , Asai Keisuke , Nonomura Taku
Published: December 2022

Antioxidants were applied to a dual-luminophore pressure-sensitive paint (PSP), and the effects on photodegradation caused by exposure to excitation light were studied. Three types of antioxidants that are commonly used for the photostability enhancement of polymers were added to a dual-luminophore PSP, and degradation rates and pressure/temperature sensitivities were investigated by coupon-based tests. One-hour-long aging tests were performed in a pressure chamber with a continuous excitation light source under dry air and argon atmospheres at 100 kPa and 20 °C. As a result of the aging tests, a singlet oxygen quencher type antioxidant was found to reduce the degradation rate by 91% when compared with the dual-luminophore PSP without antioxidants. This implies that singlet oxygen has a dominant role in the photodegradation mechanism of the dual-luminophore PSP.
Enhanced Classification of Dog Activities with Quaternion-Based Fusion Approach on High-Dimensional Raw Data from Wearable Sensors
Authors: Muminov Azamjon , Mukhiddinov Mukhriddin , Cho Jinsoo
Published: December 2022

The employment of machine learning algorithms to the data provided by wearable movement sensors is one of the most common methods to detect pets’ behaviors and monitor their well-being. However, defining features that lead to highly accurate behavior classification is quite challenging. To address this problem, in this study we aim to classify six main dog activities (standing, walking, running, sitting, lying down, and resting) using high-dimensional sensor raw data. Data were received from the accelerometer and gyroscope sensors that are designed to be attached to the dog’s smart costume. Once data are received, the module computes a quaternion value for each data point that provides handful features for classification. Next, to perform the classification, we used several supervised machine learning algorithms, such as the Gaussian naïve Bayes (GNB), Decision Tree (DT), K-nearest neighbor (KNN), and support vector machine (SVM). In order to evaluate the performance, we finally compared the proposed approach’s F-score accuracies with the accuracy of classic approach performance, where sensors’ data are collected without computing the quaternion value and directly utilized by the model. Overall, 18 dogs equipped with harnesses participated in the experiment. The results of the experiment show a significantly enhanced classification with the proposed approach. Among all the classifiers, the GNB classification model achieved the highest accuracy for dog behavior. The behaviors are classified with F-score accuracies of 0.94, 0.86, 0.94, 0.89, 0.95, and 1, respectively. Moreover, it has been observed that the GNB classifier achieved 93% accuracy on average with the dataset consisting of quaternion values. In contrast, it was only 88% when the model used the dataset from sensors’ data.
The Effects of Isometric Fatigue on Trunk Muscle Stiffness: Implications for Shear-Wave Elastography Measurements
Authors: Vatovec Rok , Kozinc Žiga , Voglar Matej
Published: December 2022

Muscle stiffness has been implicated as a possible factor in low back pain risk. There are few studies on the effects of isometric fatigue on the shear modulus of trunk muscles. This study aimed to investigate the effects of trunk isometric fatigue on the passive and active (during low and high-level contractions) shear moduli of the erector spinae (ES) and superficial and deep multifidus (MF) muscles. We assessed passive and active shear modulus using shear-wave elastography in healthy young participants (n = 22; 11 males, 11 females), before and after an isometric trunk extension fatigue protocol. Maximal voluntary force decreased from 771.2 ± 249.8 N before fatigue to 707.3 ± 204.1 N after fatigue (−8.64%; p = 0.003). Passive shear modulus was significantly decreased after fatigue in the MF muscle (p = 0.006–0.022; Cohen’s d = 0.40–46), but not the ES muscle (p = 0.867). Active shear modulus during low-level contraction was not affected by fatigue (p = 0.697–0.701), while it was decreased during high-level contraction for both muscles (p = 0.011; d = 0.29–0.34). Sex-specific analysis indicated the decrease in ES shear modulus was significant in males (p = 0.015; d = 0.31), but not in females (p = 0.140). Conversely, the shear modulus in superficial MF had a statistically significant decrease in females (p = 0.002; d = 0.74) but not in males (p = 0.368). These results have important implications for further investigations of the mechanistic interaction between physical workloads, sex, muscle stiffness (and other variables affecting trunk stability and neuromuscular control), and the development/persistence of low back pain.
Piston Sensing for Golay-6 Sparse Aperture System with Double-Defocused Sharpness Metrics via ResNet-34
Authors: Wang Senmiao , Wu Quanying , Fan Junliu , Chen Baohua , Chen Xiaoyi , Chen Lei , Shen Donghui , Yin Lidong
Published: December 2022

In pursuit of high imaging quality, optical sparse aperture systems must correct piston errors quickly within a small range. In this paper, we modified the existing deep-learning piston detection method for the Golay-6 array, by using a more powerful single convolutional neural network based on ResNet-34 for feature extraction; another fully connected layer was added, on the basis of this network, to obtain the best results. The Double-defocused Sharpness Metric (DSM) was selected first, as a feature vector to enhance the model performance; the average RMSE of the five sub-apertures for valid detection in our study was only 0.015λ (9 nm). This modified method has higher detecting precision, and requires fewer training datasets with less training time. Compared to the conventional approach, this technique is more suitable for the piston sensing of complex configurations.
Design of Audio-Augmented-Reality-Based O&M Orientation Training for Visually Impaired Children
Authors: Wei Linchao , Jin Lingling , Gong Ruining , Yang Yaojun , Zhang Xiaochen
Published: December 2022

Orientation and Mobility training (O&M) is a specific program that teaches people with vision loss to orient themselves and travel safely within certain contexts. State-of-the-art research reveals that people with vision loss expect high-quality O&M training, especially at early ages, but the conventional O&M training methods involve tedious programs and require a high participation of professional trainers. However, there is an insufficient number of excellent trainers. In this work, we first interpret and discuss the relevant research in recent years. Then, we discuss the questionnaires and interviews we conducted with visually impaired people. On the basis of field investigation and related research, we propose the design of a training solution for children to operate and maintain direction based on audio augmented reality. We discuss how, within the perceptible scene created by EasyAR’s map-aware framework, we created an AR audio source tracing training that simulates a social scene to strengthen the audiometric identification of the subjects, and then to verify the efficiency and feasibility of this scheme, we implemented the application prototype with the required hardware and software and conducted the subsequential experiments with blindfolded children. We confirm the high usability of the designed approach by analyzing the results of the pilot study. Compared with other orientation training studies, the method we propose makes the whole training process flexible and entertaining. At the same time, this training process does not involve excessive economic costs or require professional skills training, allowing users to undergo training at home or on the sports ground rather than having to go to rehabilitation sites or specified schools. Furthermore, according to the feedback from the experiments, the approach is promising in regard to gamification.
Comparative Study between Physics-Informed CNN and PCA in Induction Motor Broken Bars MCSA Detection
Authors: Boushaba Abderrahim , Cauet Sebastien , Chamroo Afzal , Etien Erik , Rambault Laurent
Published: December 2022

In this article, two methods for broken bar detection in induction motors are considered and tested using data collected from the LIAS laboratory at the University of Poitiers. The first approach is Motor Current Signature Analysis (MCSA) with Convolutional Neural Networks (CNN), in which measurements have to be processed in the frequency domain before training the CNN to ensure that the resulting model is physically informed. A double input CNN has been introduced to perform a 100% detection regardless of the speed and load torque value. A second approach is the Principal Components Analysis (PCA), in which the processing is undertaken in the time domain. The PCA is applied on the induction motor currents to eventually calculate the Q statistic that serves as a threshold for detecting anomalies/faults. Even if obtained results show that both approaches work very well, there are major differences that need to be pointed out, and this is the aim of the current paper.
A Robust Artificial Intelligence Approach with Explainability for Measurement and Verification of Energy Efficient Infrastructure for Net Zero Carbon Emissions
Authors: Moraliyage Harsha , Dahanayake Sanoshi , De Silva Daswin , Mills Nishan , Rathnayaka Prabod , Nguyen Su , Alahakoon Damminda , Jennings Andrew
Published: December 2022

Rapid urbanization across the world has led to an exponential increase in demand for utilities, electricity, gas and water. The building infrastructure sector is one of the largest global consumers of electricity and thereby one of the largest emitters of greenhouse gas emissions. Reducing building energy consumption directly contributes to achieving energy sustainability, emissions reduction, and addressing the challenges of a warming planet, while also supporting the rapid urbanization of human society. Energy Conservation Measures (ECM) that are digitalized using advanced sensor technologies are a formal approach that is widely adopted to reduce the energy consumption of building infrastructure. Measurement and Verification (M&V) protocols are a repeatable and transparent methodology to evaluate and formally report on energy savings. As savings cannot be directly measured, they are determined by comparing pre-retrofit and post-retrofit usage of an ECM initiative. Given the computational nature of M&V, artificial intelligence (AI) algorithms can be leveraged to improve the accuracy, efficiency, and consistency of M&V protocols. However, AI has been limited to a singular performance metric based on default parameters in recent M&V research. In this paper, we address this gap by proposing a comprehensive AI approach for M&V protocols in energy-efficient infrastructure. The novelty of the framework lies in its use of all relevant data (pre and post-ECM) to build robust and explainable predictive AI models for energy savings estimation. The framework was implemented and evaluated in a multi-campus tertiary education institution setting, comprising 200 buildings of diverse sensor technologies and operational functions. The results of this empirical evaluation confirm the validity and contribution of the proposed framework for robust and explainable M&V for energy-efficient building infrastructure and net zero carbon emissions.
Carbon Derivatives-Directed International Supervision Laws and Regulations and Carbon Market Mechanism
Authors: Cheng Yao
Published: December 2022

With the global acknowledgment of the Kyoto Protocol came the carbon derivatives such as carbon futures, options, and swap contracts. The innovative carbon derivatives are complex in design and contain risks that are difficult to predict and avoid. The global Carbon Market should have higher requirements in the supervision laws and regulations. To this end, the financial system theories and the financial characteristics of carbon derivatives are expounded. The three-dimensional structural modeling technique of systems engineering is introduced to construct the Carbon Market framework. The proposed framework factors for the organization, product, and policy dimensions of the Carbon Market are also described. Additionally, this model explains the market organization, the instruments and media connecting market supply and demand, and government regulation measures. In particular, the supervision and management aspects of the policy dimension are introduced in detail. The Carbon Market and relevant law systems in the United States, the European Union, and India are mainly studied and compared. Based on the comparison results, the necessity of market supervision is explained. Finally, the Big Vector Autoregression model is used to study the relationship between the Carbon Market, energy market, and financial market. After the introduction of the National Carbon Market, the correlation between the energy market and the financial market has become relatively complex but also presents a certain degree of asymmetry. According to the above results, the paper proposes to use the “regulatory sandbox” mechanism to improve the regulation of the subject and object of the carbon financial and legal relationship and try to carry out regulatory innovation for the risks of the entire carbon market.
Connections between Big Data and Smart Cities from the Supply Chain Perspective: Understanding the Impact of Big Data
Authors: Abdalla Alaa Amin , Abdalla Yousif Abdelbagi , Haddad Akarm M. , Bhavani Ganga , Zabalawi Eman
Published: December 2022

This study explores the impact of Big Data and smart city initiatives on supply chain management. The effects of smart cities and SCM integration on sustainable development are also examined. Big Data, smart cities, and supply chain characteristics have all received a significant amount of attention (supply network structure, governance mechanisms). Based on literature reviews, we created a comprehensive model for supply chains, Big Data, and smart cities. The study concluded that smart cities have various consequences for network architecture and governmental systems. Future research directions in supply chain management and smart cities are also addressed in this paper. A comprehensive model was developed that can be used to undertake empirical research on the implications of smart cities and Big Data on supply chain management and sustainable development in the future. Big Data, smart cities, and supply chains have more than merely causal interactions, and Big Data and smart cities will hugely impact sustainable development and SCM operations. Several studies have recently examined the use of information technology in supply chains, but few have specifically addressed smart cities and Big Data, according to literature analyses.
Removal of Metals by Biomass Derived Adsorbent in Its Granular and Powdered Forms: Adsorption Capacity and Kinetics Analysis
Authors: Aguiar Ana Beatriz Soares , Costa Josiel Martins , Santos Gabriela Espirito , Sancinetti Giselle Patrícia , Rodriguez Renata Piacentini
Published: December 2022

Among the various existing metals, zinc and copper are predominant metals in several effluents from industries such as electroplating, plastics production and mining. Technical methods have been applied in the treatment of effluents containing metals, including chemical removal, adsorption, ion exchange, membrane technologies and electrochemistry. However, it is necessary to develop technologies that minimize costs and increase treatment quality while reducing residual sludge generation. Adsorption using biological materials stands out for removing metals, a low-cost technique and high efficiency. Thus, this study evaluated metal adsorption using an adsorbent from granular and powdered anaerobic sludge, followed by a kinetic analysis, aiming at a new alternative for wastewater treatment. Evaluation of the copper and zinc adsorption process using granular and powdered biomass resulted in maximum removals of 72.9% and 62.7% for zinc, respectively, and 92.8% and 85.0% for copper, respectively. Analyzing the kinetic models, the pseudo-second-order model fitted the data better. Applying the kinetics of other studies in the literature for copper and zinc removal by other adsorbents, the pseudo-second-order model was the most representative model. In this context, kinetic modeling allowed the determination of the solute removal rate, estimating the adsorption mechanism.
Socio-Technical and Political Complexities: Findings from Two Case Studies of Large IT Project-Based Organizations
Authors: Eftekhari Navid Ahmadi , Mani Saba , Bakhshi Javad , Statsenko Larissa , Naeni Leila Moslemi
Published: December 2022

Information technology (IT) projects are becoming more complex due to technological advancements, increased sociopolitical demand, and competition. In recent years, the project complexity field has attracted increasing attention with diverse strategies and methods proposed to identify, evaluate, and respond to various complexities. This study aims to identify and prioritize factors contributing to complexity in IT projects by reporting two case studies conducted on large IT organizations. The literature on project complexity informed and guided this exploratory research. The data were collected through 21 semi-structured interviews and analyzed by applying open and axial coding content analysis. Underpinned by complexity theories, 19 factors contributing to the complexity of IT projects were identified, and their importance was highlighted using the Friedman test. The top five factors contributing to IT project complexity were identified as follows: the diversity of stakeholders; technological newness of the project; conflicting goals of stakeholders; variety of product sub-systems and components; and uncertainty of project objectives. This study’s findings contribute to the project management literature and inform practitioners about how to achieve more effective management of complex IT projects.
Design of a Diagnostic Immunoassay for Aflatoxin M1 Based on a Plant-Produced Antibody
Authors: Capodicasa Cristina , Bastiani Erica , Serra Thea , Anfossi Laura , Catellani Marcello
Published: December 2022

A new green competitive ELISA for aflatoxin M1 quantification in raw milk was developed. This diagnostic tool is based on an anti AFM1 mAb produced by plant molecular farming in alternative to classical systems. Our assay, showing an IC50 below 25 ng/L, fits with the requirements of EU legislation limits for AFM1 (50 ng/L). Optimal accuracy was achieved in correspondence of the decision levels (25 and 50 ng/L), and the assay enabled AFM1 quantification in the range 5–110 ng/L, with limit of detection 3 ng/L. Moreover, to evaluate a real applicability in diagnostics, raw milk-spiked samples were analysed, achieving satisfactory recovery rates of AFM1. In conclusion, an efficient and ready-to-use diagnostic assay for the quantification of aflatoxin M1 in milk, based on a plant-produced recombinant mAb, has been successfully developed.
eCry1Gb.1Ig, A Novel Chimeric Cry Protein with High Efficacy against Multiple Fall Armyworm (Spodoptera frugiperda) Strains Resistant to Different GM Traits
Authors: Chae Hyunsook , Wen Zhimou , Hootman Travis , Himes Jo , Duan Qianqian , McMath Joel , Ditillo Jesse , Sessler Richard , Conville Jared , Niu Ying , Matthews Phillip , Francischini Fabricio , Huang Fangneng , Bramlett Matthew
Published: December 2022

Spodoptera frugiperda (fall armyworm, FAW) is one of the most devastating insect pests to corn and soybean production in the Americas and is rapidly expanding its range worldwide. It is known to be hard to control either by chemical insecticide applications or by GM. Although the use of GM traits can be an effective way to control this pest, it is very rare to find native insecticidal proteins that provide the necessary level of FAW control in crop fields where FAW pressure and damage are high. Insecticidal Cry proteins sourced from Bacillus thuringiensis have been heavily utilized in the development of crops with GM traits; however, it is increasingly difficult to identify Cry proteins with unique modes of action. Protein engineering via a phylogenetically guided Cry protein domain swapping approach enabled us to discover novel chimeric Cry proteins engineered from inactive parent sequences. Some of these chimeras show excellent efficacy against key biotypes of FAW from Brazil and North America. In this study, we characterized a Cry-based chimera eCry1Gb.1Ig that is a very potent FAW toxin. eCry1Gb.1Ig showed high efficacy against multiple FAW strains that are resistant to various traits, including Cry1Fa, Vip3Aa and Cry1A.105/Cry2Ab. These results clearly indicate that the FAW strains resistant to Cry1Fa, Vip3Aa or Cry1A.105/Cry2Ab demonstrate no cross-resistance to eCry1Gb.1Ig and strongly suggest that eCry1Gb.1Ig acts through a novel mode of action compared to the existing traits. In addition to its FAW activity, eCry1Gb.1Ig has also been shown to control Chrysodeixis includens (soybean looper, SBL) and Anticarsia gemmatalis (velvetbean caterpillar, VBC), which are significant pests of soybean. When eCry1Gb.1Ig was introduced into corn and soybean crops, transgenic events showed strong efficacy against FAW, SBL and VBC, but no adverse plant phenotypes. This suggests that the in planta expression of the eCry1Gb.1Ig protein does not compromise plant growth or reproduction and can protect plants from FAW-related damage. Therefore, this valuable discovery will provide a differentiating FAW control trait that will give growers another tool to help them reduce yield loss due to FAW.
Aflatoxins in Feed: Types, Metabolism, Health Consequences in Swine and Mitigation Strategies
Authors: Popescu Roua Gabriela , Rădulescu Andreea Luminița , Georgescu Sergiu Emil , Dinischiotu Anca
Published: December 2022

Feeding farm animals with aflatoxin-contaminated feed can cause various severe toxic effects, leading to increased susceptibility to infectious diseases and increased mortality, weight loss, poor performance and reduced reproductive capability. Following ingestion of contaminated foodstuffs, aflatoxins are metabolized and biotransformed differently in animals. Swine metabolism is not effective in detoxifying and excreting aflatoxins, meaning the risk of aflatoxicosis is increased. Thus, it is of great importance to elucidate the metabolism and all metabolic pathways associated with this mycotoxin. The damage induced by AFB1 in cells and tissues consists of inhibition of cell proliferation, carcinogenicity, immunosuppression, mutagenicity, oxidative stress, lipid peroxidation and DNA damage, leading to pathological lesions in the liver, spleen, lymph node, kidney, uterus, heart, and lungs of swine. At present, it is a challenging task and of serious concern to completely remove aflatoxins and their metabolites from feedstuff; thus, the aim of this study was a literature review on the deleterious effects of aflatoxins on swine metabolism, as well as alternatives that contribute to the detoxification or amelioration of aflatoxin-induced effects in farm animal feed.
Chronic Aflatoxin Exposure and Cognitive and Language Development in Young Children of Bangladesh: A Longitudinal Study
Authors: Mahfuz Mustafa , Hossain Md. Shabab , Alam Md. Ashraful , Gazi Md. Amran , Fahim Shah Mohammad , Nahar Baitun , Ahmed Tahmeed
Published: December 2022

Aflatoxin can cross the blood–brain barrier, damage brain tissues, and have the potential to harm the development of the human brain. Although dietary aflatoxin exposure is common in children, there is a paucity of data on aflatoxin exposure and child developmental outcomes. The child’s cognitive, motor, and language functions were assessed using the Bayley Scales of Infant and Toddler Development-III or BSID-III at the same time points. Association between exposure to aflatoxin and subtests of BSID-III were examined using mixed-effect linear regression. Aflatoxin assays were performed on 194, 167, and 163 children at 15, 24, and 36 months of age, and chronic aflatoxin exposure was detected in 20.6%, 16.8%, and 60.7% of children, respectively. Multi-variable analyses showed that aflatoxin exposure was independently related to the children’s cognitive score (β: −0.69; 95% CI: −1.36, −0.02), receptive language score (β: −0.90; 95% CI: −1.62, −0.17), and expressive language score (β: −1.01; 95% CI: −1.96, −0.05). We did not observe any association between exposure to aflatoxin and the motor function of children. Chronic exposure to aflatoxin exposure was linked to reduced cognitive, expressive, and receptive language scores of the study children. Further research is needed in a different setting to confirm this novel finding.
Glycyrrhizic Acid and Compound Probiotics Supplementation Alters the Intestinal Transcriptome and Microbiome of Weaned Piglets Exposed to Deoxynivalenol
Authors: Xu Xiaoxiang , Chang Juan , Wang Ping , Liu Chaoqi , Liu Mengjie , Zhou Ting , Yin Qingqiang , Yan Guorong
Published: December 2022

Deoxynivalenol (DON) is a widespread mycotoxin that affects the intestinal health of animals and humans. In the present study, we performed RNA-sequencing and 16S rRNA sequencing in piglets after DON and glycyrrhizic acid and compound probiotics (GAP) supplementation to determine the changes in intestinal transcriptome and microbiota. Transcriptome results indicated that DON exposure altered intestinal gene expression involved in nutrient transport and metabolism. Genes related to lipid metabolism, such as PLIN1, PLIN4, ADIPOQ, and FABP4 in the intestine, were significantly decreased by DON exposure, while their expressions were significantly increased after GAP supplementation. KEGG enrichment analysis showed that GAP supplementation promoted intestinal digestion and absorption of proteins, fats, vitamins, and other nutrients. Results of gut microbiota composition showed that GAP supplementation significantly improved the diversity of gut microbiota. DON exposure significantly increased Proteobacteria, Actinobacteria, and Bacillus abundances and decreased Firmicutes, Lactobacillus, and Streptococcus abundances; however, dietary supplementation with GAP observably recovered their abundances to normal. In addition, predictive functions by PICRUSt analysis showed that DON exposure decreased lipid metabolism, whereas GAP supplementation increased immune system. This result demonstrated that dietary exposure to DON altered the intestinal gene expressions related to nutrient metabolism and induced disturbances of intestinal microbiota, while supplementing GAP to DON-contaminated diets could improve intestinal health for piglets.
Genetic Interaction of Global Regulators AflatfA and AflatfB Mediating Development, Stress Response and Aflatoxins B1 Production in Aspergillus flavus
Authors: Wang Xiuna , Zha Wenjie , Yao Bin , Yang Lan , Wang Shihua
Published: December 2022

Aspergillus flavus produces carcinogenic and mutagenic aflatoxins, which cause economic losses and risk of food safety by contaminating grains, food and feed. In this study, we characterized two bZIP transcription factors, AflatfA and AflatfB, and their genetic interaction. Compared to the wild type (WT), AflatfA deletion and AflatfA and AflatfB double deletion both caused retarded vegetative growth of mycelia. Relative to WT, the AflatfA deletion strain (ΔAflatfA) and AflatfA and AflatfB double deletion strain (ΔAflatfAΔAflatfB) produced more sclerotia, whereas the AflatfB deletion strain (ΔAflatfB) produced less sclerotia. After 4 °C preservation and incubation at 50 °C, conidia viability dramatically decreased in the ΔAflatfA and ΔAflatfAΔAflatfB but ΔAflatfB mutants, whereas conidia viability of the ΔAflatfAΔAflatfB strain was higher after storage at 4 °C than in AflatfA mutant. Conidia of ΔAflatfA, ΔAflatfB and ΔAflatfAΔAflatfB strains significantly increased in sensitivity to H2O2 in comparison with WT. Compared to WT, the mycelium of ΔAflatfA and ΔAflatfB strains were more sensitive to H2O2; conversely, the ΔAflatfAΔAflatfB strain showed less sensitivity to H2O2. ΔAflatfA and ΔAflatfAΔAflatfB strains displayed less sensitivity to the osmotic reagents NaCl, KCl and Sorbitol, in comparison with WT and ΔAflatfB strains. When on YES medium and hosts corn and peanut, ΔAflatfA and ΔAflatfAΔAflatfB strains produced less aflatoxin B1 (AFB1) than ΔAflatfB, and the AFB1 yield of ΔAflatfB was higher than that of WT. When WT and mutants were inoculated on corn and peanut, the ΔAflatfA and ΔAflatfAΔAflatfB but not ΔAflatfB mutants produced less conidia than did WT. Taken together, this study reveals that AflatfA controls more cellular processes, and the function of AflatfA is stronger than that of AflatfB when of the same process is regulated, except the response to H2O2, which might result from the effect of AflatfA on the transcriptional level of AflatfB.
Cooling Process of White Dwarf Stars in Palatini f(R) Gravity
Authors: Kalita Surajit , Sarmah Lupamudra , Wojnar Aneta
Published: December 2022

A simple cooling model of white dwarf stars is re-analyzed in Palatini f(R) gravity. Modified gravity affects the white dwarf structures and consequently their ages. We find that the resulting super-Chandrasekhar white dwarfs need more time to cool down than sub-Chandrasekhar ones, or when compared to the Newtonian models.
Enhanced Swine Wastewater Treatment by Constructed Wetland—Microbial Fuel Cell Systems
Authors: Zhang Yun , Liu Feng , Lin Yidong , Sun Lei , Guo Xinru , Yang Shuai , He Jinlong
Published: December 2022

This paper studies the effects of planting plants and coupled microbial fuel cells (MFCs) on the decontamination capacity and purification mechanism of constructed wetlands (CWs). Four systems were set, namely CW-without plants (A1), CW-with plants (A2), CW-MFC-without plants (A3) and CW-MFC-with plants (A4). The daily reductions per unit area of chemical oxygen demand (COD) were 48.72 ± 5.42, 51.26 ± 4.10, 53.49 ± 5.44 and 58.54 ± 4.16 g·(d·m2)−1, respectively. The daily reductions per unit area of nitrogen (N) were 11.89 ± 0.73, 12.38 ± 0.76, 12.24 ± 0.79 and 13.61 ± 1.07 g·(d·m2)−1, respectively. After studying the pollutant removal efficiency, it was found that the unit area of A4 removes the highest number of pollutants, improving the area efficiency of the wetland system and fundamentally alleviating the disadvantage of the large land footprint of wetland processes. The average output voltages of A3 and A4 were 568.29 and 717.46 mV, respectively, and the maximum power densities were 4.59 and 15.87 mW/m3, respectively. In addition, after high-throughput analysis of microbial samples, anaerobic ammonia oxidising (anammox) bacteria were found to remove N from the system in the anaerobic anode region.
Water Quality Indicators in Three Surface Hydraulic Connection Conditions in Tropical Floodplain Lakes
Authors: Salcedo Miguel Ángel , Cruz-Ramírez Allan Keith , Sánchez Alberto J. , Álvarez-Pliego Nicolás , Florido Rosa , Ruiz-Carrera Violeta , Morales-Cuetos Sara Susana
Published: December 2022

Water quality indicators have been tied to natural or man-made surface hydraulic connection (SHC) conditions. Among these, temporally connected lakes (TCL) are hydraulic intermediates between isolated (IL) and permanently connected lakes (PCL). Therefore, the aim of this study is to answer if water quality indicators can estimate the possible overlap between the two opposed conditions of SHC (IL and PCL) with the intermediate one (TCL) in lakes with similar modifications in the water level regulation at the basin level. Among nine water variables sampled in six lakes with the three SHC conditions mentioned, chlorophyll a (Chl-a), Secchi disk (SD), and total phosphorus (TP) were identified as quality water indicators through principal component analysis. Furthermore, said indicators were used to measure their overlap and trophic state index deviations. The Chl-a, SD, and TP values in TCL showed a 0.72 overlap of PCL and IL. TP surplus measured in all the lakes was meaningful in urbanized ILs and lessened in a rural lake (PCL6) with submerged rooted macrophytes. The estimated overlap of trophic indicators between TCL, IL, and PCL in this study must be verified at a global representative scale for predictive and preventive use in the conservation of tropical coastal plain lakes.