bluuebunny/crossref_metadata_2025 huggingface dataset:
New Cognitive Deep-Learning CAPTCHA
Authors: Trong Nghia Dinh , Huong Thien Ho , Hoang Vinh Truong
Published: February 2023

CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart), or HIP (Human Interactive Proof), has long been utilized to avoid bots manipulating web services. Over the years, various CAPTCHAs have been presented, primarily to enhance security and usability against new bots and cybercriminals carrying out destructive actions. Nevertheless, automated attacks supported by ML (Machine Learning), CNN (Convolutional Neural Network), and DNN (Deep Neural Network) have successfully broken all common conventional schemes, including text- and image-based CAPTCHAs. CNN/DNN have recently been shown to be extremely vulnerable to adversarial examples, which can consistently deceive neural networks by introducing noise that humans are incapable of detecting. In this study, the authors improve the security for CAPTCHA design by combining text-based, image-based, and cognitive CAPTCHA characteristics and applying adversarial examples and neural style transfer. Comprehend usability and security assessments are performed to evaluate the efficacy of the improvement in CAPTCHA. The results show that the proposed CAPTCHA outperforms standard CAPTCHAs in terms of security while remaining usable. Our work makes two major contributions: first, we show that the combination of deep learning and cognition can significantly improve the security of image-based and text-based CAPTCHAs; and second, we suggest a promising direction for designing CAPTCHAs with the concept of the proposed CAPTCHA.
Spectroradiometer Calibration for Radiance Transfer Measurements
Authors: Rammeloo Clemens , Baumgartner Andreas
Published: February 2023

Optical remote sensing and Earth observation instruments rely on precise radiometric calibrations which are generally provided by the broadband emission from large-aperture integrating spheres. The link between the integrating sphere radiance and an SI-traceable radiance standard is made by spectroradiometer measurements. In this work, the calibration efforts of a Spectra Vista Corporation (SVC) HR-1024i spectroradiometer are presented to study how these enable radiance transfer measurements at the Calibration Home Base (CHB) for imaging spectrometers at the Remote Sensing Technology Institute (IMF) of the German Aerospace Center (DLR). The spectral and radiometric response calibrations of an SVC HR-1024i spectroradiometer are reported, as well as the measurements of non-linearity and its sensitivity to temperature changes and polarized light. This achieves radiance transfer measurements with the calibrated spectroradiometer with relative expanded uncertainties between 1% and 3% (k=2) over the wavelength range of 380 nm to 2500 nm, which are limited by the uncertainties of the applied radiance standard.
Time Domain Transmissiometry-Based Sensor for Simultaneously Measuring Soil Water Content, Electrical Conductivity, Temperature, and Matric Potential
Authors: Kojima Yuki , Matsuoka Manabu , Ariki Tomohide , Yoshioka Tetsuo
Published: February 2023

Owing to the increasing popularity of smart agriculture in recent years, it is necessary to develop a single sensor that can measure several soil properties, particularly the soil water content and matric potential. Therefore, in this study, we developed a sensor that can simultaneously measure soil water content (θ), electrical conductivity (σb), temperature, and matric potential (ψ). The proposed sensor can determine θ and σb using time domain transmissiometry and can determine ψ based on the capacitance of the accompanying ceramic plate. A series of laboratory and field tests were conducted to evaluate the performance of the sensor. The sensor output values were correlated with the soil properties, and the temperature dependence of the sensor outputs was evaluated. Additionally, field tests were conducted to measure transient soil conditions over a long period. The results show that the developed sensor can measure each soil property with acceptable accuracy. Moreover, the root-mean-square errors of the sensor and reference values were 1.7 for the dielectric constant (which is equivalent to θ), 62 mS m−1 for σb, and 0.05–0.88 for log ψ. The temperature dependence was not a problem, except when ψ was below −100 kPa. The sensor can be used for long-term measurements in agricultural fields and exhibited sufficient lifetime and performance. We believe that the developed sensor can contribute to smart agriculture and research on heat and mass transfer in soil.
An Adaptable and Unsupervised TinyML Anomaly Detection System for Extreme Industrial Environments
Authors: Antonini Mattia , Pincheira Miguel , Vecchio Massimo , Antonelli Fabio
Published: February 2023

Industrial assets often feature multiple sensing devices to keep track of their status by monitoring certain physical parameters. These readings can be analyzed with machine learning (ML) tools to identify potential failures through anomaly detection, allowing operators to take appropriate corrective actions. Typically, these analyses are conducted on servers located in data centers or the cloud. However, this approach increases system complexity and is susceptible to failure in cases where connectivity is unavailable. Furthermore, this communication restriction limits the approach’s applicability in extreme industrial environments where operating conditions affect communication and access to the system. This paper proposes and evaluates an end-to-end adaptable and configurable anomaly detection system that uses the Internet of Things (IoT), edge computing, and Tiny-MLOps methodologies in an extreme industrial environment such as submersible pumps. The system runs on an IoT sensing Kit, based on an ESP32 microcontroller and MicroPython firmware, located near the data source. The processing pipeline on the sensing device collects data, trains an anomaly detection model, and alerts an external gateway in the event of an anomaly. The anomaly detection model uses the isolation forest algorithm, which can be trained on the microcontroller in just 1.2 to 6.4 s and detect an anomaly in less than 16 milliseconds with an ensemble of 50 trees and 80 KB of RAM. Additionally, the system employs blockchain technology to provide a transparent and irrefutable repository of anomalies.
Preclinical In Vitro Investigation of MDM2 Inhibition in Combination with Antiangiogenic Therapy for Breast Cancer Treatment
Authors: Alaseem Ali M. , Alhazzani Khalid , Alanazi Ahmed Zuwaiel , Alqarni Yasser , Algahtani Mohammad M. , Alhamed Abdullah S. , Alasiri Glowi , Alotaibi Fahad T. , Jawaid Talha , Aldali Jehad A.
Published: February 2023

Background: Combining antiangiogenic drugs with other chemotherapeutic drugs has been found to produce superior therapeutic outcomes and prevent drug resistance in a variety of cancers. Methods: Experimental assays such as the MTT assay, flow cytometry, western blotting, and qPCR have been used to evaluate the efficacy of combination therapy. Results: When compared to controls and monotherapies, the combination treatment of axitinib and idasanutlin demonstrated a substantial decrease in cell viability at lower doses, a significant decrease in migration, and a shift toward early and late apoptosis. This study examined major apoptotic, metastatic, and angiogenic factors, including MDM2, p21, BCL-2, BCL-XL, and MMP9, which have showed differential expressions at the protein and mRNA levels after combination. Axitinib and idasanutlin decreased tumorigenesis and migration in vitro in the MCF-7 cell line when compared to other chemotherapeutic medications. The suggested mechanisms of the antitumorigenic effect of the combination therapy may depend on its capacity to promote the production of apoptotic markers and reduce antiapoptotic markers. Conclusions: Treatments with axitinib and idasanutlin demonstrated effective therapeutic targeting of the primary angiogenic growth factor and, consequently, the pro-metastatic arbitrators. This will not only eliminate cancer cells but also stop other malignant processes and ultimately reduce the metastatic cascade.
Improvement of Seed Quality by Priming: Concept and Biological Basis
Authors: Corbineau Françoise , Taskiran-Özbingöl Nesrin , El-Maarouf-Bouteau Hayat
Published: February 2023

Presoaking seeds in water (hydropriming) or in a solution, usually of polyethylene glycol (PEG) or various salts at low water potential (osmopriming), has been demonstrated to improve the germination of seeds of numerous species including vegetables (carrot, celery, leek, lettuce, tomato), floral plants (cyclamen, primrose, pansy) and others (sugar beet, rape, soybean, sunflower). This treatment allows the germination stricto sensu to occur but prevents the radicle protrusion. Germination of primed seeds is more rapid and uniform than that of unprimed ones. Primed seeds germinate in a wider range of temperatures and are less sensitive to oxygen deprivation. Interestingly, priming also improves the germination of aged seeds. The stimulatory effect of priming persists after redrying and often during storage; however, primed seeds often deteriorate faster during storage or accelerated aging than unprimed ones. A better understanding of the mechanisms involved during priming allows us to suggest markers of the effectiveness of priming. Among these markers, ethylene production during imbibition, cell-cycle processes (DNA replication, ß-tubulin), soluble sugar metabolism (raffinose family oligosaccharides, in particular), reactive oxygen species scavenging through antioxidant systems and energy metabolism are correlated to seed vigor. Global approaches (proteomic, metabolomic or transcriptomic) could also result in the identification of new markers.
Development of a New Method to Estimate the Water Purification Efficiency of Bulk-Supported Nanosorbents under Realistic Conditions
Authors: Moisiadis Elias , Pournara Anastasia D. , Manos Manolis J. , Giokas Dimosthenis L.
Published: February 2023

The direct use of nanosorbents for water purification is limited due to their aggregation and the lack of techniques for their recovery from natural waters. To overcome these problems, the affixation of nanomaterials onto bulk, non-mobile supports has been proposed. However, a method to simulate the efficiency of these sorbents under realistic conditions is still not available. To address this need, this work describes a method for evaluating the sorption efficiency of nanosorbent materials incorporated on bulk supports under non-equilibrium conditions. The method combines the principles of passive sampling, an environmental monitoring technique that is based on passive diffusion of dissolved contaminants from water to a sorbent, with batch sorption experiments that measure sorption under equilibrium conditions, to determine the parameters associated with water purification. These parameters are the maximum sorption capacity of the sorbent and the sampling rate, which is the volume of contaminated water treated per unit of time. From these variables, the deployment time of the sorbent until reaching saturation is proposed as an alternative indicator of sorbent efficiency. As proof-of-principle, the removal of oxyanions from a Zr-metal-organic framework (MOR−1) immobilized on cotton textiles was investigated. The results show that the sorption capacity under passive diffusion uptake conditions, is approximately 20 mg/g for As(VI) and 36 mg/g Se(IV), which is 10 to 30 times lower compared to that determined in batch sorption studies, indicating that conventional equilibrium sorption overestimates the efficiency of the sorbents under realistic conditions. The application of the method to a worst-case scenario, involving the severe contamination of freshwaters with arsenate species, is also demonstrated.
Chemical Profiling of Significant Antioxidant and Phytotoxic Microwave-Extracted Essential Oil from Araucaria heterophylla Resin
Authors: Abd-ElGawad Ahmed , Saleh Ibrahim , El-Razek Mohamed , Elkarim Asmaa , El-Amier Yasser , Mohamed Tarik , El Gendy Abd , Afifi Sherif , Esatbeyoglu Tuba , Elshamy Abdelsamed
Published: February 2023

Due to the various hazards of using synthetic chemical compounds in pharmaceutics, agriculture, and industry, scientists and researchers do their best to explore and assess new green natural compounds from natural resources with potent activity. The essential oil (EO) from the resin collected from Araucaria heterophylla Salisb. was extracted by the microwave technique and chemically characterized via GC-MS analysis. Furthermore, the extract EO was assessed for its antioxidant and phytotoxic activities. The EO has 33 compounds, mainly terpenes (98.23%), and the major compounds were α-pinene (62.57%), β-pinene (6.60%), germacrene D (5.88%), and β-caryophyllene (3.56%). The extracted EO showed substantial antioxidant activity, where it showed IC50 values of 142.42 and 118.03 mg L−1 for DPPH and ABTS, respectively. On the other hand, the EO revealed considerable phytotoxicity against the weed Chenopodium murale, where the EO showed IC50 values of 304.0, 230.1, and 147.1 mg L−1, for seed germination, seedling shoot growth, and seedling root growth, respectively. Moreover, the EO showed the same pattern of allelopathic inhibition against the weed Sonchus oleraceus, where it showed IC50 values of 295.7, 224.5, and 106.1 mg L−1, for seed germination, seedling shoot growth, and seedling root growth, respectively. The present study showed that the extraction technique affects the constituents of the EO, particularly the quantitative composition. The EO of A. heterophylla resin also revealed considerable antioxidant and phytotoxic activity against weeds. Therefore, it can be considered a promising natural resource that could be integrated into the weed management approach. However, further study is recommended for deep characterization of their authentic compounds and evaluation of their mode of action(s) on a wide spectrum of weeds.
Contaminant Cocktails of High Concern in Honey: Challenges, QuEChERS Extraction and Levels
Authors: Lamas Mariana , Rodrigues Francisca , Amaral Maria Helena , Delerue-Matos Cristina , Fernandes Virgínia Cruz
Published: February 2023

Environmental pollution is a crucial problem in our society, having nowadays a better understanding of its consequences, which include the increase of contaminant cocktails present in the environment. The contamination of honeybees can occur through their interaction with the nearby environment. Therefore, if honeybees are previously contaminated, there is a possibility of contamination of their products, such as honey as natural, or minimally processed, product, resulting from the honeybees’ activity. Considering that honey is a highly consumed product, it is extremely necessary to control its quality and safety, including evaluating the presence and quantification of contaminants, which should follow monitoring studies and the legislation established by the European Union. This work aims to review the literature of different contaminants reported on honey, including pesticides, persistent organic pollutants, polycyclic aromatic hydrocarbons, and pharmaceuticals, focusing on the reports using the QuEChERS technique for the extraction. Furthermore, reports of microplastics on honey samples were also discussed. Despite the existence of several methods that identify and quantify these contaminants, few methods have been reported to operate with different groups of contaminants simultaneously. The development of methods with this characteristic (while being fast, low cost, and with a lower impact on the environment), monitoring studies to identify the risks, and an update on legislation are priority actions and future perspectives to follow.
Expression Profile of Selected Genes Involved in Na+ Homeostasis and In Silico miRNA Identification in Medicago sativa and Medicago arborea under Salinity Stress
Authors: Sarri Efi , Kapazoglou Aliki , Gerakari Maria , Abraham Eleni M. , Bebeli Penelope J. , Tani Eleni
Published: February 2023

The accumulation of ions due to increased salinity in the soil is one of the major abiotic stressors of cultivated plants that negatively affect their productivity. The model plant, Medicago truncatula, is the only Medicago species that has been extensively studied, whereas research into increased salinity adaptation of two important forage legumes, M. sativa and M. arborea, has been limited. In the present study, the expression of six genes, namely SOS1, SOS3, NHX2, AKT, AVP and HKT1 was monitored to investigate the manner in which sodium ions are blocked and transferred to the various plant parts. In addition, in silico miRNA analysis was performed to identify miRNAs that possibly control the expression of the genes studied. The following treatments were applied: (1) salt stress, with initial treatment of 50 mM NaCl and gradual acclimatization every 10 days, (2) salt shock, with continuous application of 100 mM NaCl concentration and (3) no application of NaCl. Results showed that M. arborea appeared to overexpress and activate all available mechanisms of resistance in conditions of increased salinity, while M. sativa acted in a more targeted way, overexpressing the HKT1 and AKT genes that contribute to the accumulation of sodium ions, particularly in the root. Regarding miRNA in silico analysis, five miRNAs with significant complementarity to putative target genes, AKT1, AVP and SOS3 were identified and served as a first step in investigating miRNA regulatory networks. Further miRNA expression studies will validate these results. Our findings contribute to the understanding of the molecular mechanisms underlying salt-responsiveness in Medicago and could be used in the future for generating salt-tolerant genotypes in crop improvement programs.
Ecological Environmental Quality in China: Spatial and Temporal Characteristics, Regional Differences, and Internal Transmission Mechanisms
Authors: Lv Jiehua , Zhou Wen
Published: February 2023

In recent years, ecological environmental problems such as the greenhouse effect, soil erosion, climate change, and biodiversity reduction have become more and more salient, and ecological environmental quality has gradually become a research hotspot. This paper constructs an index system for evaluating ecological environment quality based on the pressure–state–response (PSR) model, which contains three elemental layers, natural resources, ecological environment, and government inputs, measures the ecological environment quality index by using the “vertical and horizontal layer by layer” scatter degree method, and discusses the spatial and temporal evolution trends of ecological environment quality in each province and six regions in China during 2005–2020. This paper further measures the regional ecological environment quality differences by using the Thiel index and analyzes the transmission mechanism within the pressure–state–response model by using the mediation models. The results show that the ecological environment quality of all Chinese provinces and six regions has improved significantly during the period under study, the response system and state system scores have improved significantly, the unbalanced development of ecological environment quality within north China has improved the most, and there are significant direct and mediation effects among the subsystems within the ecological environment quality with high system transmission efficiency. Therefore, the government should improve the quality of the ecological environment by seeking cross-provincial linkage development, improving the level of pollution control, and formulating relevant standards and laws and regulations.
Savior or Distraction for Survival: Examining the Applicability of Machine Learning for Rural Family Farms in the United Arab Emirates
Authors: Gilani Sayed Abdul Majid , Copiaco Abigail , Gernal Liza , Yasin Naveed , Nair Gayatri , Anwar Imran
Published: February 2023

Machine learning (ML) has seen a substantial increase in its role in improving operations for staff and customers in different industries. However, there appears to be a somewhat limited adoption of ML by farm businesses, highlighted by a review of the literature investigating innovative behaviors by rural businesses. A review of the literature identified a dearth of studies investigating ML adoption by farm businesses in rural regions of the United Arab Emirates (UAE), especially in the context of family-owned farms. Therefore, this paper aims to investigate the drivers and barriers to ML adoption by family/non-family-owned farms in rural UAE. The key research questions are (1) what are the drivers and barriers for rural UAE farms adopting ML? As well as (2) is there a difference in the drivers and barriers between family and non-family-owned farms? Twenty semi-structured interviews were conducted with farm businesses across several rural regions in the UAE. Then, through a Template Analysis (TA), drivers and barriers for rural UAE-based farm owners adopting ML were identified. Interview findings highlighted that farms could benefit from adopting ML in daily operations to save costs and improve efficiency. However, 16 of 20 farms were unaware of the benefits related to ML due to access issues (highlighted by 12 farms) in incorporating ML operations, where they felt that incorporating ML into their operations was costly (identified by 8 farms). It was also identified that non-family-owned farms were more likely to take up ML, which was attributed to local culture influencing family farms (11 farms identified culture as a barrier). This study makes a theoretical contribution by proposing the Machine Learning Adoption Framework (MLAF). In terms of practical implications, this study proposes an ML program specifically targeting the needs of farm owners in rural UAE. Policy-based implications are addressed by the findings aligning with the United Nations’ Sustainability Development Goals 9 (Industry, Innovation, and Infrastructure) and 11 (Sustainable Cities and Communities).
Comprehensive Correlation for the Prediction of the Heat Release Characteristics of Diesel/CNG Mixtures in a Single-Zone Combustion Model
Authors: Lebedevas Sergejus , Raslavičius Laurencas , Drazdauskas Martynas
Published: February 2023

Fuel combinations with substantial differences in reactivity, such as diesel/CNG, represent one of the most promising alternative combustion strategies these days. In general, the conversion from diesel to dual-fuel operation can be performed in existing in-use heavy-duty compression-ignition engines with minimum modifications, which guarantee very little particles, less nitrogen oxide (NOx), and reduced noise by half compared to diesel. These factors make it feasible to retrofit a CNG fuel system on an existing diesel engine to operate it in dual fuel mode. However, the single-zone combustion models using the traditional single-Wiebe function are exceptionally adopted to assess the dedicated dual fuel engines, whereas the heat loss to the walls is estimated by using the Woschni heat loss formulation. It means that the fast and preliminary analysis of the unmodified engine performance by 1-zone models becomes complicated due to the obvious deterioration of the energy parameters, which, in turn, was predetermined from the deviation in the thermodynamic cycle variables as the calculation outcome. In this study, the main novelty lies in the fact that we propose a novel composition-considered Woschni correlation for the prediction of the heat release duration characteristics of diesel/CNG mixtures for the unmodified diesel engine. The elimination of former deficiencies distinctive to a single-zone thermodynamic model by applying the interim steps described became the core of the research presented in this paper. It led to successful derivation of the necessary correlation for modelling the heat release duration characteristics of an ICE operated in the dual fuel mode.
Study on Calcium Carbonate Deposition of Microorganism Bottom Grouting to Repair Concrete Cracks
Authors: Jia Qiang , Wang Yucheng
Published: February 2023

Microbially induced calcium carbonate deposition (MICP) restoration technology has good restoration effects and low environmental pollution compared with traditional restoration methods, and this technology has been applied in actual projects. However, grouting components such as bacterial solutions are easily lost in vertical cracks due to their low viscosity as well as their self-weight. In this paper, cracks were repaired using microbial mineralization by sealing the bottom of vertical cracks and pressure grouting. Controlled tests were designed based on three variables: crack width, presence or absence of media in the crack, and grouting method. The results show that the smaller the crack width, the easier it is to achieve a seal. The addition of media in the cracks can improve the repair efficiency. As the amount of grouting increases, the amount of calcium carbonate production in the cracks gradually increases, and the height and thickness of calcium carbonate deposition also gradually increases. The bottom closure pressure grouting method can not only reduce the amount of grouting fluid, but also shorten the test period. This method overcomes the difficulty that grouting poses as it should not be left in vertical cracks and plays an important role in reducing costs and shortening repair time.
The Corporate Social Responsibility and Its Impact on Financial Performance: A Case of Developing Countries
Authors: Ma Chao , Chishti Mazhar Farid , Durrani Muhammad Kashif , Bashir Rizwana , Safdar Sofia , Hussain Rana Tanveer
Published: February 2023

In developing nations, environmental policies have not given nearly enough consideration to the role that environmentally friendly innovation plays. Green innovation and long-term financial performance are extremely dependent on one another. Despite the fact that numerous studies have investigated the impact that a variety of corporate social responsibility (CSR) activities have had on environmental sustainability, relatively few have investigated the implications of green innovation strategies and sustainability. From the mid-2021 through to mid-2022, information was gathered from 184 businesses listed on the Pakistan Stock Exchange (PSX) across 12 different industries. Estimates of the results were obtained by the use of structural equation modeling using partial least squares (PLS-SEM). The outcomes of the study indicated that all parts of CSR were positively significant in the process of fostering environmentally sustainable growth, with the exception of one aspect of CSR that was directed toward customers. Additionally, sustainable development contributes to the mediation impact that green innovation has, making this effect even more powerful. The data show that CSR activities have an exceptional impact on financial performance (FP) in all aspects other than one, and that green innovation (GI) also has a high-quality impact on FP, which demonstrates the significance of CSR practices in enhancing sustainable environment.
Energy Efficiency Management in Small and Medium-Sized Enterprises: Current Situation, Case Studies and Best Practices
Authors: Gennitsaris Stavros , Oliveira Miguel Castro , Vris George , Bofilios Antonis , Ntinou Theodora , Frutuoso Ana Rita , Queiroga Catarina , Giannatsis John , Sofianopoulou Stella , Dedoussis Vassilis
Published: February 2023

Energy efficiency is a key factor to meet the ambitious climate targets of the European Union (EU) aligned with the international policy directives. On their own, Small and Medium-sized Enterprises (SMEs) do not consume big amounts of energy, but taking into consideration that they represent about 99% of businesses worldwide, their cumulative energy consumption is remarkable. Even though SMEs experience several barriers in their effort to improve their energy efficiency, their contribution to the EU’s energy efficiency improvement targets is crucial through the implementation of measures to improve their energy footprint. The purpose of this paper is to present a comprehensive review of SMEs’ energy efficiency and energy footprint management, which has been carried out in the context of the “SMEnergy—Energy Footprint Management for SMEs” EU-funded Erasmus+ project. The correlation between energy conversion and consumption processes and energy efficiency, as well as the current situation of energy footprint management and energy management systems in SMEs, are discussed in detail. Moreover, successful case studies of SMEs that have implemented specific measures to improve their energy footprint and achieve energy efficiency targets are also included. It is highlighted that SMEs exhibit a high potential for energy efficiency enhancement that could directly promote climate change mitigation and sustainable development.
Algae-Powered Buildings: A Review of an Innovative, Sustainable Approach in the Built Environment
Authors: Sedighi Mahsa , Pourmoghaddam Qhazvini Peiman , Amidpour Majid
Published: February 2023

Environmental pollution, global warming, energy consumption, and limited natural resources are some key factors from which today’s built environment faces interrelated problems and their management plays a vital role in sustainability. The building sector is involved in 35% of global energy usage and 40% of energy related CO2 emissions. Application of bioactive elements on buildings’ façades is a novel approach for solving the above-mentioned problems. Management of some important factors such as thermal comfort, energy efficiency, wastewater treatment, and CO2 capture is positively affected by bioactive façades because of their environmentally friendly nature. They also have positive effects on global warming, pollution control, social wealth, and sustainable development on a larger scale. The buildings integrated with photobioreactors (PBRs) can meet their thermal needs due to thermal insulation, shading, solar collection, and light-to-biomass conversion. Energy savings up to 30% are estimated to be met by PBR-integrated buildings due to reduced heating, cooling, ventilation, and lighting loads. The above amount of energy saving results in less CO2 emission. Moreover, the algae-integrated buildings can sequester CO2 with an average sequestration rate of 5 g/ft2/day when optimum growing environments and operation modes are implemented. This study is an overview of microalgae intervention and PBR-adapted buildings as an innovative approach for energy efficiency in the built environment with regard to implemented or speculative cases, pros and cons, challenges, and prospects.
Maximizing Bio-Hydrogen Production from an Innovative Microbial Electrolysis Cell Using Artificial Intelligence
Authors: Fathy Ahmed , Rezk Hegazy , Yousri Dalia , Alharbi Abdullah G. , Alshammari Sulaiman , Hassan Yahia B.
Published: February 2023

In this research work, the best operating conditions of microbial electrolysis cells (MECs) were identified using artificial intelligence and modern optimization. MECs are innovative materials that can be used for simultaneous wastewater treatment and bio-hydrogen production. The main objective is the maximization of bio-hydrogen production during the wastewater treatment process by MECs. The suggested strategy contains two main stages: modelling and optimal parameter identification. Firstly, using adaptive neuro-Fuzzy inference system (ANFIS) modelling, an accurate model of the MES was created. Secondly, the optimal parameters of the operating conditions were determined using the jellyfish optimizer (JO). Three operating variables were studied: incubation temperature (°C), initial potential of hydrogen (pH), and influent chemical oxygen demand (COD) concentration (%). Using some measured data points, the ANFIS model was built for simulating the output of MFC considering the operating parameters. Afterward, a jellyfish optimizer was applied to determine the optimal temperature, initial pH, and influent COD concentration values. To demonstrate the accuracy of the proposed strategy, a comparison with previous approaches was conducted. For the modelling stage, compared with the response surface methodology (RSM), the coefficient of determination increased from 0.8953 using RSM to 0.963 using ANFIS, by around 7.56%. In addition, the RMSE decreased from 0.1924 (using RSM) to 0.0302 using ANFIS, whereas for the optimal parameter identification stage, the optimal values were 30.2 °C, 6.53, and 59.98 (%), respectively, for the incubation temperature, the initial potential of hydrogen (pH), and the influent COD concentration. Under this condition, the maximum rate of the hydrogen production is 1.252 m3H2/m3d. Therefore, the proposed strategy successfully increased the hydrogen production from 1.1747 m3H2/m3d to 1.253 m3H2/m3d by around 6.7% compared to RSM.
Lightweight Network-Based Surface Defect Detection Method for Steel Plates
Authors: Wang Changqing , Sun Maoxuan , Cao Yuan , He Kunyu , Zhang Bei , Cao Zhonghao , Wang Meng
Published: February 2023

This article proposes a lightweight YOLO-ACG detection algorithm that balances accuracy and speed, which improves on the classification errors and missed detections present in existing steel plate defect detection algorithms. To highlight the key elements of the desired area of surface flaws in steel plates, a void space convolutional pyramid pooling model is applied to the backbone network. This model improves the fusion of high- and low-level semantic information by designing feature pyramid networks with embedded spatial attention. According to the experimental findings, the suggested detection algorithm enhances the mapped value by about 4% once compared to the YOLOv4-Ghost detection algorithm on the homemade data set. Additionally, the real-time detection speed reaches about 103FPS, which is about 7FPS faster than the YOLOv4-Ghost detection algorithm, and the detection capability of steel surface defects is significantly enhanced to meet the needs of real-time detection of realistic scenes in the mobile terminal.
Liner-Shipping Network Design with Emission Control Areas: A Real Case Study
Authors: Lan Xiangang , Tao Qin , Wu Xincheng
Published: February 2023

In recent years, liner-shipping companies have faced a traditional trade-off between cost and emission (CO2 and SOX) reduction. This study considers this element to construct a liner-shipping network design model which includes a package-cargo transport plan, route allocation, and route design. The objective is to maximize profit by selecting the ports to be visited, the sequence of port visits, the cargo flows between ports, and the number/operating speeds of vessels. In addition, emission control areas (ECAs) exist in the liner network. With reference to the idea of the column generation algorithm, this study proposed a heuristic algorithm based on empirical data through a real case calculation and selected the optimal scheme, which is in-line with both economic and environmental benefits. The results show that the model and optimization method are feasible and provide an effective solution for the liner network design of shipping companies, while also considering environmental factors. In addition, the effects of the number of ECAs, inter-port origin-destination (OD) demand, freight rate, fuel price, and carbon prices on the design of transport networks are discussed to provide a reference for the operation of shipping companies and government decision-making.
Study on the Factors Affecting the Green Housing Purchase Intention in Urban Residents—Taking the Beijing-Tianjin-Hebei Region as an Example
Authors: Ren Wei , Wang Yaxiao
Published: February 2023

Green housing has the characteristics of energy saving, environmental protection and comfort. Promoting the development of green housing market is of great significance to accelerate the green transformation of the construction industry and help the construction sector achieve the emission reduction target. This paper takes urban residents in the Beijing-Tianjin-Hebei region as the research object, and based on the theory of planned behavior, identifies five influencing factors such as purchasing attitude, subjective norms, perceived behavior control, environmental concern and green housing product attributes, and builds a model of influencing factors of urban residents’ green housing purchase intention in the Beijing-Tianjin-Hebei region. The results show that subjective norms, perceived behavioral control, environmental concerns and green housing product attributes all have positive effects on the purchase intention, and their influence paths and effects on the purchase intention of green housing are different.
Innovative Data-Driven Energy Services and Business Models in the Domestic Building Sector
Authors: Aranda Juan , Tsitsanis Tasos , Georgopoulos Giannis , Longares Jose Manuel
Published: February 2023

The market of energy services for the residential sector in Europe is very limited at present. Various reasons can be argued such as the high transaction costs in a highly fragmented market and the low energy consumption per dwelling. The rather long payback time for investments render Energy Services Companies’ (ESCOs) services financially unattractive for many ESCOs and building residents, thus hindering a large potential of energy savings in a sector that is responsible of almost half of Europe’s energy consumption. If the ambitious 2030 and 2050’s decarbonisation targets are to be met, the EU’s residential sector must be part of the solution. This paper offers insights about novel ESCO business models based on intensive data-driven Artificial Intelligence algorithms and analytics that enable the deployment of smart energy services in the domestic sector under a Pay-for-Performance (P4P) approach. The combination of different sources of energy efficiency services and the optimal participation of domestic consumers in aggregated demand response (DR) schemes open the door to new revenue streams for energy service providers and building residents and reduce the hitherto long payback periods of ESCOs services in the sector. Innovative business models for ESCOs and demand flexibility Aggregators are thoroughly described. Especially customised Performance Measurement and Verification protocols enable fair and transparent P4P ESCO contracts. The new human-centric energy and non-energy services increase the energy consumption awareness of building users and deploy behavioural and automated responses to both environmental and market signals to maximise the economic benefit for both energy service providers and consumers, always respecting data protection rules and the consumers’ comfort preferences. The new hybrid business models of P4P energy services make traditional EPC more attractive to energy service providers, with low cost data collection and treatment systems to bring payback periods below 10 years in the residential building sector.
Evaluating the Potential of Renewable Energy Sources in a Full-Scale Upflow Anaerobic Sludge Blanket Reactor Treating Municipal Wastewater in Ghana
Authors: Arthur Philomina Mamley Adantey , Konaté Yacouba , Sawadogo Boukary , Sagoe Gideon , Dwumfour-Asare Bismark , Ahmed Issahaku , Bayitse Richard , Ampomah-Benefo Kofi
Published: February 2023

Wastewater management remains a major challenge in developing countries due to the lack of adequate infrastructure, making the need for economically viable and efficient technologies that can be sustained by emerging economies imperative. The upflow anaerobic sludge blanket (UASB) reactor represents an efficient and low-cost technology that produces by-products from which valuable resources can be recovered. This study assessed the energy recovery potential in the form of electricity from biogas and sludge by-products produced by a full-scale UASB reactor. Biogas production rate and composition were monitored to determine the biogas energy recovery potential. Dehydrated sludge from sludge drying beds was likewise quantified and characterised for its elemental composition, immediate composition, gross calorific value and net calorific value to estimate sludge energy recovery potential. The average daily biogas production was found to be 611 ± 275 Nm3/d, with 65% methane in the biogas output. Average sludge dry matter production was determined to be 358.24 TS kg/d. The net energy recovery potential was estimated to be 534.1 MWh/yr, 36% more than the yearly energy demand (392.7 MWh/yr) of the entire plant. Conservative energy recovery at a UASB-based municipal wastewater treatment facility could serve as a self-supply energy option to support its operations.
Gender-Ethnicity Intersectionality in Climate Change Adaptation in the Coastal Areas of Bangladesh
Authors: Assaduzzaman Mohammad , Filatova Tatiana , Lovett Jon C. , Coenen Frans. H. J. M.
Published: February 2023

Climate change effects are not uniform and have disproportionate impacts among different groups of people within communities. It is therefore important to understand the underlying issues of intersectionality for climate change adaptation and human well-being. This paper aims to measure human capabilities and freedom of choice by analyzing perceived climate change impacts and current climate change adaptation ability among ethnic and non-ethnic communities in Bangladesh. This study applies a range of participatory rural appraisal tools and key informant interviews to assess impacts of climate change when considering gender and ethnicity. Women in the coastal regions have less access to resources and services because of social capital and cultural practices and this directly or indirectly influences their adaptation to climate change. Women have limited or no participation in decision-making processes at family or community levels and this impacts their vulnerability and well-being. In consequence, women’s capabilities must be focused on moderating their vulnerability and risk, and developing effective adaptation to the adverse impacts of climate change and natural hazards.
Addressing Agency Problem in Employee Training: The Role of Goal Congruence
Authors: Madhavan Vandana , Venugopalan Murale , Gupta Bhumika , Sisodia Gyanendra Singh
Published: February 2023

Individualized learning plans and corresponding training programs are maintained and organized in most organizations. Employees may be averse to training if they do not see how it contributes to their professional advancement. This is an example of conflict between management and employee interests in a business. The misalignment between management’s offerings and employees’ desires is a significant factor contributing to such a situation. Our research focused on how companies and individuals put training resources to use from a perspective of divergent goals. It provides insights into making employee training more effective. We investigate the relationship between organizational, individual, and training efficacy using the principal–agent theory and the concept of bounded rationality. We attempted to validate three a priori conditions relating to goal congruence, training motivation, and decision-making through in-depth interviews and focus group discussions. As per participant inputs, career aspirations drive employees’ training preferences. The significance of goal congruence in achieving corporate objectives is often neglected in the academic literature. Although goal congruence can be a useful tool in assisting organizations in achieving their stated objectives, enhanced communication and discussion between managers and employees are required in order to improve and align employee goals with the company’s, for the sake of the individual’s and organization’s development. Furthermore, firms should invest in technology-enabled learning that ensures better access to learning, in order to achieve the kind of productivity and profit margins that would benefit everyone involved. We have also proposed a training value transaction model that accommodates the diverse interests. The model depicts the role of goal congruence in enhanced value fulfilment of the principals as well as agents.
Determinants of Geothermal Power Sustainability Development: Do Global Competitiveness Markets Matter?
Authors: Wang Xiaohui , Alsaleh Mohd
Published: February 2023

As a substantially capital-intensive venture, the distribution network of the geothermal business is disproportionately clustered around the project designing phase. The profound geothermal industry is broad, and consequently the geothermal economy differs substantially from one place to another. The primary goal of this study is to analyze the effect of factors relating to global competitiveness along with economic growth on the sustainability of geothermal energy among European 27 countries. Employing auto regressive distributing lag (ARDL), the major findings suggest that a significant rise in the geothermal power production sustainable development can occur in the 14 European Union emerged economies applying global competitiveness criteria than in EU13 developing economies. Among additional criteria, a conducive environment, intellectual capital, market shares, as well as an innovation ecosystem contributes more significantly to the sustainability of geothermal energy among the 14 classed as established in this research than the 13 emerging economies. The results suggest that geothermal power sustainability among the European countries regions could attain a sustainable increased level of geothermal energy generation by putting in place the necessary global competitiveness determinants for the EU 2030 Energy Union goals to be achieved. The attainment of these Energy Union goals will assist in combating climate change and fighting environmental pollution. Three estimators were adopted to confirm that all calculated projections made in the study are said to be valid. The global competitiveness measures should be made better effective by the EU nations and this will help in achieving a pollution-free society and environment. Authorities in charge of policy and law-making in the EU regions should participate more in global competitiveness for geothermal energy production to become sustainable. Cointegrated strategies that will promote sustainability should be stressed by policymakers in the EU. This will go a long way in reducing the level of carbon dioxide emissions and also in promoting sustainability in the area of geothermal power generation.
Dealing with Cross-Sectoral Uncertainty: A Case Study on Governing Uncertainty for Infrastructures in Transition
Authors: Røsok Oddrun P. , de Bruijne Mark L. C. , Veeneman Wijnand W.
Published: February 2023

The interdependencies between infrastructures are growing. Engineering decision making that earlier was largely confined to a specific sector now requires more and more understanding of how systems interact: a system-of-systems perspective. The article analyzes the effect of that added complexity in a single case study in de Zuid-As, Amsterdam, in the Netherlands, and relates the findings to the literature on engineering decision making and project management in complex projects. The article concludes that cross-sectoral engineering decision making has an additional level of complexity that requires governance of uncertainty. Despite this challenge being a well-known challenge among infrastructure operators, it is still not recognized for its importance, and it seems to be a neglected element in collaboration. Key is an open approach in the early stages that goes beyond classic cooperative decision making in engineering and project management environments.