bluuebunny/crossref_metadata_2025 huggingface dataset:
In Vitro Activity of Isavuconazole and Amphotericin B in Association against Mucorales
Authors: Ortalli Gaia , Oliva Ester , Lo Cascio Giuliana ,   on behalf of the Medical Mycology Committee (CoSM)—Italian Association of Clinical Microbiologists (AMCLI), Farina Claudio
Published: July 2023

Mucormycoses can be treated with the combination of Amphotericin B and Isavuconazole. This study evaluates the effects of these drugs in vitro against 59 strains representing 12 Mucorales. In vitro testing of the two drugs together and alone was performed using the MIC Test strip “Epsilon test synergy-method” (ETSM), which is more standard in clinical practice than microbroth dilution testing. Amphotericin B and Isavuconazole have synergistic/additive effects against L. corymbifera, R. arrhizus and M. circinelloides. Different effects have been shown for other Mucorales. ETSM can help the clinical management of mucormycosis from a practical point of view, due to its feasibility in the laboratory.
Cystatins from the Human Liver Fluke Opisthorchis viverrini: Molecular Characterization and Functional Analysis
Authors: Geadkaew-Krenc Amornrat , Grams Rudi , Siricoon Sinee , Kosa Nanthawat , Krenc Dawid , Phadungsil Wansika , Martviset Pongsakorn
Published: July 2023

A high incidence of cholangiocarcinoma (bile duct cancer) has been observed in Thailand. This usually rare cancer has been associated with infection with the human liver fluke, Opisthorchis viverrini. Secretions of the parasite that interact with the host are thought to be a major component of its pathogenicity and proteolysis is a key biological activity of the secreted molecules. In this study, we present a molecular analysis of cysteine proteinase inhibitors (cystatins) of Opisthorchis viverrini. Six cDNA coding sequences of Opisthorchis viverrini cystatins, OvCys1–6, were cloned from the adult stage of the parasite using RT-PCR. Based on their sequences, OvCys1 and OvCys2 are classified as type 1 cystatins, while OvCys3–6 are classified as type 2 cystatins, with each containing a signal peptide and only one C-terminal disulfide bond. Their C-terminal region sequences are diverse compared with other cystatin members. Cystatins OvCys1, 3 and 4 were found in crude worm extracts and excretory-secretory (ES) products from the adult parasite using Western blot detection, while the other isoforms were not. Thus, OvCys1, 3 and 4 were selected for inhibition analysis and immune reactivity with Opisthorchis viverrini-infected hamster sera. OvCys1, 3, and 4 inhibited mammalian cathepsin L more effectively than cathepsin B. The pH range for their full activity was very wide (pH 3–9) and they were heat stable for at least 3 h. Unlike Fasciola gigantica cystatins, they showed no immune reactivity with infected hamster sera based on indirect ELISA. Our findings suggest that Opisthorchis viverrini cystatins are not major antigenic components in the ES product of this parasite and that other effects of Opisthorchis viverrini cystatins should be investigated.
Novel Molecular Consortia of Cannabidiol with Nonsteroidal Anti-Inflammatory Drugs Inhibit Emerging Coronaviruses’ Entry
Authors: Pawełczyk Anna , Nowak Rafał , Gazecka Monika , Jelińska Anna , Zaprutko Lucjusz , Zmora Paweł
Published: July 2023

The COVID-19 pandemic provoked a global health crisis and highlighted the need for new therapeutic strategies. In this study, we explore the potential of the molecular consortia of cannabidiol (CBD) and non-steroidal anti-inflammatory drugs (NSAIDs) as novel antiviral dual-target agents against SARS-CoV-2/COVID-19. CBD is a natural compound with a wide range of therapeutic activities, including antiviral and anti-inflammatory properties, while NSAIDs are commonly used to mitigate the symptoms of viral infections. Chemical modifications of CBD with NSAIDs were performed to obtain dual-target agents with enhanced activity against SARS-CoV-2. The synthesised compounds were characterised using spectroscopic techniques. The biological activity of three molecular consortia (CBD–ibuprofen, CBD–ketoprofen, and CBD–naproxen) was evaluated in cell lines transduced with vesicular stomatitis virus-based pseudotypes bearing the SARS-CoV-1 or SARS-CoV-2 spike proteins or infected with influenza virus A/Puerto Rico/8/34. The results showed that some CBD–NSAID molecular consortia have superior antiviral activity against SARS-CoV-1 and SARS-CoV-2, but not against the influenza A virus. This may suggest a potential therapeutic role for these compounds in the treatment of emerging coronavirus infections. Further studies are needed to investigate the efficacy of these compounds in vivo, and their potential use in clinical settings. Our findings provide a promising new approach to combatting current and future viral emergencies.
The Vital Role Played by Deferiprone in the Transition of Thalassaemia from a Fatal to a Chronic Disease and Challenges in Its Repurposing for Use in Non-Iron-Loaded Diseases
Authors: Kontoghiorghes George J.
Published: July 2023

The iron chelating orphan drug deferiprone (L1), discovered over 40 years ago, has been used daily by patients across the world at high doses (75–100 mg/kg) for more than 30 years with no serious toxicity. The level of safety and the simple, inexpensive synthesis are some of the many unique properties of L1, which played a major role in the contribution of the drug in the transition of thalassaemia from a fatal to a chronic disease. Other unique and valuable clinical properties of L1 in relation to pharmacology and metabolism include: oral effectiveness, which improved compliance compared to the prototype therapy with subcutaneous deferoxamine; highly effective iron removal from all iron-loaded organs, particularly the heart, which is the major target organ of iron toxicity and the cause of mortality in thalassaemic patients; an ability to achieve negative iron balance, completely remove all excess iron, and maintain normal iron stores in thalassaemic patients; rapid absorption from the stomach and rapid clearance from the body, allowing a greater frequency of repeated administration and overall increased efficacy of iron excretion, which is dependent on the dose used and also the concentration achieved at the site of drug action; and its ability to cross the blood–brain barrier and treat malignant, neurological, and microbial diseases affecting the brain. Some differential pharmacological activity by L1 among patients has been generally shown in relation to the absorption, distribution, metabolism, elimination, and toxicity (ADMET) of the drug. Unique properties exhibited by L1 in comparison to other drugs include specific protein interactions and antioxidant effects, such as iron removal from transferrin and lactoferrin; inhibition of iron and copper catalytic production of free radicals, ferroptosis, and cuproptosis; and inhibition of iron-containing proteins associated with different pathological conditions. The unique properties of L1 have attracted the interest of many investigators for drug repurposing and use in many pathological conditions, including cancer, neurodegenerative conditions, microbial conditions, renal conditions, free radical pathology, metal intoxication in relation to Fe, Cu, Al, Zn, Ga, In, U, and Pu, and other diseases. Similarly, the properties of L1 increase the prospects of its wider use in optimizing therapeutic efforts in many other fields of medicine, including synergies with other drugs.
The Effect of Electrolyzed Hydrogen-Rich Alkaline Reduced Water on Patients with Chronic Constipation—A Clinical Trial
Authors: Sharma Subham , Kim Yundeok , Bajgai Johny , Rahman Md. Habibur , Jeong Yun Ju , Goh Seong Hoon , Park Hong Jun , Kim Cheol-Su , Kim Hyun Il , Lee Kyu-Jae
Published: July 2023

Chronic constipation is a common symptom-based disorder that affects patient quality of life. Electrolyzed hydrogen-rich alkaline reduced water (EHARW) helps treat gastrointestinal disorders owing to its various bioactive properties. This single-arm, open-labelled study aimed to investigate the improvement of EHARW (pH 9.5; H2 ≈ 0.5 mg/L) in chronic constipation patients. Thirty patients with chronic constipation were enrolled after screening as intention-to-treat (ITT). During the intervention period, two patients dropped out, and 28 patients completed the study as per protocol (PP). The selected patients were instructed to drink EHARW (pH 9.5; H2 ≈ 0.5 mg/L) (20 mL/kg body weight/day) generated from a home medical device for four weeks. Complete spontaneous bowel movement (CSBM) frequency was measured as the primary outcome, and Bristol stool form, patient assessment of constipation–symptoms (PAC-SYM) score, and patient assessment of constipation–quality of life (PAC-QOL) score were measured as the secondary outcomes after the 4-week intervention compared to baseline. As a result of EHARW treatment, no adverse events were observed during the study period. Moreover, the frequency of CSBM/week (29.8%, p < 0.05) and Bristol stool form score (24.6%, p < 0.01) significantly increased compared to baseline. Finally, the overall and subscale scores of the PAC-SYM (58.0%) and PAC-QOL (54.2%) questionnaires significantly decreased (p < 0.001). These results suggest that daily ingestion of EHARW (pH 9.5; H2 ≈ 0.5 mg/L) can improve CSBM frequency as a primary outcome in chronic constipation patients. Likewise, EHARW (pH 9.5; H2 ≈ 0.5 mg/L) improved Bristol stool form score, symptoms and the quality of life as a secondary outcome in patients with chronic constipation through a home-based intervention.
Transient Simulation and Optimization for the Integral Hoisting of Extra-Large Air Separation Cold Box
Authors: Tong Zheming , He Sheng , Tong Shuiguang , Han Yisong , Peng Xudong
Published: July 2023

The cold box is a crucial component for cryogenic distillation in air separation units. With the increasing focus on energy conservation and emissions reduction, the integral hoisting of the cold box has emerged as a viable alternative to traditional cold box installation due to its highly efficient performance, short cycle time, and superior integration capabilities. Nonetheless, there are concerns surrounding the large size and weight of these boxes, as well as their eccentric structure, which can cause significant challenges during the integral hoisting process and pose safety hazards. To address these issues, this paper proposes a method for optimizing the lifting point of an extra-large cold box through dynamic simulation under actual working conditions. Firstly, a transient structure FEM simulation was carried out using multi-type mesh coupling based on the operating conditions of an extra-large cold box. Secondly, the posture and strength of the box during the hoisting process were analyzed to determine the most dangerous working conditions. Finally, the maximum equivalent stress of the trusses was employed as the fitness function of the particle swarm algorithm to optimize the lifting point position in the whole parameter range. The findings indicated that the most dangerous situation during the hoisting process occurred near the 0° working condition in the flip-up process and that optimizing the lifting point position based on this working condition significantly reduced the stress levels on the trusses.
Current Sensor Integration Issues with Wide-Bandgap Power Converters
Authors: Parsa Sirat Ali , Parkhideh Babak
Published: July 2023

Precise current sensing is essential for several power electronics’ protection, control, and reliability mechanisms. Even so, WBG power converters will likely struggle to develop a single current-sensing scheme to measure various types of currents due to the limited space and size of these devices, the required high sensing speed, and the high electromagnetic interference (EMI) emissions they cause. Analysis of existing current sensors was conducted in such terms with the objective of understanding the challenges associated with their integration into WBG power converters. Since each of these requirements has different design tradeoffs, it is challenging to consider one specific method of current sensing to be perfect for all situations; thus, the possibility of developing novel methods to improve the performance of these single-scheme current sensors is further explored.
Optimizing Appearance-Based Localization with Catadioptric Cameras: Small-Footprint Models for Real-Time Inference on Edge Devices
Authors: Rostkowska Marta , Skrzypczyński Piotr
Published: July 2023

This paper considers the task of appearance-based localization: visual place recognition from omnidirectional images obtained from catadioptric cameras. The focus is on designing an efficient neural network architecture that accurately and reliably recognizes indoor scenes on distorted images from a catadioptric camera, even in self-similar environments with few discernible features. As the target application is the global localization of a low-cost service mobile robot, the proposed solutions are optimized toward being small-footprint models that provide real-time inference on edge devices, such as Nvidia Jetson. We compare several design choices for the neural network-based architecture of the localization system and then demonstrate that the best results are achieved with embeddings (global descriptors) yielded by exploiting transfer learning and fine tuning on a limited number of catadioptric images. We test our solutions on two small-scale datasets collected using different catadioptric cameras in the same office building. Next, we compare the performance of our system to state-of-the-art visual place recognition systems on the publicly available COLD Freiburg and Saarbrücken datasets that contain images collected under different lighting conditions. Our system compares favourably to the competitors both in terms of the accuracy of place recognition and the inference time, providing a cost- and energy-efficient means of appearance-based localization for an indoor service robot.
RUDE-AL: Roped UGV Deployment Algorithm of an MCDPR for Sinkhole Exploration
Authors: Orbea David , Cruz Ulloa Christyan , Del Cerro Jaime , Barrientos Antonio
Published: July 2023

The presence of sinkholes has been widely studied due to their potential risk to infrastructure and to the lives of inhabitants and rescuers in urban disaster areas, which is generally addressed in geotechnics and geophysics. In recent years, robotics has gained importance for the inspection and assessment of areas of potential risk for sinkhole formation, as well as for environmental exploration and post-disaster assistance. From the mobile robotics approach, this paper proposes RUDE-AL (Roped UGV DEployment ALgorithm), a methodology for deploying a Mobile Cable-Driven Parallel Robot (MCDPR) composed of four mobile robots and a cable-driven parallel robot (CDPR) for sinkhole exploration tasks and assistance to potential trapped victims. The deployment of the fleet is organized with node-edge formation during the mission’s first stage, positioning itself around the area of interest and acting as anchors for the subsequent release of the cable robot. One of the relevant issues considered in this work is the selection of target points for mobile robots (anchors) considering the constraints of a roped fleet, avoiding the collision of the cables with positive obstacles through a fitting function that maximizes the area covered of the zone to explore and minimizes the cost of the route distance performed by the fleet using genetic algorithms, generating feasible target routes for each mobile robot with a configurable balance between the parameters of the fitness function. The main results show a robust method whose adjustment function is affected by the number of positive obstacles near the area of interest and the shape characteristics of the sinkhole.
A Lightweight Detection Method for Remote Sensing Images and Its Energy-Efficient Accelerator on Edge Devices
Authors: Yang Ruiheng , Chen Zhikun , Wang Bin’an , Guo Yunfei , Hu Lingtong
Published: July 2023

Convolutional neural networks (CNNs) have been extensively employed in remote sensing image detection and have exhibited impressive performance over the past few years. However, the abovementioned networks are generally limited by their complex structures, which make them difficult to deploy with power-sensitive and resource-constrained remote sensing edge devices. To tackle this problem, this study proposes a lightweight remote sensing detection network suitable for edge devices and an energy-efficient CNN accelerator based on field-programmable gate arrays (FPGAs). First, a series of network weight reduction and optimization methods are proposed to reduce the size of the network and the difficulty of hardware deployment. Second, a high-energy-efficiency CNN accelerator is developed. The accelerator employs a reconfigurable and efficient convolutional processing engine to perform CNN computations, and hardware optimization was performed for the proposed network structure. The experimental results obtained with the Xilinx ZYNQ Z7020 show that the network achieved higher accuracy with a smaller size, and the CNN accelerator for the proposed network exhibited a throughput of 29.53 GOPS and power consumption of only 2.98 W while consuming only 113 DSPs. In comparison with relevant work, DSP efficiency at an identical level of energy consumption was increased by 1.1–2.5 times, confirming the superiority of the proposed solution and its potential for deployment with remote sensing edge devices.
Detection and Classification of Uniform and Concentrated Wall-Thinning Defects Using High-Order Circumferential Guided Waves and Artificial Neural Networks
Authors: Cirtautas Donatas , Samaitis Vykintas , Mažeika Liudas , Raišutis Renaldas
Published: July 2023

Pipeline structures are susceptible to corrosion, leading to significant safety, environmental, and economic implications. Existing long range guided wave inspection systems often fail to detect footprints of the concentrated defects, which can lead to leakage. One way to tackle this issue is the utilization of circumferential guided waves that inspect the pipe’s cross section. However, achieving the necessary detection resolution typically necessitates the use of high-order modes hindering the inspection data interpretation. This study presents the implementation of an ultrasonic technique capable of detecting and classifying wall thinning and concentrated defects using high-order guided wave modes. The technique is based on a proposed phase velocity mapping approach, which generates a set of isolated wave modes within a specified phase velocity range. By referencing phase velocity maps obtained from defect-free stages of the pipe, it becomes possible to observe changes resulting from the presence of defects and assign those changes to the specific type of damage using artificial neural networks (ANN). The paper outlines the fundamental principles of the proposed phase velocity mapping technique and the ANN models employed for classification tasks that use synthetic data as an input. The presented results are meticulously verified using samples with artificial defects and appropriate numerical models. Through numerical modeling, experimental verification, and analysis using ANN, the proposed method demonstrates promising outcomes in defect detection and classification, providing a more comprehensive assessment of wall thinning and concentrated defects. The model achieved an average prediction accuracy of 92% for localized defects, 99% for defect-free cases, and 98% for uniform defects.
Fault Diagnosis of Rolling Bearing Based on HPSO Algorithm Optimized CNN-LSTM Neural Network
Authors: Tian He , Fan Huaicong , Feng Mingwen , Cao Ranran , Li Dong
Published: July 2023

The quality of rolling bearings is vital for the working state and rotation accuracy of the shaft. Timely and accurately acquiring bearing status and early fault diagnosis can effectively prevent losses, making it highly practical. To improve the accuracy of bearing fault diagnosis, this paper proposes a CNN-LSTM bearing fault diagnosis model optimized by hybrid particle swarm optimization (HPSO). The HPSO algorithm has a strong global optimization ability and can effectively solve nonlinear and multivariate optimization problems. It is used to optimize and match the parameters of the CNN-LSTM model and dynamically find the optimal value of the parameters. This model overcomes the problem that the parameters of the CNN-LSTM model depend on empirical settings and cannot be adjusted dynamically. This model is used for bearing fault diagnosis, and the accuracy rate of fault diagnosis classification reaches 99.2%. Compared with the traditional CNN, LSTM, and CNN-LSTM models, the accuracy rates are increased by 6.6%, 9.2%, and 5%, respectively. At the same time, comparing the models with different optimization parameters shows that the model proposed in this paper has the highest accuracy. The experimental results verified the superiority of the HPSO algorithm to optimize model parameters and the feasibility and accuracy of the HPSO-CNN-LSTM model for bearing fault diagnosis.
Evaluation of Open-Source Tools for Differential Privacy
Authors: Zhang Shiliang , Hagermalm Anton , Slavnic Sanjin , Schiller Elad Michael , Almgren Magnus
Published: July 2023

Differential privacy (DP) defines privacy protection by promising quantified indistinguishability between individuals who consent to share their privacy-sensitive information and those who do not. DP aims to deliver this promise by including well-crafted elements of random noise in the published data, and thus there is an inherent tradeoff between the degree of privacy protection and the ability to utilize the protected data. Currently, several open-source tools have been proposed for DP provision. To the best of our knowledge, there is no comprehensive study for comparing these open-source tools with respect to their ability to balance DP’s inherent tradeoff as well as the use of system resources. This work proposes an open-source evaluation framework for privacy protection solutions and offers evaluation for OpenDP Smartnoise, Google DP, PyTorch Opacus, Tensorflow Privacy, and Diffprivlib. In addition to studying their ability to balance the above tradeoff, we consider discrete and continuous attributes by quantifying their performance under different data sizes. Our results reveal several patterns that developers should have in mind when selecting tools under different application needs and criteria. This evaluation survey can be the basis for an improved selection of open-source DP tools and quicker adaptation of DP.
Knowledge Transfer and Innovation: Universities as Catalysts for Sustainable Decision Making in Industry
Authors: Foti Lianne , Warwick Lance , Lyons Eric , Dhaliwal Sonia , Alcorn Michael
Published: July 2023

The urgency of addressing the climate crisis has heightened the need to make sustainable decisions. Universities and research institutions are uniquely able to help develop and disseminate useful information for industry, specifically small and medium enterprises. This case study examines the collaboration between the Guelph Turfgrass Institute (GTI) and the turfgrass industry that culminated in the creation of a “Turf Net Present Value Model”. This model enables turfgrass installers, managers, and local municipalities to determine the structure of their turfgrass systems, natural or artificial, and estimate the net present value (NPV) over an extended period. A conceptual model for effective industry-university collaboration is then used to evaluate the knowledge transfer between academia and industry, showcasing a knowledge-transfer framework for improved decision-making and sustainability in sports facilities.
Developing an Integration of Smart-Inverter-Based Hosting-Capacity Enhancement in Dynamic Expansion Planning of PV-Penetrated LV Distribution Networks
Authors: Hamedi Masoud , Shayeghi Hossein , Seyedshenava Seyedjalal , Safari Amin , Younesi Abdollah , Bizon Nicu , Iana Vasile-Gabriel
Published: July 2023

With the penetration of distributed energy resources (DERs), new network challenges arise that limit the hosting capacity of the network, which consequently makes the current expansion-planning models inadequate. Smart inverters as a promising tool can be utilized to enhance the hosting capacity. Therefore, in response to technical, economic, and environmental challenges, as well as government support for renewable resources, especially domestic solar resources located at the point of consumption, this paper is an endeavor to propose a smart-inverter-based low-voltage (LV) distribution expansion-planning model. The proposed model is capable of dynamic planning, where multiple periods are considered over the planning horizon. In this model, a distribution company (DISCO), as the owner of the network, intends to minimize the planning and operational costs. Optimal loading of transformers is considered, which is utilized to operate the transformers efficiently. Here, to model the problem, a mixed-integer nonlinear programming (MINLP) model is utilized. Using the GAMS software, the decision variables of the problem, such as the site and size of the installation of distribution transformers, and their service areas specified by the LV lines over the planning years, and the reactive power generation/absorption of the smart inverters over the years, seasons, and hours are determined. To tackle the operational challenges such as voltage control in the points of common coupling (PCC) and the limitations in the hosting capacity of the network for the maximized penetration level of PV cells, a smart-inverter model with voltage control capability in PCC points is integrated into the expansion-planning problem. Then, a two-stage procedure is proposed to integrate the reactive power exchange capability of smart inverters in the distribution expansion planning. Based on the simulations of a residential district with PV penetration, results show that by a 14.7% share of PV energy generation, the loss cost of LV feeders is reduced by 28.3%. Also, it is observed that by optimally making use of the reactive power absorption capability of the smart inverters, the hosting capacity of the network is increased by 50%.
Analysis of Correlation between the Socioeconomic Environment and Level of Nutrition in the Population of Serbia: A Part of the National Survey
Authors: Grujicic Mladen , Sekulic Marija , Stepovic Milos , Zdravkovic Natasa , Markovic Vladan , Gavrilovic Jagoda , Veselinovic Mirjana , Vuckovic-Filipovic Jelena , Nikolic Katarina , Milovanovic Olivera , Radmanovic Branimir , Milosevic Bojan , Vucic Rada , Jakovljevic Stefan , Ignjatovic Vesna , Radovanovic Snezana
Published: July 2023

Being overweight is one of the leading health problems of the 21st century. In different parts of Europe, different overweight statuses are noted. The aim of this study was to explore the correlation between the socioeconomic environment and the level of nutrition in the Serbian population. This research is part of the fourth national survey of the health of the population of Serbia. It was conducted as a descriptive, cross-sectional study. For the purposes of this research, the adult population over the age of 20 was included, and 12,439 respondents were analyzed. The nutritional status was assessed by the body mass index value—BMI. In order to investigate the differences between groups, the chi-squared test was used. The risk of being overweight was evaluated by calculating the odds ratio value, using univariate and multivariate regression. The prevalence of obesity was higher in females than in males, but pre-obesity was more common among male respondents. Males in the age category 55–64 years old, married, employed, with a higher level of education and material status were more likely to be overweight. As for the females, being overweight was most common among unemployed and economically inactive respondents, widowed/divorced, aged 65–74, with a primary school educational level and low material status. The level of nutrition is significantly associated with the socioeconomic environment.
Improvement of Higher Heating Value and Hygroscopicity Reduction of Torrefied Rice Husk by Torrefaction and Circulating Gas in the System
Authors: Wongsiriwittaya Montree , Chompookham Teerapat , Bubphachot Bopit
Published: July 2023

This study aimed to enhance the thermal characteristics of rice husk biomass through torrefaction conducted in a fixed-bed reactor. A novel approach was employed by circulating the gas produced within the system, instead of using traditional nitrogen. The torrefaction process took place at temperatures ranging from 200 to 320 °C, with different residence times of 10, 20, and 30 min for heat exchange. Quantitative analysis of the torrefied biomass revealed several notable improvements. The higher heating value of the biomass increased significantly, reaching 23.69 MJ/kg at a temperature of 320 °C and a residence time of 30 min. This enhancement indicates the effectiveness of torrefaction in increasing the energy content of the biomass. Furthermore, the torrefied biomass exhibited a remarkable reduction in hygroscopicity, with reduction by as much as 92 wt% compared to raw rice husk biomass. This reduction implies that the torrefied biomass is more resistant to moisture absorption, making it more stable and suitable for various applications. The torrefaction process in the fixed-bed reactor yielded a torrefied biomass with a production yield of 76 wt% (RH-320, RT30). This yield showcases the potential of the employed technique for producing a substantial amount of high-quality torrefied biomass. The resulting biomass holds great promise for diverse applications. It can be utilized for industrial steam production, contributing to the efficient use of biomass resources. Moreover, it could serve as an alternative fuel source for biomass power plants, offering a sustainable energy solution. Overall, this study demonstrates the effectiveness of the proposed torrefaction method in enhancing the thermal characteristics of rice husk biomass. The improved energy content and reduced hygroscopicity make torrefied biomass a valuable resource for various industries, promoting the utilization of biomass as a renewable energy source.
Determination of Energy Consumption and Technical Efficiency of Cotton Farms in Türkiye
Authors: Aytop Yeşim
Published: July 2023

This study aimed to examine the efficiency of cotton farms and the energy requirements of the input and output of cotton in Türkiye. Data were collected from 657 cotton farms, and the results showed that the energy input of machinery (28.69%) had the most significant share in the total energy input, followed by electricity (22.79%) and nitrogen (20.75%). The total energy consumption of cotton was 83,869.49 MJ ha−1. In cotton production, the energy use efficiency, energy productivity, specific energy, and net energy were measured to be 0.87, 0.07, 17.31, and −23,043.92 MJ per hectare. Cotton plants consumed more indirect energy (51.99%) than direct energy (48.01%) and more non-renewable energy (89.96%) than renewable energy (10.04%). According to the data envelopment analysis results, the average technical efficiency of cotton farms was 0.84. Inefficient farms can reduce their inputs by approximately 16% without reducing the amount of cotton production. Allocative efficiency, pure technical efficiency, and scale efficiency of cotton farms were determined at 0.570, 0.539, and 0.640, respectively. Human labour, machinery, diesel, nitrogen, and phosphate energy use should be reduced for inefficient farms to become more efficient.
A Systematic Review of Energy Management Systems for Battery/Supercapacitor Electric Vehicle Applications
Authors: Wangsupphaphol Aree , Phichaisawat Sotdhipong , Nik Idris Nik Rumzi , Jusoh Awang , Muhamad Nik Din , Lengkayan Raweewan
Published: July 2023

The purpose of this research is to present a thorough evaluation of energy management systems that consist of hybrid energy storage systems and their control algorithms, which may be used in electric vehicles. This paper outlines the characteristics of electric vehicles, research methods, an analysis of the hybrid energy storage system architecture, the converter topology, and energy management techniques. The strength and co-occurrence of keywords over the past ten years are shown in this study using a systematic research framework for performing a literature review and using keyword analysis techniques. This study reveals a pattern of recently and frequently used terms in works of literature. Consequently, their suitability, benefits, and drawbacks are assessed. In this study, the hybrid energy storage system and converter circuit architecture are evaluated and rated. A non-isolated DC-DC converter connected to an SC is a suitable configuration for the hybrid converter because it is simple to build, is reliable, and has minimal loss/weight/cost, which all improve vehicle performance. In terms of the application of control strategies, it is shown that deterministic and fuzzy-rule-based control techniques are successfully assessed using real-scale vehicle experiments and can be selected for manufacturing. On the other hand, real-time optimization-based energy management techniques have been effectively shown in lab-scale tests and may be used in a future real-scale vehicle.
Shaping Sustainable Cities: A Long-Term GIS-Emanated Spatial Analysis of Settlement Growth and Planning in a Coastal Mediterranean European City
Authors: Vardopoulos Ioannis , Ioannides Sophia , Georgiou Marios , Voukkali Irene , Salvati Luca , Doukas Yannis E.
Published: July 2023

European cities have experienced rapid (and often conflicting) transformations that include, inter alia, the environment, the economy, society, climate change, and access to affordable housing, with implications for their future development. In order to address such issues, assessing the current situation of European metropolises is crucial to understanding new urban development models. In light of these dynamics, our study focused on urban expansion in Pafos, Cyprus, between 1993 and 2021. Such dynamics were examined through photointerpretation (using kernel density estimation) via the ArcGISPro spatial analyst tool. The empirical results of this analysis are considered particularly insightful, especially those regarding urban sprawl and its implications for future land management for the study area, and could help toward shaping specific policies to guide cities towards sustainable and environmentally friendly development. The analysis reveals a gradual increase in settlements over time (55%), although the rate of growth has decreased in recent years, as a consequence of the economic crisis. Density maps revealed varying levels of urban concentration density, highlighting the presence of high-density settlement cores downtown and low-density, dispersed settlements in the surrounding districts. According to the empirical findings of this study, urban growth in Pafos features fragmented development patterns with scattered building landscapes and large empty spaces. To address the challenges of urban sprawl in the study area, specific actions are proposed to promote sustainable urban development and mitigate its negative impacts. By examining Pafos building/settlement development and urban planning, stakeholders can gain valuable insights and implement viable solutions for the future. This study contributes to the evolving discourse on urbanization, its characteristics, causes, and consequences, and highlights the importance of the “compact city” model as a counterforce to urban sprawl and a pathway to sustainability.
Analyzing Interrelationships and Prioritizing Performance Indicators in Global Product Development: Application in the Chinese Renewable Energy Sector
Authors: Malek Razika , Yang Qing
Published: July 2023

Today’s global business landscape and intense market competition have heightened the significance of global product development (GPD) practices, making it necessary for organizations to evaluate GPD projects for sustained success. Existing research has primarily focused on independent and tangible performance metrics, neglecting their interdependencies and intangible nature in real-world scenarios. To address this gap, this study seeks to enhance the understanding of GPD performance by establishing a more sophisticated assessment approach. A hybrid decision-making trial and evaluation laboratory (DEMATEL) and analytical network process (ANP) method, known as DANP, was employed to determine the importance of evaluation metrics and their relationships. This research stands out by integrating financial, quality, time, environmental, and capability dimensions, along with their respective indicators, and presenting their interrelationships and priority weights for evaluating GPD projects. The proposed framework was validated through an in-depth case investigation conducted with a Chinese sustainable energy solutions company, involving extensive discussions with decision-makers. The results indicate that technological, innovation, and environmental indicators are the most critical metrics. The influential network relation map (INRM) derived from these findings offers practical recommendations to enhance GPD project performance, catering to policymakers and researchers in both managerial and theoretical contexts.
Research on the Impact of Global Economic Policy Uncertainty on Manufacturing: Evidence from China, the United States, and the European Union
Authors: Li Yifei , Bai Yuhang
Published: July 2023

Events such as COVID-19 and the Russia–Ukraine conflict have significantly increased the uncertainty and volatility of global economic policies. In the context of economic globalization, the key question we investigate is whether global economic policy uncertainty will have different impacts on the manufacturing of the three major economies in China, the United States, and Europe Union. This study employs the time-varying parameter vector autoregressive (TVP-VAR) model to examine how global economic policy uncertainty (GEPU) affected manufacturing from March 2008 to March 2023. The empirical results show that the effects of GEPU are time varying; its short-term effects on Chinese manufacturing are slightly greater than its medium- and long-term effects, whereas its medium- and long-term effects on manufacturing in the United States (US) and European Union (EU) are significantly greater than its short-term effects. The impact of European debt crisis, the China–US trade war and Russia–Ukraine conflict on EU manufacturing is higher than that of China and the US, and the impact of the COVID-19 pandemic on China’s manufacturing is much smaller than that of the US and the EU; thus, Chinese manufacturing has a greater capacity for risk mitigation than US and EU manufacturing. This study not only provides a new perspective on the study of global economic policy uncertainty; it also provides new empirical evidence on how global economic policy uncertainty affects the manufacturing sector in China, the US and Europe and provides policymakers with guidance for decision making.
Systems Precision Medicine: Putting the Pieces Back Together
Authors: Farina Lorenzo
Published: July 2023

Systems precision medicine is an interdisciplinary approach that recognises the complexity of diseases and emphasises the integration of clinical knowledge, multi-omics data, analytical models, and the expertise of physicians and data analysts to personalise the care pathway in complex diseases, such as cancer or diabetes. The aim is to gain a comprehensive understanding of diseases by analysing individual components and identifying relevant aspects for therapy and diagnosis. Key components, their interactions and emerging patterns can be studied using statistical, mathematical and computational tools. The combination of data analysis and clinical evaluation is crucial to effective decision-making, emphasising the need for an integrative approach rather than relying on data alone. Therefore, the crucial point discussed in this paper is that the “computational” part and the “artistic” part (i.e., the physician’s intuition) cannot be separated, and therefore, systems precision medicine can be configured as a collective work of art, involving not only different medical professionals but also, and above all, professional data analysts. The work is “artistic” because data and mathematics alone, without medical knowledge of the context, are not enough. But the work is also “collective” in the sense that it must be the place of cultural integration between the professional intuition of the physician, which cannot be translated into mathematical formulas, and the ability to extract information from multi-omics data of the data analysts, who instead use formal and computational mathematical methods. However, to drive the medical revolution and reassemble a patient’s parts, data analysts need to be involved in the hospital context, and precision medicine physicians should embrace data analytical perspectives. This will require ongoing dialogue, new languages of communication, and education that promotes continuous learning and collaboration between professions, fostering a new level of interdisciplinary collaboration for personalised care.
Does Green Finance Expand China’s Green Development Space? Evidence from the Ecological Environment Improvement Perspective
Authors: Wang Zhe , Teng Yin-Pei , Wu Shuzhao , Chen Huangxin
Published: July 2023

It is important to explore the intrinsic mechanism of green finance’s role in widening the green development space for China, in order to optimize the structure of green financial development and accelerate the construction of a modernized economic system. Taking ecological environment improvement as a new research perspective, this paper presents the impacts and mechanisms of green finance on the green development space of the economy and society through the fixed-effect model and moderating-effect model, based on panel data from 30 provinces and municipalities in China from 2011 to 2020. The findings show that green finance development in China significantly expands the green development space of the economy and society, and this conclusion did not change after robustness tests such as replacing the main variables, adjusting the study interval, and considering endogeneity. In terms of its mechanism of action, ecological environment improvement plays an important mediating and regulating role in the process of green finance, essentially magnifying the green development space of the economy and society. In terms of a heterogeneity analysis, the effect of green finance on the expansion of the green development space is the largest in the eastern region, followed by the northeastern region, and the smallest in the central and western regions. In addition, the positive effect of green finance is relatively larger in regions with a higher urbanization level, government fiscal expenditure level, foreign investment level, and advanced industrial structure. The main contribution of this paper is to the field of green development, revealing the important role of the ecological benefits of green finance, which can help to achieve high-quality sustainable development in the economy and society.
Risk Assessment Considering the Bioavailability of 3-β-d-Glucosides of Deoxynivalenol and Nivalenol through Food Intake in Korea
Authors: Lee Sang Yoo , Cho Solyi , Woo So Young , Hwang Myungsil , Chun Hyang Sook
Published: July 2023

Deoxynivalenol and nivalenol are major type B trichothecenes and the most frequently occurring mycotoxins worldwide. Their 3-β-d-glucoside forms have recently become a safety management issue. These glucoside conjugates are converted back to the parent toxins during human digestion, but studies to confirm their bioavailability are lacking. In this study, a risk assessment was performed considering the bioavailability of glucoside conjugates. A literature review was conducted to compile the existing bioavailability studies of glucoside conjugates, and three exposure scenarios considering bioavailability were established. As a result of a risk assessment using deterministic and probabilistic methods, both the deoxynivalenol and nivalenol groups had safe levels of tolerable daily intake percentage (TDI%), not exceeding 100%. The TDI% for the nivalenol group was approximately 2–3 times higher than that for the deoxynivalenol group. Notably, infants showed higher TDI% than adults for both toxin groups. By food processing type, the overall TDI% was highest for raw material, followed by simple-processed and then fermented-processed. Since glucoside conjugates can be converted into parent toxins during the digestion process, a risk assessment considering bioavailability allows the more accurate evaluation of the risk level of glucoside conjugates and can direct their safety management in the future.
The Potential Role of SARS-CoV-2 Infection and Vaccines in Multiple Sclerosis Onset and Reactivation: A Case Series and Literature Review
Authors: Tavazzi Eleonora , Pichiecchio Anna , Colombo Elena , Rigoni Eleonora , Asteggiano Carlo , Vegezzi Elisa , Masi Francesco , Greco Giacomo , Bastianello Stefano , Bergamaschi Roberto
Published: July 2023

The recent SARS-CoV-2 pandemic and related vaccines have raised several issues. Among them, the potential role of the viral infection (COVID-19) or anti-SARS-CoV-2 vaccines as causal factors of dysimmune CNS disorders, as well as the safety and efficacy of vaccines in patients affected by such diseases and on immune-active treatments have been analyzed. The aim is to better understand the relationship between SARS-CoV-2 infection/vaccines with dysimmune CNS diseases by describing 12 cases of multiple sclerosis/myelitis onset or reactivation after exposure to SARS-CoV-2 infection/vaccines and reviewing all published case reports or case series in which MS onset or reactivation was temporally associated with either COVID-19 (8 case reports, 3 case series) or anti-SARS-CoV-2 vaccines (13 case reports, 6 case series). All the cases share a temporal association between viral/vaccine exposure and symptoms onset. This finding, together with direct or immune-based mechanisms described both during COVID-19 and MS, claims in favor of a role for SARS-CoV-2 infection/vaccines in unmasking dysimmune CNS disorders. The most common clinical presentations involve the optic nerve, brainstem and spinal cord. The preferential tropism of the virus together with the presence of some host-related genetic/immune factors might predispose to the involvement of specific CNS districts.
Natural History of Oral HPV Infection among Indigenous South Australians
Authors: Ju Xiangqun , Sethi Sneha , Antonsson Annika , Hedges Joanne , Canfell Karen , Smith Megan , Garvey Gail , Logan Richard M. , Jamieson Lisa M.
Published: July 2023

This study aims to describe the natural history of and identify the risk factors associated with oral human papillomavirus (HPV) infections in an Australian Indigenous cohort. A longitudinal cohort study design, with baseline (2018), 12-month, and 24-month data obtained from Indigenous Australians aged 18+ years in South Australia, was performed. Face-to-face interviews were conducted, and saliva samples for HPV testing were collected at each time point. Basic descriptive analyses were conducted to calculate prevalence, incidence, persistence, clearance, and incidence proportions of any HPV infection. Multivariable logistic regression analyses with adjusted prevalence ratios (PRs) were conducted to identify risk factors associated with oral HPV infection. Among 993 participants with valid saliva samples, 44 HPV types were identified. The prevalence of infection with any oral HPV infection was 51.3%, high-risk HPV was 11%, and types implicated in Heck’s disease (HPV 13 or 32) was 37.4%. The incidence, persistence, and clearance of any and high-risk HPV infections were 30.7%, 11.8% and 33.3% vs. 9.3%, 2.8%, and 9%, respectively. Our findings indicate that the prevalence, incidence, and persistence of oral HPV infection in a large sample of Indigenous Australians were high, and clearance was low. Oral sex behaviours and recreational drug use were risk factors associated with incident high-risk HPV infection.