Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel wind turbine fault diagnosis based on deep transfer learning of improved residual network and multi-target data

In industrial production, problems such as lack of data, complex fault types, and low generalizability of deep learning models seriously affect the fault diagnosis of wind turbines. Therefore, we have developed a fault diagnosis model for wind turbines under harsh conditions to address the above problems. First, the collected one-dimensional vibration data is reshaped into two-dimensional form by using the Gramian Angular Field. The two-dimensional form not only extends the spatial structure of the data, but also effectively improves the information expression of the data. In addition, the data is classified into large-scale data, medium-scale data, small-scale data, class-imbalanced data, and heterogeneous data based on the data type. Then, the deep residual network structure is redesigned to improve the diagnostic performance of the model based on the sensitivity of the reshaped data to the size of the convolutional kernel, and the new structure of the network is employed to implement transfer learning. Finally, we adopt the developed fault diagnosis model to achieve the fault diagnosis of bearings and gears in the wind turbine gearbox. Meanwhile, an automatic hyperparameter search mechanism was added to improve the partial hyperparameter optimization in this study. It is demonstrated that the model proposed in this study has excellent diagnostic performance with multi-target data for wind turbines, and has excellent generalizability and reliability.
The comparative assessment of welders’ exposure to welding fumes based on mass and number concentration

Introduction: Nowadays, Shielded Metal Arc Welding (SMAW) is the most widely used arc welding. During the welding operation, typically, various harmful agents such as fumes, gases, heat, sound and ultraviolet radiation are produced of which fume is the most important component from the viewpoint of occupational health. The present study aims to compare the number and the mass concentration emitted in SMAW to determine the most appropriate index of exposure to fumes in the welding processes. Material and Method: In this study, the portable laser aerosol spectrometer and dust monitor of GRIMM, model 1.106, was used to measure the number and mass concentration of fumes emitted from SMAW on 304 stainless steel with a thickness of 0.4 mm. Air sampling was performed at a distance of 41 cm representing the welder’s breathing zone. The measurements of number concentration (NC) and mass concentration (MC) were taken under the condition of 25 volt voltage and direct current of the electrode polarity. Result: The total NC and MC of welding fumes in welder’s breathing zone was 1140451 particles per liter and 1631.11 micrograms per cubic meter, respectively. The highest number concentration was found to correspond to the particles with 0.35 to 0.5 micrometer-sized distribution (NC1; 938976 particles per liter) and the lowest was related to the particles with 5 to 6.5 micrometer-sized distribution (NC7; 288 particles per liter) and the particles larger than 6.5 micrometer (NC8; 463 particles per liter). Moreover, the highest mass concentration was related to the particles with 0.35 to 0.5 micrometer-sized distribution (MC1; 450 micrograms per cubic meter) and the particles larger than 6.5 micrometer (MC8; 355 micrograms per cubic meter). Conclusion: The findings indicated that there is no agreement between number and mass concentration as two particles assessment index, and as the particles’ size become smaller, the mismatch of them is becoming more apparent. Since the smaller particles penetrate into the lower respiratory tract and have higher potential for adverse health effects, it is necessary to measure and assess particles in various size distributions and especially the smaller fraction of particles. Therefore, it is thought that considering the mass concentration alone and not paying attention to number concentration in the assessment of exposure to particles in the industrial workplaces and specifically in welding stations will not be reflected valid assessment of adverse health effects of welding fumes as a systemic poison on body organs.
Potential Utilization of Brevundimonas diminuta to Reduce Ammonia in Wastewater

Ammonia is a harmful substance on wastewater if discharged to the river, then it must be treated well. This research was aimed to examine the potential for the type of bacteria Brevundimonas diminuta in the ammonia degradation process of wastewater. Earlier step was done to examine bacteria growth in synthetical medium of ammonia concentration ranged 5 – 25 ppm. It shows that the bacteria could grow well within that ranges. Then, the bacteria performances were examined to grow in wastewater sampled from Musi River containing ammonia ranged 1.9 – 2.94 mg/L in the airlift bioreactor. The wastewater used in bioreactor was 1 L. Air was injected into the bioreactor with variations in the air flow rate of 1.5 to 4.5 L/m and with varying observation times from 1.5 to 6 hours. The optimal decrease in ammonia levels has occurred when the air flow rate was 3 L/min for 6 hours and the ammonia level was reduced from 2.94 mg/L to 1.76 mg/L.
LCC-HVDC's Systematical Impact on Voltage Stability: Theoretical Analysis and a Practical Case Study

Line-commutated-converter HVDC (LCC-HVDC) is generally believed to impair voltage stability. While in a China's practical power grid, increase of HVDC power unexpectedly enhances the stability. The phenomenon reveals that existing studies overlook HVDC's systematical impact on the receiving AC system. This paper empirically elaborates how the phenomenon arises, theoretically clarifies the mechanism, and demonstrates its effect on practical system operating. In this paper, systematical features of power grid are visually depicted by two vectors, i.e., network structural vector and power-flow conditional vector. A unified burden rate is proposed to evaluate voltage stability of any AC bus. Impact of HVDC is explicitly expressed by two positive-definite terms and one negative-definite term. The latter of them is overlooked by most existing literatures, and enhances voltage stability by reducing the burden rate. The proposed methodology is validated by simulations with a practical power system in Asia with tens of HVDC infeed and tens of thousands of AC nodes.
Prognostication of incidence and severity of ischemic stroke in hot dry climate from environmental and non-environmental predictors

Background: Recently, rapid fluctuations of ambient temperature were found to be associated with hospital admission for cardiovascular diseases in general and the ischemic stroke in particular. Objective: to test if climatic factors predict the incidence of stroke reliably and to study the predictive potential of risk factors for a stroke. Materials and methods: In a retrospective design, we studied 566 patients admitted to the stroke unit in 2016-2019. A distributed lag nonlinear model was used to explore immediate and delayed effects of weather and clinicodemographic risk factors on the stroke incidence. Supervised machine learning was used to build models predictive of the mRS score. We assessed model performance by calculating R2, mean absolute error and root-mean-square error. Results and conclusions: We found a non-correlation between the weather parameters and statistics on stroke. The disparities in their trends lead us to investigate behind time effects of the environment with distributed lag models and a concordant impact of all the settings - with machine learning models. If categorized into two classes by severity and functional outcomes, the cases have few disparities in the weather parameters within a week before the stroke onset. In contrast to the groups classified by severity, the ones grouped by outcomes have a significant difference in age, nationality, the presence of background diseases and smoking status. We ranked environmental and individual risk factors by the information gain that they provide to the models. Inclusion of the weather parameters into the machine learning model predicting the mRS score provides a slight boost in performance.
Energy Efficiency Optimization for Integrated Sensing and Communications Systems

In this paper, we consider an energy efficient waveform design in integrated sensing and communication (ISAC) systems. The transmitted waveform simultaneously serves multiple communication users and estimates the parameters of a moving target. In order to improve its energy efficiency (EE) while guaranteeing target estimation performance, we maximize the EE of the emitted dual-use waveform, under a Cramér-Rao bound (CRB) constraint. However, the considered optimization problem is a fractional function that is highly non-convex. Thus, we firstly adopt fractional programming based on Dinkelbach’ method and then, solve the sub-problem by leveraging semi-definite relaxation (SDR). Numerical results demonstrate superior performance than the benchmark and show the trade-off between EE and CRB.
How to Take a “Portrait” of Interpersonal Synergies Formation? – Exemplar Data with Expert Badminton Doubles

Abstract In the last decades, previous research regarding interpersonal synergies formation has emerged under the Uncontrolled Manifold concept, identifying synergies formation based on a single combination of task elements. However, in social systems with redundant degrees of freedom, several combinations to stabilize a performance goal may be the most common picture. Thus, the main goal of this explorative study was to create a “portrait” of a set of task elements combinations (i.e., player’s positions and velocities) adopted to stabilize a performance goal (i.e., interpersonal distance) in expert badminton doubles, which is usually associated with synergies formation. The Uncontrolled Manifold concept was used for that purpose. Our results displayed that the created “portraits” did not display any pattern of interpersonal synergies. Moreover, for all the portraits there were combinations of task elements that had the primacy over the others. A primacy that changed with the rally length. For the shortest and for the longest rallies, the player’s velocity seemed to be more relevant to form interpersonal synergies, whereas for the middle length, both the player’s position and velocity were important for that purpose. Also, there were a decreasing of synergies strength with the increasing of the rally length.
Analysis of the paddy fields to support community and tourism activities in Ciemas District, Indonesia

Ciletuh Geopark Tourism Area (UNESCO Global Geopark Ciletuh Palabuhanratu) is now one of the main tourist attractions in the Sukabumi Regency, West Java province of Indonesia. The geopark area is in the Ciemas District. Based on the Regional Spatial Planning of the Sukabumi Regency, the Ciemas District is designated as the center of the agricultural sector activities for paddy rice plants in the Sukabumi District. Through the development of tourism activities, Ciletuh Palabuhanratu Geopark will undoubtedly threaten the rice farming sector in the area. In the disruption era, as technology changes, information and knowledge become more accessible. The development of these tourism activities will grow faster in Ciemas District, causing many changes, especially in physical space and regional economics. One change would be in the rice fields in this area, which has been transformed into built-in land in villas, inns, and homestays. If this is allowed, then the function of the sustainability of agricultural activities in the Ciemas District will be threatened. For this reason, control efforts are needed so that paddy fields that must be maintained do not change function quickly. This study analyzes the land use and land cover for paddy fields in Ciemas District. The method of this study is the quantitative method. The analytical methods include population projection, ArcGIS analysis, and Land Potential Index. The findings contribute to supporting the development of tourism while maintaining food security. The results indicate that there are protected areas from being prioritized, which are not prioritized for paddy fields, can be used for the development of tourism activities. Thus, this can be used as a source for the government to make policies for fulfilling food requirements and developing tourism activities in the region.
An Improved Two-states Cyclical Dynamic Model for Plastic Waste Management

The panacea to the global challenge of plastic waste management is the transition towards plastic circular economy, which can be sustained through tailor-made management strategies. However, cutting-edge strategic solutions are constrained by inadequate data due to inadequate plastic-based predictive models. This paper presents an improved version of an existing two-state cyclical dynamic closed (CDC) model. The CDC model was formulated using a homogeneous linear system of ordinary differential equations (ODEs) and was modified by introducing a separation target which plays an essential role in determining both quantity and quality of recycled plastics. The Laplace transforms technique was the main analytic solution technique used. Values of the parameters were computed using the global plastic data applied for the existing CDC model, and with a technique termed the nth-order product derivative proximity, alternating pairs of initial values were selected each for the global annual plastic production and the global annual plastic waste generation. The validation process of the new CDC model was accomplished using the root mean squared error (RMSE) and the mean average percentage error (MAPE), which are measures of the model’s predictive power. Comparatively, RMSEs of the new CDC model were smaller than the RMSEs of the existing CDC model. MAPEs for the new CDC model were 6.5% and 7% (as against 13% and 18% in the existing model) respectively for the global annual plastic: production and waste generation, indicating that the new model predicts with 93.5% and 93% degrees of accuracy respectively for the global annual plastic: production and waste generation. Therefore, the new CDC model has outperformed the existing CDC model in terms of predictive power, and thus, establishing the new CDC model as an improved version of the existing one. The model can therefore make impactful policy decisions for sustainable plastic waste management thereby aiding to achieve the transition towards circular economy in plastic waste management.
Anticancer, Anticoagulant, Antioxidant and Antimicrobial Activities of Thevetia peruviana Latex with Molecular Docking of Antimicrobial and Anticancer Activities

Natural origin molecules represent reliable and excellent sources to overcome some medicinal problems. The study of anticancer, anticoagulant, and antimicrobial activities of Thevetia peruviana latex were the aim of the current research. An investigation using high-performance liquid chromatography (HPLC) revealed that the major content of the flavonoids are rutin (11.45 µg/mL), quersestin (7.15 µg/mL), naringin (5.25 µg/mL), and hisperdin (6.07 µg/mL), while phenolic had chlorogenic (12.39 µg/mL), syringenic (7.45 µg/mL), and ferulic (5.07 µg/mL) acids in latex of T. peruviana. Via 1,1-diphenyl-2- picrylhydrazyl (DPPH) radical scavenging, the experiment demonstrated that latex had a potent antioxidant activity with the IC50 43.9 µg/mL for scavenging DPPH. Hemolysis inhibition was 58.5% at 1000 µg/mL of latex compared with 91.0% at 200 µg/mL of indomethacin as positive control. Negligible anticoagulant properties of latex were reported where the recorded time was 11.9 s of prothrombin time (PT) and 29.2 s of the activated partial thromboplastin time (APTT) at 25 µg/mL, compared with the same concentration of heparin (PT 94.6 s and APPT 117.7 s). The anticancer potential of latex was recorded against PC-3 (97.11% toxicity) and MCF-7 (96.23% toxicity) at 1000 μg/mL with IC50 48.26 μg/mL and 40.31 µg/mL, respectively. Disc diffusion assessment for antimicrobial activity recorded that the most sensitive tested microorganisms to latex were Bacillus subtilis followed by Escherichia coli, with an inhibition zone (IZ) of 31 mm with minimum inhibitory concentration (MIC) (10.2 μg/mL) and 30 mm (MIC, 12.51 μg/mL), respectively. Moreover, Candida albicans was sensitive (IZ, 28 mm) to latex, unlike black fungus (Mucor circinelloides). TEM examination exhibited ultrastructure changes in cell walls and cell membranes of Staphylococcus aureus and Pseudomonas aeruginosa treated with latex. Energy scores of the molecular docking of chlorogenic acid with E. coli DNA (7C7N), and Rutin with human prostate-specific antigen (3QUM) and breast cancer-associated protein (1JNX), result in excellent harmony with the experimental results. The outcome of research recommended that the latex is rich in constituents and considered a promising source that contributes to fighting cancer and pathogenic microorganisms.
Synthesis of nanostructured flower-like MoS2 and its friction properties as additive in lubricating oils

Purpose This paper aims to synthesize nanostructured flower-like molybdenum disulphide (MoS2) particles and evaluate their suitability to act as additives for lubricants. Design/methodology/approach A hydrothermal method without any surfactant successfully produced 250 nm mean diameter Nanostructured flower-like MoS2 particles. The resulting product was then solvothermally treated with absolute ethanol producing lipophilic powder particles which after being dispersed in two different lubricants were tribologicaly characterized by using a pin-on-disk tribometer geometry with reciprocal movement under immersion. Findings The influence on the stability and the coefficient of friction of the nano-oils is shown to be strongly dependent on their molecular structure. After 1 h of tribological testing, a ramified polyolester oil with 1 Wt. % of MoS2 nanoflowers did not show decrease in its average coefficient of friction, while an additive naphthenic oil with the same concentration of nanoparticles showed a decrease of 86 per cent. Originality/value In this paper, nanostructured flower-like MoS2 synthesized by the hydrothermal method was dispersed in lubricating oils to study its stability and tribological properties. To ensure good stability of the nano-oils, the particles were solvothermally treated with absolute ethanol to dehydrate and lipophilize them. A ramified polyolester-based nano-oil with 1 Wt. % of MoS2 nanoflowers showed no decrease in its steady state coefficient of friction, while a naphthenic-based nano-oil with the same concentration of nanoparticles presented a remarkable 86 per cent reduction.
Gluconate-Lactobionate-Dextran Perfusion Solutions Attenuate Ischemic Injury and Improve Function in a Murine Cardiac Transplant Model

Static cold storage is the cheapest and easiest method and current gold standard to store and preserve donor organs. This study aimed to compare the preservative capacity of gluconate-lactobionate-dextran (Unisol) solutions to histidine-tryptophan-ketoglutarate (HTK) solution. Murine syngeneic heterotopic heart transplantations (Balb/c-Balb/c) were carried out after 18 h of static cold storage. Cardiac grafts were either flushed and stored with Unisol-based solutions with high-(UHK) and low-potassium (ULK) ± glutathione, or HTK. Cardiac grafts were assessed for rebeating and functionality, histomorphologic alterations, and cytokine expression. Unisol-based solutions demonstrated a faster rebeating time (UHK 56 s, UHK + Glut 44 s, ULK 45 s, ULK + Glut 47 s) compared to HTK (119.5 s) along with a better contractility early after reperfusion and at the endpoint on POD 3. Ischemic injury led to a significantly increased leukocyte recruitment, with similar degrees of tissue damage and inflammatory infiltrate in all groups, yet the number of apoptotic cells tended to be lower in ULK compared to HTK. In UHK- and ULK-treated animals, a trend toward decreased expression of proinflammatory markers was seen when compared to HTK. Unisol-based solutions showed an improved preservative capacity compared with the gold standard HTK early after cardiac transplantation. Supplemented glutathione did not further improve tissue-protective properties.
Developing Innovation Skills Through Work-integrated Learning

Innovation skills are an asset for engineering managers as innovation has become a key competitive driver for technology oriented industries. Increasing attention is being placed on designing course offerings that help develop innovation skills and particularly, creating suitable contexts in which innovation can flourish. This study investigates factors influencing the development of innovation skills through work-integrated learning. Moving beyond a single viewpoint to a dyadic perspective involving students and industry supervisors, this qualitative study contributes to a conceptual framework for fostering the development of innovation skills through industry placements. Implications are discussed for engineering and technology managers for developing innovation capacity and skills.
Applying Design and Technology Education in Addressing Farmers’ Problems in the Makonde Rural District, Zimbabwe

Abstract This paper reports on the results of a developmental and participatory research study that is coupled with a significant, ongoing and long-term community development project in the Makonde Rural District, Northwestern Zimbabwe. From the beginning, this project has involved the application of Design and Technology Education in problem-solving with the intention to help farmers develop a sustainable localised model of technology for the processing and production of maizemeal, peanut butter and cooking oil. Following design principles adopted from literature, various machines were designed and developed for the purpose. The results and findings reported here include those drawn from the scientific/technical observations made during the development of these machines, followed by the interviews conducted with participants/end users.
Automatic Personality Evaluation from Transliterations of YouTube Vlogs Using Classical and State of the art Word Embeddings

The study of automatic personality recognition has gained attention in the last decade thanks to a variety of applications that derive from this field. The big five model (also known as OCEAN) constitutes a well-known method to label different personality traits. This work considers transliterations of video recordings collected from YouTube (originally provided by the Idiap research institute) and automatically generated scores for the five personality traits which also were provided in the database. The transliterations are modeled with two different word embedding approaches, Word2Vec and GloVe and three different levels of analysis are included: regression to predict the score of each personality trait, binary classification between strong vs. weak presence of each trait, and the tri-class classification according to three different levels of manifestations in each trait (low, medium, and high). According to our findings, the proposed approach provides similar results to others reported in the state-of-the-art. We think that further research is required to find better results. Our results, as well as others reported in the literature, suggest that there is a big gap in the study of personality traits based on linguistic patterns, which make it necessary to work on collecting and labeling data considering the knowledge of expert psychologists and psycholinguists.