bluuebunny/crossref_metadata_2025 huggingface dataset:
Progressive Mitochondrial Dysfunction of Striatal Synapses in R6/2 Mouse Model of Huntington’s Disease
Authors: Petersen Maria Hvidberg , Willert Cecilie Wennemoes , Andersen Jens Velde , Madsen Mette , Waagepetersen Helle Sønderby , Skotte Niels Henning , Nørremølle Anne
Published: March 2022

Background: Huntington’s disease (HD) is a neurodegenerative disorder characterized by synaptic dysfunction and loss of white matter volume especially in the striatum of the basal ganglia and to a lesser extent in the cerebral cortex. Studies investigating heterogeneity between synaptic and non-synaptic mitochondria have revealed a pronounced vulnerability of synaptic mitochondria, which may lead to synaptic dysfunction and loss. Objective: As mitochondrial dysfunction is a hallmark of HD pathogenesis, we investigated synaptic mitochondrial function from striatum and cortex of the transgenic R6/2 mouse model of HD. Methods: We assessed mitochondrial volume, ROS production, and antioxidant levels as well as mitochondrial respiration at different pathological stages. Results: Our results reveal that striatal synaptic mitochondria are more severely affected by HD pathology than those of the cortex. Striatal synaptosomes of R6/2 mice displayed a reduction in mitochondrial mass coinciding with increased ROS production and antioxidants levels indicating prolonged oxidative stress. Furthermore, synaptosomal oxygen consumption rates were significantly increased during depolarizing conditions, which was accompanied by a marked increase in mitochondrial proton leak of the striatal synaptosomes, indicating synaptic mitochondrial stress. Conclusion: Overall, our study provides new insight into the gradual changes of synaptic mitochondrial function in HD and suggests compensatory mitochondrial actions to maintain energy production in the HD brain, thereby supporting that mitochondrial dysfunction do indeed play a central role in early disease progression of HD.
A non-cooperative behavior management method for multi-attribute large group decision-making
Authors: Dong Xiaoqin , Sun Xianbin
Published: March 2022

In multi-attribute large group decision-making (MALGDM), the ideal state indicates a high degree of consensus among a set of decision-makers (DMs). It is complex to reach consensus because the number of decision attributes and DMs increases. Thus, we developed a novel consensus model to manage the decision-making in large group based on the non-cooperative behavior. The improved clustering method takes account of the similarities among different DMs. Similar DMs will be grouped into the same group. The consensus threshold is determined from an objective and subjective aspect to judge whether the consensus reaching process continues. With the introduction of three non-cooperative behaviors, we investigated a non-cooperative behavior detection method under the change of consensus level. Base on the number of DMs who are willing to change their preliminary views and the change value of consensus level, the non-cooperative degree of subgroup can be computed. According to the non-cooperative degree, the subgroups’ weight can be modified to raise the consensus level. Meanwhile, the subgroup is allowed to change. Based on the adjustment amount of DMs’ opinions, whether decision maker (DM) belongs to this subgroup is recalculated. Finally, an emergency decision-making problem in flood disaster is applied to manifest the feasibility and distinctive features of the proposed method.
RETRACTED: A multi-objective mathematical model of financial flows in Omni-Channel distribution systems
Authors: Gheitasi Mohsen , Feylizadeh Mohammad Reza , Ahari Roya M.
Published: March 2022

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.
RETRACTED: Recognition method of internal concrete structure defects based on 1D-CNN
Authors: Xu Juncai , Zhang Jingkui , Shen Zhenzhong
Published: March 2022

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.
Illumination invariant face recognition using contourlet transform and convolutional neural network
Authors: Hussain Muhammad , Alotaibi Fouziah , Qazi Emad-ul-Haq , AboAlSamh Hatim A.
Published: March 2022

The face is a dominant biometric for recognizing a person. However, face recognition becomes challenging when there are severe changes in lighting conditions, i.e., illumination variations, which have been shown to have a more severe effect on recognition performance than the inherent differences between individuals. Most of the existing methods for tackling the problem of illumination variation assume that illumination lies in the large-scale component of a facial image; as such, the large-scale component is discarded, and features are extracted from small-scale components. Recently, it has been shown that large-scale component is also important; in addition, small-scale component contains detrimental noise features. Keeping this in view, we introduce a method for illumination invariant face recognition that exploits large-scale and small-scale components by discarding the illumination artifacts and detrimental noise using ContourletDS. After discarding the unwanted components, local and global features are extracted using a convolutional neural network (CNN) model; we examined three widely employed CNN models: VGG-16, GoogLeNet, and ResNet152. To reduce the dimensions of local and global features and fuse them, we employ linear discriminant analysis (LDA). Finally, ridge regression is used for recognition. The method was evaluated on three benchmark datasets; it achieved accuracies of 99.7%, 100%, and 79.76% on Extended Yale B, AR, and M-PIE, respectively. The comparison reveals that it outperforms the state-of-the-art methods.
RETRACTED: A simple and efficient technique for leaf extraction in complex backgrounds of low resolution mobile photographed images
Authors: Pushpa B.R. , Shobha Rani N.
Published: March 2022

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.
The study of cross-validated bagging fuzzy-ID3 algorithm for breast cancer classification
Authors: Idris Nur Farahaina , Ismail Mohd Arfian
Published: March 2022

Globally, the second most common cause of death for female cancer patients is breast cancer. In the United States, about 11,000 females aged below 40 are diagnosed with invasive breast cancer each year. Early detection of breast cancer is the foundation for preventing the progression of the disease, and the diagnosis can be conducted using intelligent systems for quicker detection. Based on the FUZZYDBD method and bootstrap aggregation (bagging) technique, the Bagging fuzzy-ID3 algorithm (BFID3) was proposed for this study. This method combined the techniques of the fuzzy system, ID3 algorithm and bagging. For BFID3’s data fuzzification, the automatic fuzzy database definition method, known as the FUZZYDBD method, would assist in developing the fuzzy database. One of the weaknesses of the ID3 algorithm is its incapability to handle continuous data. The problem was resolved via the linguistic variable replacement and data fuzzification in the BFID3. Meanwhile, this paper’s implementation of the bagging technique improved the generalization ability and reduced overfitting. Additionally, BFID3 was verified through an extensive comparison with several existing methods to investigate the competency of the proposed method. The study identified that BFID3 was proficient in breast cancer classification.
RETRACTED: A novel fault detection and identification system for solar PV systems using truncated arrangement of active cell (TAAC) structure
Authors: Jeyasingh Dani Abraham , Rajamanickam Manickaraj Sasiraja , Govindhan Radhakrishnan Rajesh Kanna
Published: March 2022

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.
Deep-Hist: Breast cancer diagnosis through histopathological images using convolution neural network
Authors: Iqbal Saeed , Qureshi Adnan N.
Published: March 2022

Breast cancer diagnosis utilizes histopathological images to get best results as per standards. For detailed diagnosis of breast cancer, microscopic analysis is necessary. During analysis, pathologists examine breast cancer tissues under different magnification levels and it takes a long time, can be hampered by human interpretation and requires expertise of different magnifications. A single patient usually requires dozens of such images during examination. Since, labelling the data is a computationally expensive task, it is assumed that the images for all patients have the same label in conventional image-based classification and is not usually tested practically. In this study, we are intending to investigate the significance of machine learning techniques in computer aided diagnostic systems based on analysis of histopathological breast cancer images. Publicly available BreakHis data set containing around 8,000 histopathological images of breast tumours is used for conducting experiments. The recently proposed non-parametric approach is proven to show interesting results when compared in detail with machine learning approaches. Our proposed model ’Deep-Hist’ is magnification independent and achieves > 92.46% accuracy with Stochastic Gradient Descent (SGD) which is better than the pretrained models for image classification. Hence, our approach can be used in processing data for use in research and clinical environments to provide second opinions very close to the experts’ intuition.
Improved YOLO object detection algorithm to detect ripe pineapple phase
Authors: Cuong Nguyen Ha Huy , Trinh Trung Hai , Meesad Phayung , Nguyen Thanh Thuy
Published: March 2022

A computational method for detecting pineapple ripening could lead to increased agricultural productivity. It is possible to predict fruit maturity before harvesting to increase agricultural productivity. A ripe fruit’s quality, its standard content of physical and chemical properties will increase the value of a good when traded outside the market. This paper studies and improves the Tiny YOLO-v4 model for identifying the pineapple ripening period. Researchers studied pineapples in a pineapple garden in Vietnam’s central region. They wanted to determine when pineapples were ripe. The API and the website are based on the YOLO innovation model. Apps and website APIs will be available for mobile devices so that people can monitor fruits. Technology transfer and academic research are combined in this study. We prepared the pineapple data set by using 5,000,000 pineapples harvested from the pineapple farm at different stages of growth. To make the measurements, we improved the YOLO-v4 algorithm. This results in a more accurate training model and reduced train-ing time. A 98.26% recognition accuracy is quite impressive. Research takes place at large-scale plantations, so the models are created from the data collected at the plantations and are used as labels; training takes a long time for the tiniest details about pineapples, and finding pineapple-growing regions takes a long time. The deep learning classifier was able to process pineapple plantation photos by using the camera on the mobile phone.
An EEMD-CNN-BiLSTM-attention neural network for mixed frequency stock return forecasting
Authors: Cai Yi , Guo Jinlu , Tang Zhenpeng
Published: March 2022

The regularly issued low frequency data, such as the change of fund position (weekly), and Producer Price Index (monthly), can affect the subsequent trend of stock returns. However, the forecasting effect of low frequency data on high frequency has not been discussed amply. This paper proposes a new mixed frequency neural network that helps to fill this research gap. The original time series is decomposed into several components through ensemble empirical mode decomposition, then the frequency alignment method is applied to integrate the high frequency component with low frequency variable as inputs, and the CNN-BiLSTM-Attention network completes the remaining forecasting work. The empirical results show that compared with other benchmark models, the proposed procedures perform better when predicting the high frequency components and obtain a smaller statistical error in the final ensemble results. The proposed model has great potential for the forecasting of reverse mixed time series.
A novel approach for anomaly detection in automatic meter intelligence system using machine learning and pattern recognition
Authors: Anh Nguyen Thi Ngoc , Anh Pham Ngoc Quang , Thu Vu Hoai , Van Thai Doan , Solanki Vijender Kumar , Tuan Dang Minh
Published: March 2022

Anomaly detection for sensor systems is one of the most researched topics for the Internet of Thing systems. Researchers have been attracted to machine learning classification problems that are considered the most effective techniques. The novel model is proposed by combining anomaly pattern Symbolic Aggregate Approximation (SAX), processing imbalance data and machine learning techniques for sensor anomaly detection. The advantage of anomaly patterns and machine learning leads to the the proposed model to have better performance. The proposed model consists of three phases: finding anomaly pattern features, processing imbalanced data, exploring data by machine learning model. In this paper, the main contributions with respect to previous works can be listed as follows: (i) Successful modeling the new method of SAX for time series data for finding complex and dynamic anomaly patterns. (ii) Archiving applied anomaly pattern feature into machine learning model Random Forest and hyperparameters optimisation of these model. (iii) Fitfully proposed a model combining SAX, imbalance technique, and random forest to anomaly detection. (iv) Achieving applied proposal model in automatic meter intelligence system in Vietnam. The experiential results of the proposed model have described the robustness and better performance for detecting anomalies of power meter sensors.
Image processing based fault classification in power systems with classical and intelligent techniques
Authors: Sabih Muhammad , Umer Muhammad , Farooq Umar , Gu Jason , Balas Marius M. , Asad Muhammad Usman , Qureshi Khurram Karim , Khan Irfan A. , Abbas Ghulam
Published: March 2022

This paper is devoted to develop interest of power system engineers in learning basic concepts of image processing and consequently using deep networks to solve problems of complex power system networks. To this end, we study fault classification in a power system through automation of equal area (EAC) criterion. By considering EAC graphs as images and using classical image processing techniques, we successfully distinguish between different transient conditions including sudden change of input power as well as short circuit at the sending end and middle points of a single and double circuit transmission lines. In addition to classification, some parameters are also determined from EAC images such as initial rotor angle, clearing angle, and maximum rotor angle. Further, the use of deep networks is introduced to perform the same task of fault classification and a comparison is drawn with multilayer perceptron neural networks. Developed algorithms are tested in MATLAB as well as Pytorch environments.
TextGraph - A lexicon based framework for concept extraction and visualization
Authors: Shaikh Anoud , Mahoto Naeem Ahmed , Unar Mukhtiar Ali
Published: March 2022

The shift in the news consumption from traditional newspapers to online news has led media analysts and researchers to apply powerful text mining techniques on the vast amount of news data. News has a profound influence on public as it informs about the events happening around them and may affect them. It keeps the people connected and allows them to engage in the decision making process. The words used in the news language are sometimes taken from the regional languages so as to express a new phenomenon, event or idea. In this paper, we have proposed a lexicon based framework named as TextGraph that automatically extracts the concepts from the Dawn news using the Term Frequency–Inverse Document Frequency (TF-IDF) weighting factor and visualizes them in a formal way. To achieve value-add insights, we have developed Pakistani English corpus and used it along with other existing dictionaries. Our proposed corpus incorporates the Pakistani English words used in the Dawn news stories, which are annotated and validated by a human expert. Experimental results show that our concept extraction method out performs and gives more specific concepts. Our research suggests that the proposed framework and corpus opens multiple directions for promising future research in this domain.
Predict the trend of economic indicators in time series based on recurrent neural network combined with leading indicators
Authors: Wu Jimmy Ming-Tai , Tsai Meng-Hsiun , Cheng Chao-Chieh , Wu Mu-En
Published: March 2022

With the rise in popularity of personal computers and decreasing cost, even a personal computer can execute complex and large calculations. So more researchers can invest in AI and machine learning. Humans can’t handle massive data sets or data that requires a long time to read and evaluate, whereas big data frameworks can read and analyze in a reasonable time. So relevant research has increased recently. In the social sciences, machine learning is used to forecast future trends and the index trend. Keeping up with current events is crucial nowadays to debate countermeasures in time. This study combines economic indicators from 1988 to 2017 with leading indicators and other types of indicators. The recurrent neural network model predicts economic index trends and tests multiple variables. The proposed methods measure the error in predicting future trends in different models to learn which indicators work well together.
A robust classification system for Southern Yellow cow behavior using 3-DoF accelerometers
Authors: Tran Duc-Nghia , Phi Khanh Phung Cong , Solanki Vijender Kumar , Tran Duc-Tan
Published: March 2022

Modern methods of monitoring help cow farmers save significantly monitoring time and improve cow health care efficiency. Behavioral changes when cows are sick may include increased or decreased daily activities such as increased lying or decreased walking time. Accelerometer advantages are low power consumption, small size, and lightweight. Thus, accelerometers have been widely used to monitor cow behavior. A cow monitoring system usually includes a central processor for receiving and processing information according to a behavioral classification algorithm through the cows’ movements. This paper introduces an effective classification system for Southern Yellow cow behavior using three degrees of freedom (3-DoF) accelerometers. The proposed classifier applied GBDT algorithm (16 seconds window) with five features, offers the good performance while investigating with four Southern Yellow cattle. The classification achievement was assessed and compared to existing ones regarding sensitivity, accuracy, and positive predictive value.
Cloud-oriented fault tolerance technique based on resource state
Authors: Khiat Abdelhamid
Published: March 2022

In this paper, we propose a new cloud reactive fault management technique called Hybrid Redundant Array of Independent resources for cloud computing (H_RAIC). The latter uses a new concept called Redundant Array of Independent resources for cloud computing (CRAIR), which is inspired by a powerful conventional technique called Redundant Arrays of Inexpensive Disks (RAID). H_RAIC takes into consideration the cloud resources state and aims to satisfy both cloud users and cloud provider requirements. Our solution was compared with the replication technique which represents a specific case of CRAIR, and with other CRAIR levels defined in this paper. The results show that our technique is a promising solution, that can be used to meet both user and provider requirements.
A novel distributed spectrum management in mobile edge computing based cognitive radio Internet of Things networks
Authors: Benidris Fatima Zohra , Limam Said
Published: March 2022

The integration of cognitive radio (CR) in Internet of Things (IoT) is an effective step into the smart technology world. The capability of CR can effectively solve spectrum-related issues for IoT applications, but this association is still a big challenge that has led to a new research dimension of CR-based IoT. To this extend, in this paper the authors propose a novel distributed spectrum management approach based on mobile edge computing (MEC) technology in cooperative environment that enables CRIoT devices to share the unutilized spectrum efficiently. The simulation results show that the proposed solution achieves good performance in terms of spectrum access/sharing and maintains a balance energy consumption of CRIoT users within lower latency.
Effects of computerized cognitive training on cognitive function, activity, and participation in individuals with stroke: A randomized controlled trial
Authors: Ho Hsiu-Yu , Chen Ming-De , Tsai Chiu-Chin , Chen Hui-Mei
Published: March 2022

BACKGROUND: Computerized cognitive training (CCT) is an emerging alternative intervention for stroke survivors. OBJECTIVE: This study investigated the effects of CCT on the cognition, activity, and participation of stroke survivors and compared the findings with those of match-dosed conventional cognitive training. METHODS: This randomized controlled trial included 39 patients with stroke who were divided into the intervention group (n = 19; receiving CCT with Lumosity software) and the control group (n = 20; receiving conventional cognitive training). Both the groups were trained for 20 min, twice a week, for 12 weeks. Participants were evaluated at pretest, posttest, and 4-week follow-up. Outcome measures included various cognitive function tests and the Stroke Impact Scale scores. RESULTS: The CCT group exhibited significant improvement in global cognitive function (evaluated using the Mini-Mental State Examination and Montreal Cognitive Assessment) and specific cognitive domains: verbal working memory (backward digit span test), processing speed (Symbol Digit Modalities Test), and three MoCA subtests (attention, naming, and delayed recall). CCT exerted no significant effect on activities and participation. No significant between-group differences in changes in cognitive function were noted. However, CCT significantly improved cognitive function domains immediately after training, and these effects were sustained at the 4-week follow-up. CONCLUSIONS: Cognitive function of individuals with chronic stroke could improve after administration of CCT. However, future studies with a more rigorous design and higher training dose are warranted to validate our findings.
Comparative effects of passive and active mode robot-assisted gait training on brain and muscular activities in sub-acute and chronic stroke1
Authors: Shin Jiwon , An HongGi , Yang Sejung , Park Chanhee , Lee Yongseok , You Sung (Joshua) H.
Published: March 2022

BACKGROUND: Robot-assisted gait training (RAGT) was initially developed based on the passive controlled (PC) mode, where the target or ideal locomotor kinematic trajectory is predefined and a patient basically ‘rides’ the robot instead of actively participating in the actual locomotor relearning process. A new insightful contemporary neuroscience and mechatronic evidence suggest that robotic-based locomotor relearning can be best achieved through active interactive (AI) mode rather than PC mode. OBJECTIVE: The purpose of this study was to compare the pattern of gait-related cortical activity, specifically gait event-related spectral perturbations (ERSPs), and muscle activity from the tibialis anterior (TA) and clinical functional tests in subacute and chronic stroke patients during robot-assisted gait training (RAGT) in passive controlled (PC) and active interactive (AI) modes. METHODS: The present study involves a two-group pretest-posttest design in which two groups (i.e., PC-RAGT group and AI-RAGT group) of 14 stroke subjects were measured to assess changes in ERSPs, the muscle activation of TA, and the clinical functional tests, following 15– 18 sessions of intervention according to the protocol of each group. RESULTS: Our preliminary results demonstrated that the power in the μ band (8– 12 Hz) was increased in the leg area of sensorimotor cortex (SMC) and supplementary motor area (SMA) at post-intervention as compared to pre-intervention in both groups. Such cortical neuroplasticity change was associated with TA muscle activity during gait and functional independence in functional ambulation category (FAC) and motor coordination in Fugl– Meyer Assessment for lower extremity (FMA-LE) test as well as spasticity in the modified Ashworth scale (MAS) measures. CONCLUSIONS: We have first developed a novel neuroimaging experimental paradigm which distinguished gait event related cortical involvement between pre- and post-intervention with PC-RAGT and AI-RAGT in individuals with subacute and chronic hemiparetic stroke.
Effects of Bobath treatment and specific mobilizations on gait in stroke patients: A randomized clinical trial
Authors: Grozdek Čovčić Gordana , Jurak Ivan , Telebuh Mirjana , Maček Zdravko , Bertić Želimir , Žura Nikolino , Grubišić Mihaela , Matić Hrvoje , Tišlar Marina Horvat , Jakuš Lukrecija
Published: March 2022

BACKGROUND: After a stroke, patients experience sensorimotor damage, balance disorders, loss of selective movement, hypotonia and/or hypertonia, and hypersensitivity, all of which affect gait. OBJECTIVE: The aim of the study was to establish the effectiveness of Bobath therapy with additional specific soft tissue mobilizations versus standard Bobath intervention. METHODS: Subjects were randomly divided into two groups (S1 and S2) of 20 people each. Both groups underwent the same intervention (Bobath concept) over a 5-week period, while the second study group (S2) also received additional, specific soft tissue mobilization. The Berg Balance Scale (BBS), Timed Up and Go Test (TUGT), Active Range of Motion (AROM) of dorsiflexion and knee flexion and extension were used as clinical variables to assess the efficacy of therapy modalities. Data was analysed using a mixed model ANOVA. RESULTS: A significant interaction of group and time was found. The experimental (S2) group had a larger improvement of balance and AROM than the control (S1) group. There was no clear advantage of one group over the other for TUGT. CONCLUSION: The findings demonstrate that a combination of Bobath treatment and additional specific soft tissue mobilizations are more effective in increasing AROM and balance and mobility.
Advancing our field by academically advancing pediatric rehabilitation medicine physicians
Authors: Houtrow Amy J. , Akamagwuna Unoma O. , Holman Lainie , Bosques Glendaliz
Published: March 2022

Academic promotion is desired by many faculty practicing at academic medical institutions, but the criteria for promotion often appear opaque to many physician faculty. In nearly all cases, evidence of scholarship is required regardless of academic track. Academic advancement can be stymied by unclear expectations, lack of protected time to engage in scholarly projects, insufficient evidence of dissemination, and limited guidance, mentorship and sponsorship. In addition to being important for promotion, scholarship is an essential aspect of academic medicine because it helps inform and advance the science. Pursuing academic excellence is an important goal for pediatric rehabilitation medicine faculty members because it helps advance the care of children with disabilities and the field itself. Pediatric rehabilitation medicine faculty in the clinician educator or clinician leader tracks are encouraged to understand the criteria for advancement, seek out mentorship, scholarize their career ikigai and identify opportunities to demonstrate academic excellence.
Selection of the calculated equivalent of the supporting devices of U-shaped frames of earthmoving vehicles
Authors: Gorbatyuk Ievgenii , Komotska Svitlana , Mishchuk Dmitry , Volianiuk Vladimir
Published: March 2022

The article describes the choice of the design equivalent of the supporting devices of U-shaped frames of earthmoving vehicles. When calculating the U-shaped frames of earth-transport machines for the action of forces located in the plane of the frame, the supporting devices must be represented and calculated either as hinged-fixed supports or as a statically defined system.

The difference in the choice of the calculated equivalent of the supporting devices leads to an is-total difference in the estimated load of the entire frame. Considering the same frame with the same load, but with different resistance devices, it is easy to reconcile that the value of the largest bending moment in the transverse part of the raw can vary by 30-35%. A significant difference in load exists in the longitudinal parts of the frame. In this regard, there is a need to formulate the criteria by which it can be concluded that the supporting frame device that is designed corresponds to the calculated equivalent.

The correct choice of the calculated equivalent of the supporting devices of the ground-transport machine frame significantly affects the results obtained during calculations and helps to greatly simplify them.
Denúncias contra o governo brasileiro às cortes internacionais
Authors: Santos Talita Sebastianna Braz , Pádua Thainá Penha
Published: March 2022

O presente artigo objetiva investigar a situação do Governo de Jair Messias Bolsonaro, sobretudo no ano de 2020 e 2021 e a (in) existência da adoção de medidas de combate à pandemia do vírus Covid-19, o que tem causado diversas denúncias em órgãos internacionais de proteção aos Direitos Humanos. Para a elaboração deste estudo utilizou-se o método de pesquisa analítico indutivo, partindo-se de uma concepção micro analítica para uma macro analítica, tendo como principal fonte a bibliográfica e documental. No que tange ao resultado, percebeu-se que, muito embora o ordenamento jurídico do Brasil tenha a previsão de diversos direitos e garantias fundamentais e seja signatário de muitas normas protecionistas de Direitos Humanos, na atual gestão, muitos estão sendo negligenciados de forma ignóbil, o que tem caracterizado grave violação à ordem constitucional vigente, sobretudo após o advento da pandemia.
ANALISIS STRUKTUR GEDUNG TAHAN GEMPA DENGAN METODE SISTEM GANDA (DUAL SYSTEM)
Authors: Hendra Hendra , Zulkarnaen Lio Varan , Rosanti Indah , Ariyansyah Rona
Published: March 2022

Most parts of Indonesia are earthquake prone areas with medium and high intensity. In the process of constructing the building, it needs to pay attention to several aspects. One of it is the effect of the earthquake. Earthquakes always attack the joint structure so that the structure needs to be designed using the moment-bearing frame system method with the concept of strong columns and weak beams combined with shear walls, so that the building can muffle earthquake vibrations. The building of Lidra Hotel, located on Raya Sedau Street, Samudera Indah beach, Sungai Raya Kepulauan sub-District, Bengkayang Regency, West Kalimantan Province. The building consists of 17 floors with a height of 67 meters from the ground, reinforced concrete structure. Based on the results of the Standard Penetration Test, this building will be builded on medium ground (SD site class) and included in the Seismic Design Category (KDS) D, then the calculation of this building uses the Special Moment Resistant Frame System (SRPMK) combined with a shear wall, so it is called the Dual System Method. Earthquake load planning uses the 2017 Indonesia Earthquake Map using the spectrum response method according to SNI 1726-2019. Calculation of loading according to SNI 1727-2020 and PPPURG 1987, structural concrete requirements for buildings according to SNI 2847-2019, calculation of structural steel roof according to SNI 1729-2020. Based on the calculation of the roof some results were obtained the size of the column and the truss size IWF 148.100.6.9mm, CNP gording 150.50.20.3,2mm, 1 type of plate 130mm;  2 types of beams 350mm/700mm and 300mm/600mm;  4 types of columns, K1 1200mm x 600mm, K2 800mm x 800mm K3 1000mm x 500mm, and K4 700mm x 700mm;  1 type of shear wall 300mm;  ladder plate and balustrade 130mm; 4 types of foundation 3600mm x 3600mm x 700mm – spun pile 9 D 600mm;  1500mm x 1500mm x 400mm – spun pile 4 D 350mm;  3000mm x 3000mm x 500mm – spun pile 9 D 500mm;  7600mm x 5600mm x 1000mm – spun pile 12 – 800mm.
PENILAIAN KERENTANAN BANGUNAN DI KABUPATEN MANOKWARI SELATAN AKIBAT GEMPA BUMI
Authors: Tethool Yoga Charol Vincenthius , Birawaputra Indra , Putri Mayang K. F. , Safkaur Natalia E. , Ohoilulin Veronika Y. , Rombe Geraldo R.
Published: March 2022

Earthquake is a natural occurrence in which energy is released and propagated from the earth to the ground surface. The effects of the earthquakes include failure of building, landslides, tsunamis and so on. Mitigation actions are required to reduce the number of people killed or injured as a result of earthquakes. Assessing the vulnerability of buildings to earthquakes is one of the mitigation efforts. The Hazus Method is used to assess the vulnerability of a building. This method requires information such as: building typology and seismic activity at the location to be studied. The goal of this study is to assess the level of earthquake susceptibility based on the structure type in Manokwari Selatan Regency. Concludingly, the building typology in Manokwari Selatan Regency will be divided into five categories of vulnerability, specifically: no damage, light damage, moderate damage, major damage, and collapse. Based on observations, there are several typologies of the existing buildings in the Manokwari Selatan Regency, namely: wooden light frames (W1), Steel Moment Frame (S1L), Concrete Moment Frame C1L) and Reinforced Masonry Bearing (RM1L). The results of the analysis show that the typology Steel Moment Frame (S1L) has the possibility of light, moderate, heavy damage, collapse and no damage, respectively 7.13%, 32.66%, 39%, 18.13%, and 3.07%; the typology Concrete Moment Frame (C1L) are 8.39%, 34.91%, 36.84%, 16.35%, and 3.51% respectively; the typology wooden light frames (W1) are 18.16%, 47.89%, 21.18%, 10.03%, and 2.74%; the typology Reinforced Masonry Bearing (RM1L) has a probability of 10.76%, 34.45%, 36.06%, 13.14%, and 5.59%.
SYNTHESIS AND PROPERTIES OF 2-SUBSTITUTED ORGANOSILICON 1,4-DIENE EPOXIDES
Authors:   Institute of Polymer Materials, NAS of Azerbaijan, Garamanov A.M. , Gusiev N.Kh. ,   Institute of Polymer Materials, NAS of Azerbaijan, Gurbanov M.Sh. ,   Institute of Polymer Materials, NAS of Azerbaijan, Akhmedova R.A. ,   Institute of Polymer Materials, NAS of Azerbaijan, Allakhverdieva S.N. ,   Institute of Polymer Materials, NAS of Azerbaijan
Published: March 2022

The addition reaction of triorganosilanes to 1-(5-methylhexene-5-yn-2-yloxy)-2,3-epoxypropane in the presence of H2PtCl6.6H2O catalyst proceeds via a triple bond, but with an advantageous yield (75-85%) of the β-isomer (relative to the oxygen atom). The addition of dimethylamine to the epoxy ring leads to secondary amino alcohols, and the corresponding episulfide is obtained by the reaction of epoxysilane with thiocarbamide. Epoxy-1,4-dienes can be used as modifiers for phenol-formaldehyde resin
THERMODYNAMIC PROPERTIES OF Sb2Te3-BASED SOLID SOLUTIONS IN THE SnTe–Sb2Te3 SYSTEM
Authors:   M.Nagiyev Institute of Catalysis and Inorganic Chemistry, NAS of Azerbaijan, Seyidzade А.E. , Aghayeva A.A. ,   M.Nagiyev Institute of Catalysis and Inorganic Chemistry, of NAS of Azerbaijan, Orujlu E.N. ,   M.Nagiyev Institute of Catalysis and Inorganic Chemistry, NAS of Azerbaijan, Imamaliyeva S.Z. ,   M.Nagiyev Institute of Catalysis and Inorganic Chemistry, of NAS of Azerbaijan
Published: March 2022

The SnTe–Sb2Te3 system was investigated by using EMF measurements of reversible concentration cells relative to the SnTe electrode in the 300–400 K temperature range. The formation of solid solutions (up to 20 mol%) based on Sb2Te3 was confirmed. Based on EMF measurements, the equations of the temperature dependences of the EMF for solid solutions based on Sb2Te3 with compositions of 5, 10, and 20 mol% SnTe were obtained and these results were furtherly used to calculate the partial thermodynamic functions of SnTe in alloys. The standard thermodynamic functions of the formation of solid solutions of the above compositions were calculated by graphical integration of the Gibbs-Duhem equation along the SnTe–Sb2Te3 section
Dinamismo ambientale, esperienza digitale del board, e cambiamento strategico delle imprese. L'integrazione tra Dynamic Managerial Capabilities e Resource Dependence Theory
Authors: Boccardelli Paolo , Acciarini Chiara , Peruffo Enzo
Published: March 2022

A partire dalla considerazione che il cambiamento strategico rappresenta una tema centrale negli studi di management e strategia, lo studio si propone di investigare le decisioni strategiche delle imprese in contesti caratterizzati da elevati livelli di dinamismo. Inoltre, l'introduzione in letteratura della variabile che misura l'esperienza digitale del board, intesa come rapporto tra membri con esperienza professionale in ambiti digital-related e il totale dei componenti del board, consente di valutare l'impatto sul metodo (fusioni e acquisizioni, alleanze strategiche e joint venture) e sull'ampiezza (diversificazione e internazionalizzazione) del cambiamento strategico. I risultati dimostrano l'associazione non solo tra maggiore dinamismo ambientale e cambiamento esperienza digitale del board e lo stesso cambiamento strategico d'impresa. Le implicazioni teoriche e pratiche consentono di apprezzare il coinvolgimento del board non solo nel controllo, ma anche nella direzione strategica dell'impresa, e di comprendere come le imprese decidano di orchestrare il proprio set di capacità dinamiche per allinearsi ai cambiamenti esterni. 
Early warning system e governance del rischio di credito: l'emersione anticipata della crisi d'impresa dal punto di vista dei creditori finanziari
Authors: Boldreghini Fulvio , Sancetta Giuseppe
Published: March 2022

La capacità di stimare la probabilità di default è da sempre uno degli obiettivi principali delle istituzioni finanziarie e, nell'imminente futuro, sarà sicuramente la chiave per far fronte all'aumento delle esposizioni deteriorate dovuto agli impatti dell'emergenza pandemica da Covid-19. Il presente lavoro si propone di indagare le pratiche di governance e gli strumenti di gestione del rischio di credito adottati per rilevare tempestivamente segnali di squilibrio economico e finanziario delle società. Di conseguenza, è stata prima condotta una revisione approfondita dei modelli di previsione dell'insolvenza per illustrare limiti e punti di forza ampiamente discussi in letteratura. In secondo luogo, si è fatto ricorso all'analisi di un case study per descrivere sia la metodologia dell'Early Warning System (EWS), sia le pratiche di governance interna adottate per monitorare e gestire tempestivamente i segnali di deterioramento del merito creditizio. I risultati evidenziano la necessità di combinare modelli quantitativi con un approccio di tipo judgmental per costruire un processo di monitoraggio in grado di intercettare anomalie ed evitare errate classificazioni. L'analisi del case study mostra inoltre come il coinvolgimento anticipato degli esperti di ristrutturazione del debito sia un elemento chiave nella definizione delle azioni volte a prevenire eventi di default. 
ESG, CSR, Shareholder value: non è il momento per un reset del capi
Authors: Miglietta Angelo
Published: March 2022

Recentemente si è assistito a una crescente attenzione agli interessi di tipo sociale e valoriale che riguardano l'impresa. Tra questi certamente meritano una menzione speciale le tematiche relative a Environment, Social and Governance e di Corporate Social Responsibility, che oggi permeano mass media e influenzano il sentire comune: fino al punto di sostenere che le imprese debbano abbandonare l'obiettivo del profitto per perseguire invece finalità ambientali e sociali. Nel lavoro si dimostra quanto sbagliata e pericolosa, perché contraria all'interesse generale, risulti tale posizione. Così, attraverso un percorso argomentativo, si riconcilia la shareholder theory con la stakeholder theory, dimostrando che la ricchezza prodotta dall'impresa non è una bene di cui beneficiano solo gli azionisti ma tutta la società nel suo insieme, e che ciò è possibile solo se viene perseguito l'obiettivo di ottimizzazione, e non necessariamente massimizzazione, della creazione di valore per gli azionisti. Emerge come corollario rilevante la necessità di un opportuno intervento regolatore dello Stato, senza il quale il perseguimento degli obbiettivi di interesse generale può essere realizzato solo marginalmente, con poca efficienza e in modo arbitrario da parte del management.
Tensioni e sommosse. La nobiltà napoletana fra i regni di Filippo II e Filippo III (1585-1620)
Authors: Eliszezynski Giuseppe Mrozek
Published: March 2022

L'articolo si propone di dimostrare il ruolo avuto dalle principali famiglie aristocratiche nell'evoluzione politica del viceregno napoletano, in un periodo di grande complessità. In particolare, l'analisi si sviluppa attorno a tre momenti cardine: il linciaggio dell'Eletto del Popolo Starace nel 1585, il passaggio dal regno di Filippo II a quello di Filippo III e la progressiva crisi del potere del duca di Lerma e dei suoi alleati. I fatti del 1620 e la tormentata fine del governo del III duca di Osuna costituiscono l'ideale conclusione di una fase storica molto travagliata.
El orden del Inca: gobierno virreinal y sujeción político-laboral de los indios del Perú colonial (1560-1570)
Authors: Morong Reyes Germán , Gloël Matthias
Published: March 2022

Durante il decennio 1560-1570, nel giovane vicereame peruviano, la produzione di testi di ordine investigativo e informativo, legittimati sull'esperienza in situ e richiesti dai sovrani spagnoli, favorì la circolazione di conoscenze sugli indios e sugli Incas che risultarono fondamentali, strumentalmente parlando, al progetto di sottomissione lavorativa promossa dalla Corona iberica. Poiché furono proprio i funzionari reali del XVI secolo ad applicare la cultura della moderna conoscenza empirica alle pratiche di dominazione e amministrazione, l'articolo - che si basa sull'analisi di un corpus limitato di documenti, composto da lettere, informes e tratados - mostra le sostanziali differenze ravvisabili nella descrizione delle popolazioni assoggettate. Mentre la descrizione degli indios si basava su criteri atavici di inferiorità e incapacità, quella degli Incas mostra come i viceré, gli studiosi e i governatori suggerissero frequentemente al regime coloniale di ripristinare le leggi precedentemente in vigore presso questa popolazione, dal momento che il loro antico sistema normativo, oltre a fornire elementi utili al disciplinamento e alla convivenza politica degli indigeni, aveva anche favorito il massimo impegno lavorativo delle comunità assoggettate.
Il falso riformismo degli anni Novanta ovvero l'inarrestabile affermazione della concorrenza come paradigma della regolazione sociale
Authors: Iannello Carlo
Published: March 2022

Le prospettive, quindi, non appaiono affatto rasserenanti quanto a tenuta della democrazia, quanto a vigore di quella certa idea che la voleva partecipata, militante della causa della giustizia e dell'uguaglianza. Perché proprio le istituzioni che per garantirla e realizzarla furono dettate dal Costituente italiano sono in pericolo. Soprattutto i principi fondamentali che il Costituente volle e sancì appaiono abbandonati o rinnegati. Il clima storico è profondamente mutato. Non c'è quasi più sedimento, né forse memoria, tantomeno rimpianto dei giorni, delle parole, delle speranze, delle donne e degli uomini di questo Paese che, affrancati dal dominio politico, esterno e interno, si disponevano ad affrancarsi dalle altre forme di dominio, da quella economica a quella culturale e sociale a quella di genere, più intense e profonde forse, anche se apparivano meno incombenti di quanto fossero, meno minacciose e imperiose. E che perciò ci diedero la speranza che avremmo potuto liberarcene, tutti insieme. Non ci siamo riusciti. Ma sappiamo che fu la stagione più alta della nostra storia nazionale. E così ci apparve e ci appare lasciando volentieri al revisionismo storico il piacere di mestare nel fango, il mestiere di pitoccare i compensi che otterrà il suo trasformismo, la paga che meriterà la voluttà del servilismo
Generating cropping schemes from FADN data at the farm and territorial scale
Authors: Bazzani Guido M. , Spadoni Roberta
Published: March 2022

The paper presents an innovative approach to cropping scheme classification based on fad n data with two main goals. First, the identification at the regional level (NUTS 2) of land use patterns common to similar farms defined ‘group cropping scheme'. Second, the farm-level construction of farm cropping schemes, which expand the observed crop mix and identify suitable variation ranges considering the farm production context. The schemes are based on the observed behaviour of homogeneous farms and capture their common structural characteristics regarding land use.The schemes can be used at the territorial scale to analyse landuse trends and patterns over time. At the farm level, the method is designed to analyse short-term adaptations and is suitable to be used, together with other data, in mathematical programming models to run policy analysis exercises. At this latter scale, crop substitution within a scheme allows the set of eligible crops to be expanded while remaining linked to the observed behaviour on a spatial basis.The paper applies the methodology to identify and quantify the cropping schemes using FADN data on Italian farms specialising in annual field crops. An algorithm implemented in gams automates the process. Results confirm the validity of the method and open a field of research for future applications.
Mapping data granularity: The case of FADN
Authors: Cardillo Concetta , Vitali Giuliano
Published: March 2022

The present analysis looks into the issue of mapping information contained in the fadn database aimed at finding a methodology useful as a preliminary analysis to data extraction.To the purpose the concept of data granularity has been introduced. The method has been used to perform a farm-based analysis, revealing a wide heterogeneity of factors and levels that show the existence of specific data ‘patches'. The work proved to be able to increase awareness regarding effective data availability as a preliminary analysis to queries performed on relational data-bases which are not designed from a systems basis, and that can be considered valid for any survey-supplied data.