Lack of ApoE inhibits ADan amyloidosis in a mouse model of familial Danish dementia.

The Apolipoprotein E-ε4 allele (APOE-ε4) is the strongest genetic risk factor for late onset Alzheimer disease (AD). ApoE plays a critical role in amyloid-ß (Aß) accumulation in AD, and genetic deletion of the murine ApoE gene in mouse models results in a decrease or inhibition of Aß deposition. The association between the presence of ApoE and amyloid in amyloidoses suggests a more general role for ApoE in the fibrillogenesis process. However, whether decreasing levels of ApoE would attenuate amyloid pathology in different amyloidoses has not been directly addressed. Familial Danish dementia (FDD) is an autosomal dominant neurodegenerative disease characterized by the presence of widespread parenchymal and vascular Danish amyloid (ADan) deposition and neurofibrillary tangles. A transgenic mouse model for FDD (Tg-FDD) is characterized by parenchymal and vascular ADan deposition. To determine the effect of decreasing ApoE levels on ADan accumulation in vivo, we generated a mouse model by crossing Tg-FDD mice with ApoE KO mice (Tg-FDD+/-/ApoE-/-). Lack of ApoE results in inhibition of ADan deposition up to 18 months of age. Additionally, our results from a genetic screen of Tg-FDD+/-/ApoE-/- mice emphasize the significant role for ApoE in neurodegeneration in FDD via glial-mediated mechanisms. Taken together, our findings suggest that the interaction between ApoE and ADan plays a key role in FDD pathogenesis, in addition to the known role for ApoE in amyloid plaque formation in AD.

On Scalability of FDD-Based Cell-Free Massive MIMO Framework.

Cell-free massive multiple-input multiple-output (MIMO) systems have the potential of providing joint services, including joint initial access, efficient clustering of access points (APs), and pilot allocation to user equipment (UEs) over large coverage areas with reduced interference. In cell-free massive MIMO, a large coverage area corresponds to the provision and maintenance of the scalable quality of service requirements for an infinitely large number of UEs. The research in cell-free massive MIMO is mostly focused on time division duplex mode due to the availability of channel reciprocity which aids in avoiding feedback overhead. However, the frequency division duplex (FDD) protocol still dominates the current wireless standards, and the provision of angle reciprocity aids in reducing this overhead. The challenge of providing a scalable cell-free massive MIMO system in an FDD setting is also prevalent, since computational complexity regarding signal processing tasks, such as channel estimation, precoding/combining, and power allocation, becomes prohibitively high with an increase in the number of UEs. In this work, we consider an FDD-based scalable cell-free network with angular reciprocity and a dynamic cooperation clustering approach. We have proposed scalability for our FDD cell-free and performed a comparative analysis with reference to channel estimation, power allocation, and precoding/combining techniques. We present expressions for scalable spectral efficiency, angle-based precoding/combining schemes and provide a comparison of overhead between conventional and scalable angle-based estimation as well as combining schemes. Simulations confirm that the proposed scalable cell-free network based on an FDD scheme outperforms the conventional matched filtering scheme based on scalable precoding/combining schemes. The angle-based LP-MMSE in the FDD cell-free network provides 14.3% improvement in spectral efficiency and 11.11% improvement in energy efficiency compared to the scalable MF scheme.

CSI Feedback Model Based on Multi-Source Characterization in FDD Systems.

In wireless communication, to fully utilize the spectrum and energy efficiency of the system, it is necessary to obtain the channel state information (CSI) of the link. However, in Frequency Division Duplexing (FDD) systems, CSI feedback wastes part of the spectrum resources. In order to save spectrum resources, the CSI needs to be compressed. However, many current deep-learning algorithms have complex structures and a large number of model parameters. When the computational and storage resources are limited, the large number of model parameters will decrease the accuracy of CSI feedback, which cannot meet the application requirements. In this paper, we propose a neural network-based CSI feedback model, Mix_Multi_TransNet, which considers both the spatial characteristics and temporal sequence of the channel, aiming to provide higher feedback accuracy while reducing the number of model parameters. Through experiments, it is found that Mix_Multi_TransNet achieves higher accuracy than the traditional CSI feedback network in both indoor and outdoor scenes. In the indoor scene, the NMSE gains of Mix_Multi_TransNet are 4.06 dB, 4.92 dB, 4.82 dB, and 6.47 dB for compression ratio η = 1/8, 1/16, 1/32, 1/64, respectively. In the outdoor scene, the NMSE gains of Mix_Multi_TransNet are 3.63 dB, 6.24 dB, 4.71 dB, 4.60 dB, and 2.93 dB for compression ratio η = 1/4, 1/8, 1/16, 1/32, 1/64, respectively.

Advancing Fault Detection in HVAC Systems: Unifying Gramian Angular Field and 2D Deep Convolutional Neural Networks for Enhanced Performance.

Efficiency and comfort in buildings rely on on well-functioning HVAC systems. However, system faults can compromise performance. Modern data-driven fault detection methods, considering diverse techniques, encounter challenges in understanding intricate interactions and adapting to dynamic conditions present in HVAC systems during occupancy periods. Implementing fault detection during active operation, which aligns with real-world scenarios and captures dynamic interactions and environmental changes, is considered highly valuable. To address this, utilizing the dynamic simulation system HVAC SIMulation PLUS (HVACSIM+), an HVAC fault model was developed using 194 sensor signals from each HVAC component within a single-story, four-room building. The advanced HVAC fault detection framework, leveraging simulated HVAC operational scenarios with the Gramian angular field (GAF) and two-dimensional convolutional neural networks (GAF-2DCNNs), offers a robust and proactive solution. By utilizing the GAF capacity to convert time-series sensor data into informative 2D images, integrated with 2DCNN for automated feature extraction, hidden temporal relationships within 1D signals are captured. After training on nine significant HVAC faults and normal conditions during occupancy, the effectiveness of the proposed GAF-2DCNN is evaluated through comparisons with support vector machine (SVM), random forest (RF), and hybrid RF-SVM, one-dimensional convolutional neural networks (1D-CNNs). The results demonstrates an impressive overall accuracy of 97%, accompanied by precision, recall, and F1 scores that surpass 90% for individual HVAC faults. Through the introduction of the unified approach that integrates HVACSIM+ simulated data and GAF-2DCNN, a notable enhancement in robustness and reliability for handling substantial HVAC faults is achieved.

Chemical traits of cerebral amyloid angiopathy in familial British-, Danish-, and non-Alzheimer's dementias.

Familial British dementia (FBD) and familial Danish dementia (FDD) are autosomal dominant forms of dementia caused by mutations in the integral membrane protein 2B (ITM2B, also known as BRI2) gene. Secretase processing of mutant BRI2 leads to secretion and deposition of BRI2-derived amyloidogenic peptides, ABri and ADan that resemble APP/ß-amyloid (Aß) pathology, which is characteristic of Alzheimer's disease (AD). Amyloid pathology in FBD/FDD manifests itself predominantly in the microvasculature by ABri/ADan containing cerebral amyloid angiopathy (CAA). While ABri and ADan peptide sequences differ only in a few C-terminal amino acids, CAA in FDD is characterized by co-aggregation of ADan with Aß, while in contrast no Aß deposition is observed in FBD. The fact that FDD patients display an earlier and more severe disease onset than FBD suggests a potential role of ADan and Aß co-aggregation that promotes a more rapid disease progression in FDD compared to FBD. It is therefore critical to delineate the chemical signatures of amyloid aggregation in these two vascular dementias. This in turn will increase the knowledge on the pathophysiology of these diseases and the pathogenic role of heterogenous amyloid peptide interactions and deposition, respectively. Herein, we used matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) in combination with hyperspectral, confocal microscopy based on luminescent conjugated oligothiophene probes (LCO) to delineate the structural traits and associated amyloid peptide patterns of single CAA in postmortem brain tissue of patients with FBD, FDD as well as sporadic CAA without AD (CAA+) that show pronounced CAA without parenchymal plaques. The results show that CAA in both FBD and FDD consist of N-terminally truncated- and pyroglutamate-modified amyloid peptide species (ADan and ABri), but that ADan peptides in FDD are also extensively C-terminally truncated as compared to ABri in FBD, which contributes to hydrophobicity of ADan species. Further, CAA in FDD showed co-deposition with Aß x-42 and Aß x-40 species. CAA+ vessels were structurally more mature than FDD/FBD CAA and contained significant amounts of pyroglutamated Aß. When compared with FDD, Aß in CAA+ showed more C-terminal and less N-terminally truncations. In FDD, ADan showed spatial co-localization with Aß3pE-40 and Aß3-40 but not with Aßx-42 species. This suggests an increased aggregation propensity of Aß in FDD that promotes co-aggregation of both Aß and ADan. Further, CAA maturity appears to be mainly governed by Aß content based on the significantly higher 500/580 patterns observed in CAA+ than in FDD and FBD, respectively. Together this is the first study of its kind on comprehensive delineation of Bri2 and APP-derived amyloid peptides in single vascular plaques in both FDD/FBD and sporadic CAA that provides new insight in non-AD-related vascular amyloid pathology. Cover Image for this issue: https://doi.org/10.1111/jnc.15424.

Operational and Analytical Modal Analysis of a Bridge Using Low-Cost Wireless Arduino-Based Accelerometers.

Arduino-based accelerometers are receiving wide attention from researchers to make long-term Structural Health Monitoring (SHM) feasible for structures with a low SHM budget. The current low-cost solutions found in the literature share some of the following drawbacks: (1) high noise density, (2) lack of wireless synchronization, (3) lack of automatic data acquisition and data management, and (4) lack of dedicated field tests aiming to compare mode shapes from Operational Modal Analysis (OMA) with those of a digital model. To solve these problems, a recently built short-span footbridge in Barcelona is instrumented using four Low-cost Adaptable Reliable Accelerometers (LARA). In this study, the automatization of the data acquisition and management of these low-cost solutions is studied for the first time in the literature. In addition, a digital model of the bridge under study is generated in SAP2000 using the available drawings and reported characteristics of its materials. The OMA of the bridge is calculated using Frequency Domain Decomposition (FDD) and Covariance Stochastic Subspace Identification (SSI-cov) methods. Using the Modal Assurance Criterion (MAC), the mode shapes of OMA are compared with those of the digital model. Finally, the acquired eigenfrequencies of the bridge obtained with a high-precision commercial sensor (HI-INC) showed a good agreement with those obtained with LARA.

Hybrid Random Forest and Support Vector Machine Modeling for HVAC Fault Detection and Diagnosis.

The malfunctioning of the heating, ventilating, and air conditioning (HVAC) system is considered to be one of the main challenges in modern buildings. Due to the complexity of the building management system (BMS) with operational data input from a large number of sensors used in HVAC system, the faults can be very difficult to detect in the early stage. While numerous fault detection and diagnosis (FDD) methods with the use of statistical modeling and machine learning have revealed prominent results in recent years, early detection remains a challenging task since many current approaches are unfeasible for diagnosing some HVAC faults and have accuracy performance issues. In view of this, this study presents a novel hybrid FDD approach by combining random forest (RF) and support vector machine (SVM) classifiers for the application of FDD for the HVAC system. Experimental results demonstrate that our proposed hybrid random forest-support vector machine (HRF-SVM) outperforms other methods with higher prediction accuracy (98%), despite that the fault symptoms were insignificant. Furthermore, the proposed framework can reduce the significant number of sensors required and work well with the small number of faulty training data samples available in real-world applications.

The Downlink Performance for Cell-Free Massive MIMO with Instantaneous CSI in Slowly Time-Varying Channels.

In centralized massive multiple-input multiple-output (MIMO) systems, the channel hardening phenomenon can occur, in which the channel behaves as almost fully deterministic as the number of antennas increases. Nevertheless, in a cell-free massive MIMO system, the channel is less deterministic. In this paper, we propose using instantaneous channel state information (CSI) instead of statistical CSI to obtain the power control coefficient in cell-free massive MIMO. Access points (APs) and user equipment (UE) have sufficient time to obtain instantaneous CSI in a slowly time-varying channel environment. We derive the achievable downlink rate under instantaneous CSI for frequency division duplex (FDD) cell-free massive MIMO systems and apply the results to the power control coefficients. For FDD systems, quantized channel coefficients are proposed to reduce feedback overhead. The simulation results show that the spectral efficiency performance when using instantaneous CSI is approximately three times higher than that achieved using statistical CSI.

FDD Channel Estimation via Covariance Estimation in Wideband Massive MIMO Systems.

A method for channel estimation in wideband massive MIMO systems using hybrid digital analog architectures is developed. The proposed method is useful for FDD at either sub-6 GHz or mmWave frequency bands and takes into account the beam squint effect caused by the large bandwidth of the signals. To circumvent the estimation of large channel vectors, the posed algorithm relies on the slow time variation of the channel spatial covariance matrix, thus allowing for the utilization of very short training sequences. This is possibledue to the exploitation of the channel structure. After identifying the channel covariance matrix, the channel is estimated on the basis of the recovered information. To that end, we propose a novel method that relies on estimating the tap delays and the gains as sociated with each path. As a consequence, the proposed channel estimator achieves low computational complexity and significantly reduces the training overhead. Moreover, our numerical simulations show better performance results compared to the minimum mean-squared error solution.

Internal maxillary artery bypass for the treatment of complex middle cerebral artery aneurysms.

OBJECTIVEThe rapid innovation of the endovascular armamentarium results in a decreased number of indications for a classic surgical approach. However, a middle cerebral artery (MCA) aneurysm remains the best example of one for which results have favored microsurgery over endovascular intervention. In this study, the authors aimed to evaluate the experience and efficacy regarding surgical outcomes after applying internal maxillary artery (IMA) bypass for complex MCA aneurysms (CMCAAs).METHODSAll IMA bypasses performed between January 2010 and July 2018 in a single-center, single-surgeon practice were screened.RESULTSIn total, 12 patients (9 males, 3 females) with CMCAAs managed by high-flow IMA bypass were identified. The mean size of CMCAAs was 23.7 mm (range 10-37 mm), and the patients had a mean age of 31.7 years (range 14-56 years). The aneurysms were proximally occluded in 8 cases, completely trapped in 3 cases, and completely resected in 1 case. The radial artery was used as the graft vessel in all cases. At discharge, the graft patency rate was 83.3% (n = 10), and all aneurysms were completely eliminated (83.3%, n = 10) or greatly diminished (16.7%, n = 2) from the circulation. Postoperative ischemia was detected in 2 patients as a result of graft occlusion, and 1 patient presenting with subarachnoid hemorrhage achieved improved modified Rankin Scale scores compared to the preoperative status but retained some neurological deficits. Therefore, neurological assessment at discharge showed that 9 of the 12 patients experienced unremarkable outcomes. The mean interval time from bypass to angiographic and clinical follow-up was 28.7 months (range 2-74 months) and 53.1 months (range 19-82 months), respectively. Although 2 grafts remained occluded, all aneurysms were isolated from the circulation, and no patient had an unfavorable outcome.CONCLUSIONSThe satisfactory result in the present study demonstrated that IMA bypass is a promising method for the treatment of CMCAAs and should be maintained in the neurosurgical armamentarium. However, cases with intraoperative radical resection or inappropriate bypass recipient selection such as aneurysmal wall should be meticulously chosen with respect to the subtype of MCA aneurysm.

Determination of Effective Mode Selection for Ensuring Spectrum Efficiency with Massive MIMO in IoT Systems.

Wireless Sensor Networks (WSNs) based on Internet of Things (IoT) applications are increasing day by day. These applications include healthcare, infrastructure monitoring, smart homes, wearable devices and smart cars. However, considering the fact that many different application areas will emerge in next generation wireless communication systems, efficient use of frequency spectrum is important. Because the whole frequency spectrum is now very crowded, it is important to ensure maximum spectrum efficiency for effective WSNs based on IoT. This study sought to determine which mode more effectively achieves spectrum efficiency for the performance of effective IoT systems under given conditions with respect to the length of the pilot sequence, Time Division Duplexing (TDD) or Frequency Division Duplexing (FDD). The results were obtained by Monte Carlo simulations. To the best of our knowledge, a study of effective mode selection analysis for spectrum efficiency in IoT based systems has not been available in the literature yet. The results of this study are useful for determining the appropriate design conditions for WSNs based on IoT.

A Novel Chiller Sensors Fault Diagnosis Method Based on Virtual Sensors.

Sensor fault detection and diagnosis (FDD) has great significance for ensuring the energy saving and normal operation of the air conditioning system. Chiller systems serving as an important part of central air conditioning systems are the major energy consumer in commercial and industrial buildings. In order to ensure the normal operation of the chiller system, virtual sensors have been proposed to detect and diagnose sensor faults. However, the performance of virtual sensors could be easily impacted by abnormal data. To solve this problem, virtual sensors combined with the maximal information coefficient (MIC) and a long short-term memory (LSTM) network is proposed for chiller sensor fault diagnosis. Firstly, MIC, which has the ability to quantify the degree of relevance in a data set, is applied to examine all potentially interesting relationships between sensors. Subsequently, sensors with high correlation are divided into several groups by the grouping thresholds. Two virtual sensors, which are constructed in each group by LSTM with different input sensors and corresponding to the same physical sensor, could have the ability to predict the value of physical sensors. High correlation sensors in each group improve the fitting effect of virtual sensors. Finally, sensor faults can be diagnosed by the absolute deviation which is generated by comparing the virtual sensors' output with the actual value measured from the air-cooled chiller. The performance of the proposed method is evaluated by using a real data set. Experimental results indicate that virtual sensors can be well constructed and the proposed method achieves a significant performance along with a low false alarm rate.

Clinical presentation of rapidly progressive familial Danish dementia.

Familial Danish dementia (FDD) is a rare, autosomal dominant neurodegenerative disorder characterized by progressive hearing loss, cataracts, progressive ataxia, and dementia. While multiple pathophysiological studies exist in the literature, clinical case presentations are currently limited. We present a case of young-onset dementia in a 47-year-old patient with Danish heritage who was subsequently diagnosed FDD through genetic testing. Cognitive impairment was his initial symptom, followed by Parkinsonian symptoms, and mood disturbances. The patient experienced rapid decline over only 19 months. Increased awareness and understanding of familial forms of dementia (i.e., FDD) can contribute to an enhanced provision of care for patients with such conditions.

Data Decomposition Techniques with Multi-Scale Permutation Entropy Calculations for Bearing Fault Diagnosis.

This paper presents the local mean decomposition (LMD) integrated with multi-scale permutation entropy (MPE), also known as LMD-MPE, to investigate the rolling element bearing (REB) fault diagnosis from measured vibration signals. First, the LMD decomposed the vibration data or acceleration measurement into separate product functions that are composed of both amplitude and frequency modulation. MPE then calculated the statistical permutation entropy from the product functions to extract the nonlinear features to assess and classify the condition of the healthy and damaged REB system. The comparative experimental results of the conventional LMD-based multi-scale entropy and MPE were presented to verify the authenticity of the proposed technique. The study found that LMD-MPE’s integrated approach provides reliable, damage-sensitive features when analyzing the bearing condition. The results of REB experimental datasets show that the proposed approach yields more vigorous outcomes than existing methods.

Complications associated with the use of flow-diverting devices for cerebral aneurysms: a systematic review and meta-analysis.

OBJECTIVE The objective of this study was to review the literature on the use of flow-diverting devices (FDDs) to treat intracranial aneurysms (IAs) and to investigate the safety and complications related to FDD treatment for IAs by performing a meta-analysis of published studies. METHODS A systematic electronic database search was conducted using the Springer, EBSCO, PubMed, Medline, and Cochrane databases on all accessible articles published up to January 2016, with no restriction on the publication year. Abstracts, full-text manuscripts, and the reference lists of retrieved articles were analyzed. Random-effects meta-analysis was used to pool the complication rates across studies. RESULTS Sixty studies were included, which involved retrospectively collected data on 3125 patients. The use of FDDs was associated with an overall complication rate of 17.0% (95% confidence interval [CI] 13.6%-20.5%) and a low mortality rate of 2.8% (95% CI 1.2%-4.4%). The neurological morbidity rate was 4.5% (95% CI 3.2%-5.8%). No significant difference in the complication or mortality rate was observed between 2 commonly used devices (the Pipeline embolization device and the Silk flow-diverter device). A significantly higher overall complication rate was found in the case of ruptured IAs than in unruptured IA (odds ratio 2.3, 95% CI 1.2-4.3). CONCLUSIONS The use of FDDs in the treatment of IAs yielded satisfactory results with regard to complications and the mortality rate. The risk of complications should be considered when deciding on treatment with FDDs. Further studies on the mechanism underlying the occurrence of adverse events are required.

Postprocedural, midterm, and long-term results of cerebral aneurysms treated with flow-diverter devices: 7-year experience at a single center.

OBJECTIVE Flow diversion has emerged as a viable treatment option for selected intracranial aneurysms and recently has been gaining traction. The aim of this study was to evaluate the safety and effectiveness of flow-diverter devices (FDDs) over a long-term follow-up period. METHODS The authors retrospectively reviewed all cerebral aneurysm cases that had been admitted to the Division of Neurosurgery of the Università degli Studi di Napoli between November 2008 and November 2015 and treated with an FDD. The records of 60 patients (48 females and 12 males) harboring 69 cerebral aneurysms were analyzed. The study end points were angiographic evidence of complete aneurysm occlusion, recanalization rate, occlusion of the parent artery, and clinical and radiological evidence of brain ischemia. The occlusion rate was evaluated according to the O'Kelly-Marotta (OKM) Scale for flow diversion, based on the degree of filling (A, total filling; B, subtotal filling; C, entry remnant; D, no filling). Postprocedural, midterm, and long-term results were strictly analyzed. RESULTS Complete occlusion (OKM D) was achieved in 63 (91%) of 69 aneurysms, partial occlusion (OKM C) in 4 (6%), occlusion of the parent artery in 2 (3%). Intraprocedural technical complications occurred in 3 patients (5%). Postprocedural complications occurred in 6 patients (10%), without neurological deficits. At the 12-month follow-up, 3 patients (5%) experienced asymptomatic cerebral infarction. No further complications were observed at later follow-up evaluations (> 24 months). There were no reports of any delayed aneurysm rupture, subarachnoid or intraparenchymal hemorrhage, ischemic complications, or procedure- or device-related deaths. CONCLUSIONS Endovascular treatment with an FDD is a safe treatment for unruptured cerebral aneurysms, resulting in a high rate of occlusion. In the present study, the authors observed effective and stable aneurysm occlusion, even at the long-term follow-up. Data in this study also suggest that ischemic complications can occur at a later stage, particularly at 12-18 months. On the other hand, no other ischemic or hemorrhagic complications occurred beyond 24 months.

Cable failure tolerant control and planning in a planar reconfigurable cable driven parallel robot.

The addition of geometric reconfigurability in a cable driven parallel robot (CDPR) introduces kinematic redundancies which can be exploited for manipulating structural and mechanical properties of the robot through redundancy resolution. In the event of a cable failure, a reconfigurable CDPR (rCDPR) can also realign its geometric arrangement to overcome the effects of cable failure and recover the original expected trajectory and complete the trajectory tracking task. In this paper we discuss a fault tolerant control (FTC) framework that relies on an Interactive Multiple Model (IMM) adaptive estimation filter for simultaneous fault detection and diagnosis (FDD) and task recovery. The redundancy resolution scheme for the kinematically redundant CDPR takes into account singularity avoidance, manipulability and wrench quality maximization during trajectory tracking. We further introduce a trajectory tracking methodology that enables the automatic task recovery algorithm to consistently return to the point of failure. This is particularly useful for applications where the planned trajectory is of greater importance than the goal positions, such as painting, welding or 3D printing applications. The proposed control framework is validated in simulation on a planar rCDPR with elastic cables and parameter uncertainties to introduce modeled and unmodeled dynamics in the system as it tracks a complete trajectory despite the occurrence of multiple cable failures. As cables fail one by one, the robot topology changes from an over-constrained to a fully constrained and then an under-constrained CDPR. The framework is applied with a constant-velocity kinematic feedforward controller which has the advantage of generating steady-state inputs despite dynamic oscillations during cable failures, as well as a Linear Quadratic Regulator (LQR) feedback controller to locally dampen these oscillations.

WHO Functioning and Disability Disaggregation (FDD11) tool: a reliable approach for disaggregating data by disability.

BACKGROUND: There is a global scarcity of good quality disability data, which has contributed to a lack of political will to address the challenges that persons with disabilities face. The current paper proposes a way forward to overcome this gap by demonstrating the psychometric properties of the World Health Organization Functioning and Disability Disaggregation Tool (FDD11) - a brief disability disaggregation instrument that countries can use. RESULTS: The study demonstrated that FDD11 is a valid and reliable tool. Unidimensionality of the scale produced by each calibration was supported by the factor analysis performed. The analysis indicated good fit of the items, and targeting of the items was deemed to be sufficient. The person separation index was 0.82, indicating good reliability of the final scale. CONCLUSION: FDD11 provides a good opportunity to researchers and governments to capture good quality disability data and to disaggregate existing data by disability. The tool can facilitate low- and middle-income countries in their efforts to develop evidenced-based policies to address any barriers faced by persons with disabilities, to monitor the implementation of the Convention on the Rights of Persons with Disabilities and the Sustainable Development Goals, and to take stock of the challenges that still remain.

Robust adaptive fault detection and diagnosis observer design for a class of nonlinear systems with uncertainty and unknown time-varying internal delay.

This paper introduces a novel robust adaptive fault detection and diagnosis (FDD) observer design approach for a class of nonlinear systems with parametric uncertainty, unknown system fault and time-varying internal delays. The conditions for the existence of the proposed FDD are obtained based on the well-known Linear Matrix Inequalities (LMI) technique. Using Lyapunov stability theory, the adaptation laws for updating the observer weights and unknown faults estimation are derived based on which the convergence of the state estimation error to zero and asymptotic stability of the error dynamics are proven. Toward this, a new structural algorithm for FDD observer design is also derived based on LMIs. The performance of the proposed method is also investigated while applying to some industrial systems. Simulation results illustrate superior performance of the proposed method for the systems subject to time-varying unknown delays on states, uncertainty in nonlinear system modeling and unknown system faults.

Sticking - pulling strategy for assessment of combined medicine for management of tough symptoms in COVID-19 Pandemic using different windows of spectrophotometric Platform-Counterfeit products' detection.

This study presents a comprehensive comparative study of different green spectrophotometric approaches without any physical separation on processing a ternary mixture of Aceclofenac (ACE), Paracetamol (PAR) and Rabeprazole (RAB) in combined medicine for managing tough symptoms in the COVID-19 Pandemic. The different univariate complementary resolutions according to the response used for the assay of the cited drugs after applying the processing steps were implemented using successive in-silico sample enrichment for resolving the ternary mixture via different windows of spectrophotometric platform using sticking - pulling strategy (SPS). Window I; based on manipulation of the data of zero order absorption spectrum of the mixture using novel Extended absorbance difference (EAD) and Absorbance difference (AD) methods coupled with corresponding spectrum subtraction method (SS). Window III; based on manipulation of the data of ratio spectra via Constant value coupled with constant subtraction (CV-CS) and novel Induced dual amplitude difference (IDAD) method coupled with corresponding spectrum subtraction method (SS). Finally, window IV; based on manipulation of the data of derivative of the ratio spectrum of the mixture via novel Factorized derivative ratio null contribution (FDD-NC) and Factorized unlimited derivative ratio (FUDD) methods coupled with corresponding spectrum subtraction method (SS). Synthetic mixtures and commercial medicine were constructively analyzed using the proposed methods while maintaining calibration graphs to be linear over ranges; 4.0-40.0 µg/mL for ACE, 2.0-14.0 µg/mL for PAR and 4.0-30.0 µg/mL for RAB. Moreover, methods' validation was confirmed via performing exhaustive statistical treatment of the experimental findings. The proposed methodologies can be used for the routine analysis of the cited drugs in quality control laboratories. Additionally, Spectral Similarity Index (SSI) was calculated to detect counterfeit products and methods' greenness profile was finally guaranteed through analytical greenness (AGREE) metric assessment tool.