Evaluasi Pelayanan Sarana Dan Prasarana Proteksi Kebakaran Pada Permukiman Perkotaan

The incidence of fires in the city of Sidoarjo in the last 5 years has increased. The main causes of the majority of fires were settlement activities and weeds. The aim of this study was to evaluate the level of fire protection facilities and infrastructure services in the urban settlements of Sidoarjo City based on Minister of Public Works Regulation No.20 / PRT / M / 2009 concerning Technical Guidelines for Fire Protection Management. The study area consisted of Sidoarjo District, Candi District and Buduran District. The research approach used is descriptive evaluative research. The results showed that the level of fire protection facilities and infrastructure services in the city of Sidoarjo was still 48%, it needed an increase in the aspects of the completeness of the Fire Extinguishers building, the availability of fire stations and hydrants as well as increasing community participation

Secondary cytogenetic abnormalities in core-binding factor AML harboring inv(16) vs t(8;21)

Patients with core-binding factor (CBF) acute myeloid leukemia (AML), caused by either t(8; 21)(q22;q22) or inv(16)(p13q22)/t(16;16)(p13;q22), have higher complete remission rates and longer survival than patients with other subtypes of AML. However, similar to 40% of patients relapse, and the literature suggests that patients with inv(16) fare differently from those with t(8;21). We retrospectively analyzed 537 patients with CBF-AML, focusing on additional cytogenetic aberrations to examine their impact on clinical outcomes. Trisomies of chromosomes 8, 21, or 22 were significantly more common in patients with inv(16)/t(16;16): 16% vs 7%, 6% vs 0%, and 17% vs 0%, respectively. In contrast, del(9q) and loss of a sex chromosome were more frequent in patients with t(8;21): 15% vs 0.4% for del(9q), 37% vs 0% for loss of X in females, and 44% vs 5% for loss of Y in males. Hyperdiploidy was more frequent in patients with inv(16) (25% vs 9%, whereas hypodiploidy was more frequent in patients with t(8;21) (37% vs 3%. In multivariable analyses (adjusted for age, white blood counts at diagnosis, and KIT mutation status), trisomy 8 was associated with improved overall survival (OS) in inv(16), whereas the presence of other chromosomal abnormalities (not trisomy 8) was associated with decreased OS. In patients with t(8;21), hypodiploidy was associated with improved disease-free survival; hyperdiploidy and del(9q) were associated with improved OS. KIT mutation (either positive or not tested, compared with negative) conferred poor prognoses in univariate analysis only in patients with t(8;21)

Core-binding factor acute myeloid leukemia with t(8;21) Risk factors and a novel scoring system (I-CBFit)

Background: Although the prognosis of core-binding factor (CBF) acute myeloid leukemia (AML) is better than other subtypes of AML, 30% of patients still relapse and may require allogeneic hematopoietic cell transplantation (alloHCT). However, there is no validated widely accepted scoring system to predict patient subsets with higher risk of relapse. Methods: Eleven centers in the US and Europe evaluated 247 patients with t(8;21) (q22;q22). Results: Complete remission (CR) rate was high (92.7%), yet relapse occurred in 27.1% of patients. A total of 24.7% of patients received alloHCT. The median diseasefree (DFS) and overall (OS) survival were 20.8 and 31.2 months, respectively. Age, KIT D816V mutated (11.3%) or nontested (36.4%) compared with KIT D816V wild type (52.5%), high white blood cell counts (WBC), and pseudodiploidy compared with hyper- or hypodiploidy were included in a scoring system (named I-CBFit). DFS rate at 2 years was 76% for patients with a low-risk I-CBFit score compared with 36% for those with a high-risk I-CBFit score (P <0.0001). Low- vs high-risk OS at 2 years was 89% vs 51% (P <0.0001). Conclusions: I-CBFit composed of readily available risk factors can be useful to tailor the therapy of patients, especially for whom alloHCT is not need in CR1 (ie, patients with a low-risk score)

Cuidados biomédicos de saúde em Angola e na Companhia de Diamantes de Angola, c. 1910-1970

Pretende-se caracterizar a prestação de cuidados biomédicos em Angola durante a atividade da Companhia de Diamantes de Angola. Uma análise comparativa de políticas e práticas de saúde pública de vários atores coloniais, como os serviços de saúde da Companhia, sua congénere do Estado e outras empresas coloniais, revelará diferenças de investimento na saúde, isto é, instalações e pessoal de saúde, e tratamentos. Este escrutínio bem como as condições de vida iluminarão o carácter idiossincrático e central dos serviços de saúde da Companhia em termos de morbimortalidade em Angola, e a centralidade destes para as representações de um império cuidador

MATLAB software for recursive identification and scaling using a structured nonlinear black-box model : Revision 6

This report is intended as a users manual for a package of MATLAB scripts and functions, developed for recursive prediction error identification of nonlinear state space systems and nonlinear static systems. The core of the package is an implementation of related output error identification and scaling algorithms. The algorithms are based on a continuous time, structured black box state space model of a nonlinear system. Furthermore, to initialize the algorithm an initiation scheme based on Kalman filter theory is included. The purpose of the initialization algorithm is to find initial parameters for the prediction error algorithm, and thus reducing the risk of convergence to local false minima. An RPEM algorithm for recursive identification of nonlinear static systems, that re-uses the parameterization of the nonlinear ODE model, is also included in the software package. In this version of the software a new discretization of the continuous time model based on the midpoint integration algorithm is added. The software can only be run off-line, i.e. no true real time operation is possible. The algorithms are however implemented so that true on-line operation can be obtained by extraction of the main algorithmic loop. The user must then provide the real time environment. The software package contains scripts and functions that allow the user to either input live measurements or to generate test data by simulation. The scripts and functions for the setup and execution of the identification algorithms are somewhat more general than what is described in the references. There is e.g. support for automatic re-initiation of the algorithms using the parameters obtained at the end of a previous identification run. This allows for multiple runs through a set of data, something that is useful for data sets that are too short to allow convergence in a single run. The re-initiation step also allows the user to modify the degrees of the polynomial model structure and to specify terms that are to be excluded from the model. This makes it possible to iteratively re-fine the estimated model using multiple runs. The functionality for display of results include scripts for plotting of data, parameters, prediction errors, eigenvalues and the condition number of the Hessian. The estimated model obtained at the end of a run can be simulated and the model output plotted, alone or together with the data used for identification. Model validation is supported by two methods apart from the display functionality. First, a calculation of the RPEM loss function can be performed, using parameters obtained at the end of an identification run. Secondly, the accuracy as a function of the output signal amplitude can be assessed

MATLAB software for recursive identification and scaling using a structured nonlinear black-box model : Revision 6

This report is intended as a users manual for a package of MATLAB scripts and functions, developed for recursive prediction error identification of nonlinear state space systems and nonlinear static systems. The core of the package is an implementation of related output error identification and scaling algorithms. The algorithms are based on a continuous time, structured black box state space model of a nonlinear system. Furthermore, to initialize the algorithm an initiation scheme based on Kalman filter theory is included. The purpose of the initialization algorithm is to find initial parameters for the prediction error algorithm, and thus reducing the risk of convergence to local false minima. An RPEM algorithm for recursive identification of nonlinear static systems, that re-uses the parameterization of the nonlinear ODE model, is also included in the software package. In this version of the software a new discretization of the continuous time model based on the midpoint integration algorithm is added. The software can only be run off-line, i.e. no true real time operation is possible. The algorithms are however implemented so that true on-line operation can be obtained by extraction of the main algorithmic loop. The user must then provide the real time environment. The software package contains scripts and functions that allow the user to either input live measurements or to generate test data by simulation. The scripts and functions for the setup and execution of the identification algorithms are somewhat more general than what is described in the references. There is e.g. support for automatic re-initiation of the algorithms using the parameters obtained at the end of a previous identification run. This allows for multiple runs through a set of data, something that is useful for data sets that are too short to allow convergence in a single run. The re-initiation step also allows the user to modify the degrees of the polynomial model structure and to specify terms that are to be excluded from the model. This makes it possible to iteratively re-fine the estimated model using multiple runs. The functionality for display of results include scripts for plotting of data, parameters, prediction errors, eigenvalues and the condition number of the Hessian. The estimated model obtained at the end of a run can be simulated and the model output plotted, alone or together with the data used for identification. Model validation is supported by two methods apart from the display functionality. First, a calculation of the RPEM loss function can be performed, using parameters obtained at the end of an identification run. Secondly, the accuracy as a function of the output signal amplitude can be assessed

MATLAB Software for Recursive Identification and Scaling Using a Structured Nonlinear Black-box Model : Revision 5

This report is intended as a users manual for a package of MATLAB™ scripts and functions, developed for recursive prediction error identification of nonlinear state space systems and nonlinear static systems. The core of the package is an implementation of an output error identification and scaling algorithm. The algorithm is based on a continuous time, structured black box state space model of a nonlinear system. Furthermore, to initialize the algorithm an algorithm based on Kalman filter theory is included. The purpose of the initialization algorithm is to find initial parameters for the prediction error algorithm, and thus reducing the risk of convergence to local false minima. An RPEM algorithm for recursive identification of nonlinear static systems, that re-uses the parameterization of the nonlinear ODE model, is also included in the software package. In this version of the software the discretization of the continuous time model is based on the midpoint integration algorithm. The software can only be run off-line, i.e. no true real time operation is possible. The algorithms are however implemented so that true on-line operation can be obtained by extraction of the main algorithmic loop. The user must then provide the real time environment. The software package contains scripts and functions that allow the user to either input live measurements or to generate test data by simulation. The scripts and functions for the setup and execution of the identification algorithms are somewhat more general than what is described in the references. There is e.g. support for automatic re-initiation of the algorithms using the parameters obtained at the end of a previous identification run. This allows for multiple runs through a set of data, something that is useful for data sets that are too short to allow convergence in a single run. The re-initiation step also allows the user to modify the degrees of the polynomial model structure and to specify terms that are to be excluded from the model. This makes it possible to iteratively re-fine the estimated model using multiple runs. The functionality for display of results include scripts for plotting of data, parameters, prediction errors, eigenvalues and the condition number of the Hessian. The estimated model obtained at the end of a run can be simulated and the model output plotted, alone or together with the data used for identification. Model validation is supported by two methods apart from the display functionality. First, a calculation of the RPEM loss function can be performed, using parameters obtained at the end of an identification run. Secondly, the accuracy as a function of the output signal amplitude can be assessed

MATLAB Software for Recursive Identification and Scaling Using a Structured Nonlinear Black-box Model : Revision 5

This report is intended as a users manual for a package of MATLAB™ scripts and functions, developed for recursive prediction error identification of nonlinear state space systems and nonlinear static systems. The core of the package is an implementation of an output error identification and scaling algorithm. The algorithm is based on a continuous time, structured black box state space model of a nonlinear system. Furthermore, to initialize the algorithm an algorithm based on Kalman filter theory is included. The purpose of the initialization algorithm is to find initial parameters for the prediction error algorithm, and thus reducing the risk of convergence to local false minima. An RPEM algorithm for recursive identification of nonlinear static systems, that re-uses the parameterization of the nonlinear ODE model, is also included in the software package. In this version of the software the discretization of the continuous time model is based on the midpoint integration algorithm. The software can only be run off-line, i.e. no true real time operation is possible. The algorithms are however implemented so that true on-line operation can be obtained by extraction of the main algorithmic loop. The user must then provide the real time environment. The software package contains scripts and functions that allow the user to either input live measurements or to generate test data by simulation. The scripts and functions for the setup and execution of the identification algorithms are somewhat more general than what is described in the references. There is e.g. support for automatic re-initiation of the algorithms using the parameters obtained at the end of a previous identification run. This allows for multiple runs through a set of data, something that is useful for data sets that are too short to allow convergence in a single run. The re-initiation step also allows the user to modify the degrees of the polynomial model structure and to specify terms that are to be excluded from the model. This makes it possible to iteratively re-fine the estimated model using multiple runs. The functionality for display of results include scripts for plotting of data, parameters, prediction errors, eigenvalues and the condition number of the Hessian. The estimated model obtained at the end of a run can be simulated and the model output plotted, alone or together with the data used for identification. Model validation is supported by two methods apart from the display functionality. First, a calculation of the RPEM loss function can be performed, using parameters obtained at the end of an identification run. Secondly, the accuracy as a function of the output signal amplitude can be assessed

Timing of LGM and deglaciation in the Southern Swiss Alps

Detailed mapping of Quaternary formations in Southern Switzerland (Mendrisiotto and neighbouring regions in Italy) and a compilation of radiocarbon dating make it possible to reconstruct the geometry and chronology of the Last Glacial Maximum (LGM) in the Southern Swiss Alps. A detailed chronostratigraphy of the main recessional stadials during the Lateglacial and beginning of the Holocene can also be obtained. The defined glacial stadials were correlated with the NGRIP Greenland isotopic record. An analysis of the calibrated maximum and minimum ages of the LGM allows this episode to be limited to between 28,500 and 22,900 cal BP (24,500–19,000 14C BP). The LGM advance could then tentatively been correlated with the GS-3, between 27,400 and 22,700 cal BP. For the Pleniglacial and the Pleniglacial/Lateglacial transition, the first recessional phases after the LGM were positioned between ca. 22,500 and 21,000 cal BP, and may correspond to the two first cold events of the GS-2c. The first Lateglacial stadial was the Melide stadial, and may match with one of the two cold events at 20,450 or 19,850 cal BP in NGRIP stratigraphy. In the Leventina and Bedretto Valleys (Ticino glacier), five glacial stadials have been proven for the Oldest Dryas (Biasca, Faido, Airolo, Fontana and All’Acqua stadials), two for the Younger Dryas (Maniò and Alpe di Cruina stadials) and one (Val Corno stadial) corresponding to Greenland Holocene event GH-11.2.La cartographie géologique minutieuse des terrains du Quaternaire dans la Suisse méridionale (Mendrisiotto et régions italiennes environnantes) et la compilation de plusieurs datations radiocarbone ont permis de reconstituer la géométrie et la chronologie du Dernier Maximum Glaciaire (DMG/LGM) dans le Sud des Alpes Suisses. Ces résultats ont aussi permis d’obtenir une chronostratigraphie détaillée des principaux stades glaciaires qui ont marqué le Tardiglaciaire et le début de l’Holocène. Les stades glaciaires définis ont été corrélés aux évènements de l’enregistrement isotopique du sondage NGRIP au Groënland. L’analyse des âges radiocarbone calibrés maximaux et minimaux du DMG permet de placer cet épisode entre 28500 et 22900 cal BP (24500-19000 14C BP). L’avancée glaciaire attribuée au DMG a donc été corrélée de manière hypothétique avec le GS-3, compris entre 27400 et 22700 cal BP. Pour le Pléniglaciaire et la transition Pléniglaciaire/Tardiglaciaire, les premières phases de régression glaciaire après le DMG ont été placées entre ca. 22500 et 21000 cal BP, et peuvent correspondre aux deux premiers épisodes froids du GS-2c. Le premier stade du Tardiglaciaire a été le stade de Melide, qui peut correspondre avec l’un des deux épisodes froids de 20450 ou 19850 cal BP. Par la suite, dans les vallées de Leventina et de Bedretto (glacier du Ticino), cinq stades glaciaires ont été mis en évidence pour le Dryas ancien (stades de Biasca, Faido, Airolo, Fontana et All’Acqua), deux pour le Dryas récent (Maniò et Alpe di Cruina) et un (Val Corno), en correspondance avec l’épisode froid holocène du Groënland GH-11.2