Analytical Capacity Research Articles

Policy Capacity in The Policy of Structuring General Functional Positions in The Government Environment

This study aims to analyze the policy capacity in the employee management policy in the General Functional Class in the Government of Bangkalan Regency. The problem is that the availability of position classes has not been accommodated in the Regent Regulation Number 75 of 2020 for several job classes. This raises problems such as placing employees in a lower position class than they should be, several job classes are still not fulfilled, causing jealousy between employees. This study uses the theory of policy capacity by Xun Wu, Ramesh, and Howlett with 3 (three) indicators, namely analytical capacity, operational capacity and political capacity at each level, both individual, organizational and system.
 The research was conducted using a qualitative research method with a descriptive type to dig deeper and detailed information. The location of the research was carried out in the Organizational Section of the Regional Secretariat of Bangkalan Regency. Sources of information or informants that are relevant to the topic in this study are from the Organizational Section, the Staffing and Human Resources Development Agency, the Education Office, the Environment Service. The use of data in this study comes from primary and secondary data collected through interviews, observation and the use of other supporting documents.

Moral Economy: The Afterlife of a Nebulous Concept

Since the re-purposing of the concept of the moral economy by the British historian, E.P. Thompson in the late 1960s, scholars from a variety of disciplines in social sciences and humanities have attempted to apply it as a tool for empirical analysis. As a migratory concept, the meaning of 'moral economy' has shifted from theology to philosophy to anthropology and history. Scholars of religions and historians of religion, however, have shown a reluctance in deploying the concept in their field of study. A flexible and vintage concept such as the moral economy may seem to be an oxymoron when applied to the study of religion and religious reforms. Its utility, however, is demonstrated by a collection of four critical articles in this special issue of this journal to explore wide-ranging empirical materials and contexts. These include the contemporary analysis of religious morality and regulation in Northern Nigeria, the entanglements of Muslim-owned restaurants and Islamic morality in Mumbai (India), Zulu ethnic nationality and morality in the Nazareth Baptist Church in KwaZulu-Natal (South Africa), and finally, the pre-modern theoretical and philosophical reflections of the 14th-century Tunisian Muslim philosopher, Abd al-Rahman Ibn Khaldun. In these diverse scenarios and contexts, the moral economy concept illustrates its theoretical and analytical capacity and potential in the field of the study of religions.

Cauchy transform and uniform approximation by polynomial modules

For a compact subset K of the complex plane C, let C(K) denote the algebra of continuous functions on K. For an open subset U⊂K, let A(K,U)⊂C(K) be the algebra of functions that are analytic in U. We show that there exists ϕ∈A(K,U) so that each f∈A(K,U) can uniformly be approximated by {pn+qnϕ} on K, where pn and qn are analytic polynomials in z. In particular, ϕ can be chosen as a Cauchy transform of a finite positive measure η compactly supported in C∖U. Recent developments of analytic capacity and Cauchy transform provide us useful tools in our proofs.

Chemical composition and thermal properties of commercial essential oils and their antimicrobial and antioxidant activities

This study aimed to chemically characterize commercial Essentials oils and determine their thermal properties and their antimicrobial and antioxidant activities. Essential oils extracted from leaves of Rosemary, lemongrass, cloves, orange, Tahiti lemon and thyme were studied, with chemical characterization by gas chromatography/mass spectrometry, thermoanalytical characterization and antimicrobial (microdilution) and antioxidant (ferric reduction activities power - FRAP). The chemistry of the essential oils composition was consistent with literature data and justified their thermal behavior. Comparing the thermal stability between the studied essential oils results revealed the optimization of the da Tonset = 106.6to clove`s essential oils. The results of differentiated antimicrobial and antioxidant activity justified by factors that influence the biological functions of Essentials oils, such as the origin of the plant (climate conditions), forms of cultivation and harvesting of the raw material, parameters and extraction method. Thus, it emphasizes the importance of a Brazilian production of essentials to take advantage of the technological and analytical capacity of national research centers, to provide the market with traceable products with certified identity.

Hungarian, lazy, and biased: the role of analytic thinking and partisanship in fake news discernment on a Hungarian representative sample

“Why do people believe blatantly inaccurate news headlines? Do we use our reasoning abilities to convince ourselves that statements that align with our ideology are true, or does reasoning allow us to effectively differentiate fake from real regardless of political ideology?” These were the questions of Pennycook and Rand (2019), and they are more than actual three years later in Eastern Europe (especially in Hungary) in the light of the rise of populism, and the ongoing war in Ukraine – with the flood of disinformation that follows. In this study, using a representative Hungarian sample (N = 991) we wanted to answer the same questions—moving one step forward and investigating alternative models. We aimed to extend the original research with the examination of digital literacy and source salience on media truth discernment. Most of the observations of Pennycook and Rand were confirmed: people with higher analytic thinking were better at discerning disinformation. However, the results are in line with the synergistic integrative model as partisanship interacted with cognitive reflection: anti-government voters used their analytic capacities to question both concordant and discordant fake news more than pro-government voters. Furthermore, digital literacy increased detection, but source salience did not matter when perceiving disinformation.

Data Sharing in Disruptive Technologies: Lessons from Adoption of Autonomous Systems in Singapore

Autonomous systems have been a key segment of disruptive technologies for which data are constantly collected, processed, and shared to enable their operations. The internet of things facilitates the storage and transmission of data and data sharing is vital to power their development. However, privacy, cybersecurity, and trust issues have ramifications that form distinct and unforeseen barriers to sharing data. This paper identifies six types of barriers to data sharing (technical, motivational, economic, political, legal, and ethical), examines strategies to overcome these barriers in different autonomous systems, and proposes recommendations to address them. We traced the steps the Singapore government has taken through regulations and frameworks for autonomous systems to overcome barriers to data sharing. The results suggest specific strategies for autonomous systems as well as generic strategies that apply to a broader set of disruptive technologies. To address technical barriers, data sharing within regulatory sandboxes should be promoted. Promoting public-private collaborations will help in overcoming motivational barriers. Resources and analytical capacity must be ramped up to overcome economic barriers. Advancing comprehensive data sharing guidelines and discretionary privacy laws will help overcome political and legal barriers. Further, enforcement of ethical analysis is necessary for overcoming ethical barriers in data sharing. Insights gained from this study will have implications for other jurisdictions keen to maximize data sharing to increase the potential of disruptive technologies such as autonomous systems in solving urban problems.

Enhancing critical thinking to foster students' analytical capacity in academic writing

Enhancing critical thinking to foster students' analytical capacity in academic writing

Development of a social and environmental determinants of health informatics maturity model.

Integrating social and environmental determinants of health (SEDoH) into enterprise-wide clinical workflows and decision-making is one of the most important and challenging aspects of improving health equity. We engaged domain experts to develop a SEDoH informatics maturity model (SIMM) to help guide organizations to address technical, operational, and policy gaps. We established a core expert group consisting of developers, informaticists, and subject matter experts to identify different SIMM domains and define maturity levels. The candidate model (v0.9) was evaluated by 15 informaticists at a Center for Data to Health community meeting. After incorporating feedback, a second evaluation round for v1.0 collected feedback and self-assessments from 35 respondents from the National COVID Cohort Collaborative, the Center for Leading Innovation and Collaboration's Informatics Enterprise Committee, and a publicly available online self-assessment tool. We developed a SIMM comprising seven maturity levels across five domains: data collection policies, data collection methods and technologies, technology platforms for analysis and visualization, analytics capacity, and operational and strategic impact. The evaluation demonstrated relatively high maturity in analytics and technological capacity, but more moderate maturity in operational and strategic impact among academic medical centers. Changes made to the tool in between rounds improved its ability to discriminate between intermediate maturity levels. The SIMM can help organizations identify current gaps and next steps in improving SEDoH informatics. Improving the collection and use of SEDoH data is one important component of addressing health inequities.

The role of artificial intelligence in supply chain analytics during the pandemic

The global supply networks have been disrupted and weak connections exposed to an extent that few people have ever seen in their lifetime due to the COVID-19 epidemic. As a result of the severity of the crisis, every country and industry is feeling the effects, and the massive shifts in demand and supply that have happened throughout the epidemic are easily distinguishable from the effects of previous crises. We looked into the adaptability of alliance management and AI-driven supply chain analytics in the context of an ever-changing external environment. We examined four hypotheses in this area using survey data from the American auto components manufacturing industry. To do the analysis, we used Smart PLS. Alliance management capabilities, mediated by AI-powered supply chain analytics capacity, have been found to increase an organization's operational and financial performance. We also discovered, with environmental dynamics as a moderating factor, that alliance management capability has a substantial impact on AI-powered supply chain analytics capabilities. Based on our findings, we have a deep appreciation for the interplay between dynamic capacities and the relational view of organization. Finally, we pointed up the limitations of our study and offered a number of directions for future investigation that might help address some of the concerns that our results raise.

An analysis of the effects of urbanization on urban drainage systems in Ankpa, Dekina and Oguma Towns of Kogi State in North Central Nigeria

Managing complex environmental problems such as flood disaster require up to date technology. Much of these disasters can be prevented and reduced through the use of technology such as Geographic Information Systems (GIS) and remote sensing as well as questionnaire administration. The spatial analytic capacity of GIS is employed to carryout hydrological analysis such as drainage network, DEM, Slope, flow direction and accumulation, stream order. Also land use and land cover analysis was done to examine the changes in land cover pattern across the study area. Different datasets were extracted from satellite imagery (landsat TM and OLI) of the Study area. ArcGIS 10.8 and Idrisi Terrset were used for the land cover change spatial analyses. The results from image analysis revealed that built-up area have been increasing significantly in the area due to increasing population pressure, it increases from 11.70%, 9.74% and 13.15% in 1990 to 18.12%, 16.05% and 19.37% in 2021 for Ankpa, Bassa and Oguma (Dekina) respectively. The flood risk vulnerability map reveals that about 15.44% (835.73 km2) and 15.81% (856.25 km2) of the study area have very high and high susceptibility to flooding inundation, respectively. The vulnerable zones are located on the lowest part of the plain which is responsible for the high flood vulnerability experienced in this area. It can be deduced that the magnitude of the flood hazard of a given area is a function of both the distance to the river and the elevation of the area in question. The result of the questionnaire analysis indicates that 67.55%,65.96% and 61.48% strongly agreed that shallow drainage channels in Ankpa, Dekina and Oguma are the major drainage challenges in the study area while 65.70%, 56.73% and 56.46%, strongly agree that several homes are destroyed when drainage fail and 56.20%, 63.32%, and 57.26% strongly agree that availability of social services is responsible for the increase population upsurge to urban centers. It is believed that the result of this research can be used as a means of regulating development along the plain and also serve as a decision support when making policies relating to drainage and flood management around urban centres.

Novel Moving Steady-State Visual Evoked Potential Stimulus to Assess Afferent and Efferent Dysfunction in Multiple Sclerosis.

Afferent and efferent visual dysfunction are prominent features of multiple sclerosis (MS). Visual outcomes have been shown to be robust biomarkers of the overall disease state. Unfortunately, precise measurement of afferent and efferent function is typically limited to tertiary care facilities, which have the equipment and analytical capacity to make these measurements, and even then, only a few centers can accurately quantify both afferent and efferent dysfunction. These measurements are currently unavailable in acute care facilities (ER, hospital floors). We aimed to develop a moving multifocal steady-state visual evoked potential (mfSSVEP) stimulus to simultaneously assess afferent and efferent dysfunction in MS for application on a mobile platform. The brain-computer interface (BCI) platform consists of a head-mounted virtual-reality headset with electroencephalogram (EEG) and electrooculogram (EOG) sensors. To evaluate the platform, we recruited consecutive patients who met the 2017 MS McDonald diagnostic criteria and healthy controls for a pilot cross-sectional study. Nine MS patients (mean age 32.7 years, SD 4.33) and ten healthy controls (24.9 years, SD 7.2) completed the research protocol. The afferent measures based on mfSSVEPs showed a significant difference between the groups (signal-to-noise ratio of mfSSVEPs for controls: 2.50 ± 0.72 vs. MS: 2.04 ± 0.47) after controlling for age (p = 0.049). In addition, the moving stimulus successfully induced smooth pursuit movement that can be measured by the EOG signals. There was a trend for worse smooth pursuit tracking in cases vs. controls, but this did not reach nominal statistical significance in this small pilot sample. This study introduces a novel moving mfSSVEP stimulus for a BCI platform to evaluate neurologic visual function. The moving stimulus showed a reliable capability to assess both afferent and efferent visual functions simultaneously.

Measuring policy analytical capacity in renewable energy policy: Germany‐Japan‐US comparison

AbstractPolicy makers in the modern age must confront complex and expanding knowledge on a daily basis. It has been put forth that policy making requires increasingly higher levels of policy analytical capacity at the individual, organizational, and system levels. Analytical capacity can enable better policy design and facilitate policy learning. Previous studies mostly operationalized the concept at the individual or organizational level with survey instruments, but the lack of widely and regularly conducted surveys limits the applicability of this method. Here, we propose new indicators of policy analytical capacity at the system level, using publicly available data. Our metrics are based on the outputs such as academic journal publications, government and stakeholder publications, and think‐tank rankings. As an illustration, we apply this approach to the case of renewable energy policies in three countries: Germany, Japan, and the United States. Though each of the underlying metrics is susceptible to uncertainties and biases, a totality of the indicators presents a useful, proxy measurement of policy analytical capacity. The proposed approach can supplement the survey method by enabling longitudinal and geographical investigation into policy analytical capacity.

Analytical capacity as a critical condition for responding to COVID-19 in Brazil

Abstract The coronavirus disease 2019 (COVID-19) pandemic posed several challenges to the Brazilian health system, among them the general context of ambiguity and uncertainty and the conflicting positioning of the government in power concerning scientific advice resources. Different aspects can be analyzed to explore the dynamics of strengthening and resilience of the system. This paper focuses on its analytical capacity, examining how it was developed and recently challenged. The investigation employed survey data, analysis of official documents and newspaper articles, and in-depth interviews with specialists and federal bureaucrats. The research shows the magnitude and relevance of that dimension of policy capacity in the federal health system and how it was crucial to the resilience of the system in defining the directions of the fight against COVID-19 in Brazil, although scientific-based recommendations were rejected by political leaders.

Open-Source Information – the Basis for Business Intelligence (BI)

The paper discusses the relationship between business intelligence (BI) and intelligence discipline based on information from open sources, better known as OSINT (open-source intelligence). The importance of the intelligence methodology – the intelligence cycle – is emphasized, which presupposes planning, systematic collection of information, its processing and analysis, and dissemination to end users. The goal is, ultimately, to make a business decision based on the knowledge. The development of BI and OSINT is discussed as well as the ideas that emerged in the 1970s when the importance of intelligence logic in the sphere of economy, and in society in general, was pointed out. Given the lack of empirical examples, the paper points out at the normative level that successful business is possible by relying on business intelligence (BI) and open-source information (OSINT), assuming the possession of analytical capacities, above all human and technological, which are capable to extrapolate the information necessary for a quality business decision from the immeasurable amount of data.

Equivalences between analytical railway capacity methods

Capacity analysis is of central importance in railway operation. Existing methods divide the infrastructure of question into smaller sections when computing the consumed capacity, which makes them nontransferable for real-world operation. We first review and enhance the UIC compression method, which results in a combination–reconstruction (ComRec) method to compute the compressed timetable graph of the whole infrastructure. Secondly, we propose a triangular-gap-problem-based (TGP) method to compute the headway times of train pairs when no more than one train lies within the separation gap of two trains. Then we show TGP method produces an compressed timetable graph equivalent to that by the ComRec method. Max-plus algebra approach determines the consumed capacity by solving an eigenvalue problem, and the solution corresponds to a timed event network as the compressed timetable. And by their correspondence, we show that these three methods are equivalent. Finally, we establish correspondences between the capacity methods and linear programming models. In this way, we were able to specify the condition when they give the same result and how infrastructure dividing underestimates capacity.

The Impact of Local Government Capacity on Public Service Delivery: Lessons Learned from Decentralized Indonesia

The issue of poor local government capacity has been considered one of the problems impeding the implementation of decentralization, especially in developing countries. This study addresses this issue in the case of Indonesia, a country that has implemented massive decentralization (administrative, fiscal, and political) over the last two decades. The study aims to provide empirical evidence for the impact of local government capacity on public service delivery in the decentralization regime. Local government capacity is measured based on a policy capacity framework at the organizational level that includes three types of capacities: analytical, operational, and political. The regression of the panel data model, estimated with the Hausman–Taylor method, reveals that government capacity in terms of interactions of three types of capacities has a positive impact on public service delivery. This finding indicates the three types of capacities are complementary and effectively improve local government’s achievement in delivering public services. While operational capacity (including fiscal capacity) has long been reckoned in designing decentralization, this result gives empirical evidence that other critical capacities should be well considered, political and analytical capacities. It underpins the efforts to internalize local government capacity in designing and implementing decentralization programs.

Comparison of Covid-19 Control Policies Between Indonesia and Australia Based on the Perspective of Policy Capacity Theory

The Covid-19 pandemic scrutinized the readiness and challenges of a country's control policies in the Asia Pacific region. This article aims to compare the policy capacity for Covid-19 control between the governments of Indonesia and Australia from the perspective of policy capacity theory. This article uses a qualitative case study method with data collection techniques for literature studies, online investigations, and data analysis. The two countries have something in common in implementing public health policies related to Covid-19, but the authors found different results. Based on the analysis using the theory of policy capacity, from the aspect of analytical capacity, it was found that the Indonesian Government was slow to respond to the Covid-19 pandemic. In contrast, the Australian Government carried out mandatory continuity of the Covid-19 policy. Then in terms of operational capacity, Indonesia still has minimum capacity and resources compared to those Australia. In terms of political capacity, the Government of Indonesia has lost public trust due to policy inconsistencies in controlling Covid-19. Meanwhile, the Australian Government can build public trust with transparent information disclosure. As a country with a large area and a high population in the face of Covid-19, it is necessary to strengthen policy capacity starting with practical policy design, leadership skills, good coordination between cross levels of government, and political legitimacy and public trust

Examining the geometry of streets through accessibility: new insights from streetspace allocation analysis

This paper describes streetspace allocation analysis, a method that uses street cross-sections to measure footway and carriageway widths and quantify a key parameter of street design citywide. The resulting network-based streetspace allocation metrics are employed on a proof-of-concept study of train station service areas in London, applying shortest-path analysis under a place and walking prioritisation approach. Overall, streetspace allocation statistics for London confirm the citywide predominance of space allocated for vehicular transport over pedestrian uses. A comparison of the current distribution and proposed re-allocation of streetspace on streets near stations allows for the investigation of the effects of streetspace enhancements, which tend to be beneficial in reducing pedestrian movement impedance and extending service areas. The methods presented here can offer valuable analytical capacity for developing new transit-oriented schemes and designing place-based streets that support sustainable transport and sustainable urban development.

Eight-year experience of maternal death surveillance in Morocco: qualitative study of stakeholders’ views at a subnational level

BackgroundSince 2009, Morocco has been implementing the Maternal Death Surveillance System (MDSS). The results obtained indicate significant regional variations in terms of implementation stage, completeness of maternal death reporting, and information use for action. The objective of this research is to better understand the contextual factors involved in the implementation process and use of MDSS, with a focus on the facilitators and barriers, as experienced by stakeholders in health regions. MethodsEvaluation research was conducted in 2017 based on a descriptive qualitative study using semi-structured in-depth interviews, in four out of the twelve health regions of Morocco. A total of thirty-one in-depth interviews were held with members of regional committees of maternal death reviews (RC-MDR) and other key informant staff. Interviews focused on participants’ views and their experiences with the MDSS since the introduction in 2009. We conducted thematic analysis relied on inductive and deductive approaches. Applying the Consolidated Framework for Implementation Research guided data analysis and reporting findings. FindingsEngaging leadership at all health system levels, regular training of district and regional MDSS coordinators and supportive supervision at a national level were the most important MDSS implementation facilitators. Reported barriers were essentially related to the review system: Irregular review meetings, blame culture, high turn-over of RC-MDR members, lack of analytical capacity to inform the review process and formulate recommendations, finally limited accountability for recommendation follow-up. While financial incentives boosted MDSS adoption, they were nonetheless a substantial barrier to its sustainability.ConclusionsThe MDSS is a complex process that requires taking numerous steps, including the commitment of multiple stakeholders with varying roles as well as information sharing across health system levels. Contextual factors that influence MDSS implementation at the sub-national level are to be considered. Horizontal and vertical communication about MDSS goals and feedback is crucial to strengthen stakeholders’ commitment, hence improving quality and use of MDSS. Furthermore, health regions should place emphasis on making high-quality recommendations in partnerships between the regional management teams, RC-MDR members and external stakeholders.

An Analysis by State on The Effect of Movement Control Order (MCO) 3.0 Due to COVID-19 on Malaysians’ Mental Health: Evidence from Google Trends

Due to significant social and economic upheavals brought on by the COVID-19 pandemic, there is a great deal of psychological pain. Google Trends data have been seen as a corollary measure to assess population-wide trends via observing trends in search results. Judicious analysis of Google Trends data can have both analytical and predictive capacities. This study aimed to compare nation-wide and inter-state trends in mental health before and after the Malaysian Movement Control Order 3.0 (MCO 3.0) commencing 12 May 2021. This was through assessment of two terms, “stress” and “sleep” in both the Malay and English language. Google Trends daily data between March 6 and 31 May in both 2019 and 2021 was obtained, and both series were re-scaled to be comparable. Searches before and after MCO 3.0 in 2021 were compared to searches before and after the same date in 2019. This was carried out using the differences in difference (DiD) method. This ensured that seasonal variations between states were not the source of our findings. We found that DiD estimates, β_3 for “sleep” and “stress” were not significantly different from zero, implying that MCO 3.0 had no effect on psychological distress in all states. Johor was the only state where the DiD estimates β_3 were significantly different from zero for the search topic ‘Tidur’. For the topic ‘Tekanan’, there were two states with significant DiD estimates, β_3, namely Penang and Sarawak. This study hence demonstrates that there are particular state-level differences in Google Trend search terms, which gives an indicator as to states to prioritise interventions and increase surveillance for mental health. In conclusion, Google Trends is a powerful tool to examine larger population-based trends especially in monitoring public health parameters such as population-level psychological distress, which can facilitate interventions.