ART data are made available as part of the National ART Surveillance System (NASS) that collects success rates, services, profiles and annual summary data from fertility clinics across the U.S. There are four datasets available: ART Services and Profiles, ART Patient and Cycle Characteristics, ART Success Rates, and ART Summary. All four datasets may be linked by “ClinicID.” ClinicID is a unique identifier for each clinic that reported cycles. The Summary dataset provides a full snapshot of clinic services and profile, patient characteristics, and ART success rates. It is worth noting that patient medical characteristics, such as age, diagnosis, and ovarian reserve, affect ART treatment’s success. Comparison of success rates across clinics may not be meaningful because of differences in patient populations and ART treatment methods. The success rates displayed in this dataset do not reflect any one patient’s chance of success. Patients should consult with a doctor to understand their chance of success based on their own characteristics.

ART data are made available as part of the National ART Surveillance System (NASS) that collects success rates, services, profiles and annual summary data from fertility clinics across the U.S. There are four datasets available: ART Services and Profiles, ART Patient and Cycle Characteristics, ART Success Rates, and ART Summary. All four datasets may be linked by “ClinicID.” ClinicID is a unique identifier for each clinic that reported cycles. The Summary dataset provides a full snapshot of clinic services and profile, patient characteristics, and ART success rates. It is worth noting that patient medical characteristics, such as age, diagnosis, and ovarian reserve, affect ART treatment’s success. Comparison of success rates across clinics may not be meaningful because of differences in patient populations and ART treatment methods. The success rates displayed in this dataset do not reflect any one patient’s chance of success. Patients should consult with a doctor to understand their chance of success based on their own characteristics.

ART data are made available as part of the National ART Surveillance System (NASS) that collects success rates, services, profiles and annual summary data from fertility clinics across the U.S. There are four datasets available: ART Services and Profiles, ART Patient and Cycle Characteristics, ART Success Rates, and ART Summary. All four datasets may be linked by “ClinicID.” ClinicID is a unique identifier for each clinic that reported cycles. The Patient and Cycle Characteristics dataset summarizes the types of ART services performed and the kinds of patients who received ART procedures in a specific clinic. Please note patient characteristics are presented per cycle rather than per patient. As a result, patients who had more than one ART cycle within the reporting year are represented more than once.

ART data are made available as part of the National ART Surveillance System (NASS) that collects success rates, services, profiles and annual summary data from fertility clinics across the U.S. There are four datasets available: ART Services and Profiles, ART Patient and Cycle Characteristics, ART Success Rates, and ART Summary. All four datasets may be linked by “ClinicID.” ClinicID is a unique identifier for each clinic that reported cycles. The Success Rates dataset contains success rates for ART cycles started during the year indicated. Since ART success depends on whether patients are using their own eggs or donor eggs, success rates are included separately for these two groups. Success rates for patients using their own eggs are shown per intended retrieval, per actual retrieval, and per transfer. These success rates are reported as cumulative success rates, which take into account transfers that occur within 1 year after an egg retrieval. Since ART success depends on whether patients are using ART for the first time or had prior ART cycles, users can examine success rates for all “Patients using their own eggs” or for “Patients with no prior ART using their own eggs.” For new patients using ART for the first time, the success rates are also shown after 1, 2, or all intended egg retrievals during the reporting year. In addition, the average number of transfers per intended retrieval and the average number of intended retrievals per live-birth delivery are shown. Success rates for ART cycles that involve the transfer of embryos created from donor eggs or donated embryos are shown and are not cumulative. They are based on donor cycles started in the year indicated that had embryo transfers, regardless of when the donor eggs were retrieved. Success rates in this section are not presented by patient age group because previous data show that an intended parent’s age does not substantially affect success when using donor eggs or donated embryos. The success rates are presented by types of embryos and eggs used in the transfer. This dataset excludes cycles that were considered research—that is, cycles performed to evaluate new procedures.

Data were updated on September 11, 2024. ART data are made available as part of the National ART Surveillance System (NASS) that collects success rates, services, profiles and annual summary data from fertility clinics across the U.S. There are four datasets available: ART Services and Profiles, ART Patient and Cycle Characteristics, ART Success Rates, and ART Summary. All four datasets may be linked by “ClinicID.” ClinicID is a unique identifier for each clinic that reported cycles. The Success Rates dataset contains success rates for ART cycles started during the year indicated. Since ART success depends on whether patients are using their own eggs or donor eggs, success rates are included separately for these two groups. Success rates for patients using their own eggs are shown per intended retrieval, per actual retrieval, and per transfer. These success rates are reported as cumulative success rates, which take into account transfers that occur within 1 year after an egg retrieval. Since ART success depends on whether patients are using ART for the first time or had prior ART cycles, users can examine success rates for all “Patients using their own eggs” or for “Patients with no prior ART using their own eggs.” For new patients using ART for the first time, the success rates are also shown after 1, 2, or all intended egg retrievals during the reporting year. In addition, the average number of transfers per intended retrieval and the average number of intended retrievals per live-birth delivery are shown. Success rates for ART cycles that involve the transfer of embryos created from donor eggs or donated embryos are shown and are not cumulative. They are based on donor cycles started in the year indicated that had embryo transfers, regardless of when the donor eggs were retrieved. Success rates in this section are not presented by patient age group because previous data show that an intended parent’s age does not substantially affect success when using donor eggs or donated embryos. The success rates are presented by types of embryos and eggs used in the transfer. This dataset excludes cycles that were considered research—that is, cycles performed to evaluate new procedures.

Data were updated on September 11, 2024. ART data are made available as part of the National ART Surveillance System (NASS) that collects success rates, services, profiles and annual summary data from fertility clinics across the U.S. There are four datasets available: ART Services and Profiles, ART Patient and Cycle Characteristics, ART Success Rates, and ART Summary. All four datasets may be linked by “ClinicID.” ClinicID is a unique identifier for each clinic that reported cycles. The Success Rates dataset contains success rates for ART cycles started during the year indicated. Since ART success depends on whether patients are using their own eggs or donor eggs, success rates are included separately for these two groups. Success rates for patients using their own eggs are shown per intended retrieval, per actual retrieval, and per transfer. These success rates are reported as cumulative success rates, which take into account transfers that occur within 1 year after an egg retrieval. Since ART success depends on whether patients are using ART for the first time or had prior ART cycles, users can examine success rates for all “Patients using their own eggs” or for “Patients with no prior ART using their own eggs.” For new patients using ART for the first time, the success rates are also shown after 1, 2, or all intended egg retrievals during the reporting year. In addition, the average number of transfers per intended retrieval and the average number of intended retrievals per live-birth delivery are shown. Success rates for ART cycles that involve the transfer of embryos created from donor eggs or donated embryos are shown and are not cumulative. They are based on donor cycles started in the year indicated that had embryo transfers, regardless of when the donor eggs were retrieved. Success rates in this section are not presented by patient age group because previous data show that an intended parent’s age does not substantially affect success when using donor eggs or donated embryos. The success rates are presented by types of embryos and eggs used in the transfer. This dataset excludes cycles that were considered research—that is, cycles performed to evaluate new procedures.

ART data are made available as part of the National ART Surveillance System (NASS) that collects success rates, services, profiles and annual summary data from fertility clinics across the U.S. There are four datasets available: ART Services and Profiles, ART Patient and Cycle Characteristics, ART Success Rates, and ART Summary. All four datasets may be linked by “ClinicID.” ClinicID is a unique identifier for each clinic that reported cycles. The Services and Profiles dataset provides an overview of clinic services, the clinic’s contact information, _location, the medical director’s name, and summary statistics.

ART data are made available as part of the National ART Surveillance System (NASS) that collects success rates, services, profiles and annual summary data from fertility clinics across the U.S. There are four datasets available: ART Services and Profiles, ART Patient and Cycle Characteristics, ART Success Rates, and ART Summary. All four datasets may be linked by “ClinicID.” ClinicID is a unique identifier for each clinic that reported cycles. The Patient and Cycle Characteristics dataset summarizes the types of ART services performed and the kinds of patients who received ART procedures in a specific clinic. Please note patient characteristics are presented per cycle rather than per patient. As a result, patients who had more than one ART cycle within the reporting year are represented more than once.

ART data are made available as part of the National ART Surveillance System (NASS) that collects success rates, services, profiles and annual summary data from fertility clinics across the U.S. There are four datasets available: ART Services and Profiles, ART Patient and Cycle Characteristics, ART Success Rates, and ART Summary. All four datasets may be linked by “ClinicID.” ClinicID is a unique identifier for each clinic that reported cycles. The Patient and Cycle Characteristics dataset summarizes the types of ART services performed and the kinds of patients who received ART procedures in a specific clinic. Please note patient characteristics are presented per cycle rather than per patient. As a result, patients who had more than one ART cycle within the reporting year are represented more than once.

Data were updated on September 11, 2024.

ART data are made available as part of the National ART Surveillance System (NASS) that collects success rates, services, profiles and annual summary data from fertility clinics across the U.S. There are four datasets available: ART Services and Profiles, ART Patient and Cycle Characteristics, ART Success Rates, and ART Summary. All four datasets may be linked by “ClinicID.” ClinicID is a unique identifier for each clinic that reported cycles. The Summary dataset provides a full snapshot of clinic services and profile, patient characteristics, and ART success rates. It is worth noting that patient medical characteristics, such as age, diagnosis, and ovarian reserve, affect ART treatment’s success. Comparison of success rates across clinics may not be meaningful because of differences in patient populations and ART treatment methods. The success rates displayed in this dataset do not reflect any one patient’s chance of success. Patients should consult with a doctor to understand their chance of success based on their own characteristics.

ART data are made available as part of the National ART Surveillance System (NASS) that collects success rates, services, profiles and annual summary data from fertility clinics across the U.S. There are four datasets available: ART Services and Profiles, ART Patient and Cycle Characteristics, ART Success Rates, and ART Summary. All four datasets may be linked by “ClinicID.” ClinicID is a unique identifier for each clinic that reported cycles. The Services and Profiles dataset provides an overview of clinic services, the clinic’s contact information, location, the medical director’s name, and summary statistics.

ART data are made available as part of the National ART Surveillance System (NASS) that collects success rates, services, profiles and annual summary data from fertility clinics across the U.S. There are four datasets available: ART Services and Profiles, ART Patient and Cycle Characteristics, ART Success Rates, and ART Summary. All four datasets may be linked by “ClinicID.” ClinicID is a unique identifier for each clinic that reported cycles. The Success Rates dataset contains success rates for ART cycles started during the year indicated. Since ART success depends on whether patients are using their own eggs or donor eggs, success rates are included separately for these two groups. Success rates for patients using their own eggs are shown per intended retrieval, per actual retrieval, and per transfer. These success rates are reported as cumulative success rates, which take into account transfers that occur within 1 year after an egg retrieval. Since ART success depends on whether patients are using ART for the first time or had prior ART cycles, users can examine success rates for all “Patients using their own eggs” or for “Patients with no prior ART using their own eggs.” For new patients using ART for the first time, the success rates are also shown after 1, 2, or all intended egg retrievals during the reporting year. In addition, the average number of transfers per intended retrieval and the average number of intended retrievals per live-birth delivery are shown. Success rates for ART cycles that involve the transfer of embryos created from donor eggs or donated embryos are shown and are not cumulative. They are based on donor cycles started in the year indicated that had embryo transfers, regardless of when the donor eggs were retrieved. Success rates in this section are not presented by patient age group because previous data show that an intended parent’s age does not substantially affect success when using donor eggs or donated embryos. The success rates are presented by types of embryos and eggs used in the transfer. This dataset excludes cycles that were considered research—that is, cycles performed to evaluate new procedures.

Data were updated on September 11, 2024.

ART data are made available as part of the National ART Surveillance System (NASS) that collects success rates, services, profiles and annual summary data from fertility clinics across the U.S. There are four datasets available: ART Services and Profiles, ART Patient and Cycle Characteristics, ART Success Rates, and ART Summary. All four datasets may be linked by “ClinicID.” ClinicID is a unique identifier for each clinic that reported cycles. The Summary dataset provides a full snapshot of clinic services and profile, patient characteristics, and ART success rates. It is worth noting that patient medical characteristics, such as age, diagnosis, and ovarian reserve, affect ART treatment’s success. Comparison of success rates across clinics may not be meaningful because of differences in patient populations and ART treatment methods. The success rates displayed in this dataset do not reflect any one patient’s chance of success. Patients should consult with a doctor to understand their chance of success based on their own characteristics.

ART data are made available as part of the National ART Surveillance System (NASS) that collects success rates, services, profiles and annual summary data from fertility clinics across the U.S. There are four datasets available: ART Services and Profiles, ART Patient and Cycle Characteristics, ART Success Rates, and ART Summary. All four datasets may be linked by “ClinicID.” ClinicID is a unique identifier for each clinic that reported cycles. The Patient and Cycle Characteristics dataset summarizes the types of ART services performed and the kinds of patients who received ART procedures in a specific clinic. Please note patient characteristics are presented per cycle rather than per patient. As a result, patients who had more than one ART cycle within the reporting year are represented more than once.

Tranche 2: Strategic Objective 4

Automation can improve biosecurity surveillance systems

Surveillance is an essential part of protecting New Zealand’s economic assets and natural taonga from damaging exotic organisms.

Our government currently spends over $125 million a year on monitoring for biological threats. It’s an expensive process because it requires thousands of hours of highly skilled human labour.

As part of the BioHeritage National Science Challenge the State-of-the-art Surveillance team is developing prototype technologies that will automate and improve surveillance results, while saving costs.

Research Area Summary

Spectral imaging for urban tree health

Our research team is using a camera mounted on a moving vehicle to monitor the health of our urban rākau (trees).

The camera takes pictures of street trees every fortnight. Images are pre-processed to maintain people’s privacy (blurring cars, people, houses etc.) and then analysed by computer to identify what kind of trees each street has, and whether they look damaged by pest or disease.

When we find sick trees we can send a biosecurity inspector to focus just on the damaged trees – saving time and money. It works the other way, too: if our DNA project above discovers a new insect has entered the country, and we know that it’s a threat to a specific kind of tree, we’ll have a map of where those trees are in our cities and can go inspect them.

Insect Soup: sampling eDNA with light traps

Our researchers are installing UV light traps at the Port of Tauranga to help detect the arrival of foreign insects.

All of the insects caught will be analyzed in the lab by a process called High-Throughput Sequencing (HTS). This will tell us the DNA of every insect species caught in the trap.

If a new insect species or a known threat comes into Tauranga, we know where and when it was collected, and we can take steps to eradicate it.

Developing DNA diagnostic technologies like this allows us to cost-effectively scale-up our efforts to find new pests early. This early detection is essential for protecting our natural and productive environments.

Leader:

• Steve Pawson (University of Canterbury)

ART data are made available as part of the National ART Surveillance System (NASS) that collects success rates, services, profiles and annual summary data from fertility clinics across the U.S. There are four datasets available: ART Services and Profiles, ART Patient and Cycle Characteristics, ART Success Rates, and ART Summary. All four datasets may be linked by “ClinicID.” ClinicID is a unique identifier for each clinic that reported cycles. The Summary dataset provides a full snapshot of clinic services and profile, patient characteristics, and ART success rates. It is worth noting that patient medical characteristics, such as age, diagnosis, and ovarian reserve, affect ART treatment’s success. Comparison of success rates across clinics may not be meaningful because of differences in patient populations and ART treatment methods. The success rates displayed in this dataset do not reflect any one patient’s chance of success. Patients should consult with a doctor to understand their chance of success based on their own characteristics.

Airport Video Surveillance Cameras Market Outlook

The global airport video surveillance cameras market size was valued at USD 2.8 billion in 2023 and is projected to reach USD 4.6 billion by 2032, growing at a compound annual growth rate (CAGR) of 5.8% from 2024 to 2032. This robust growth trajectory is driven by the increasing emphasis on airport security measures across the globe, fueled by rising security threats and the need for real-time monitoring and enhanced surveillance systems. With air travel recovering and expected to grow post-pandemic, airports worldwide are investing heavily in advanced surveillance technologies to ensure passenger safety and efficient airport operations, further propelling the market's expansion.

One of the primary growth factors in the airport video surveillance cameras market is the increasing vulnerability of airports to terrorism and other illegal activities. As key transit points, airports are critical to national security, necessitating comprehensive surveillance systems. The demand for sophisticated video surveillance, capable of providing real-time data and high-resolution imagery, is rising as airports strive to enhance their security infrastructure. Furthermore, advancements in surveillance technology, such as AI-driven analytics, are enabling more efficient monitoring and threat detection. These technological innovations are not only improving the effectiveness of surveillance systems but also significantly reducing the response time to potential security threats, driving their adoption globally.

Another key driver is the integration of video surveillance systems with advanced technologies like AI, IoT, and machine learning, which are drastically transforming the landscape of airport security. These technologies facilitate enhanced threat detection, predictive analysis, and automated incident response, making airport environments safer and more secure. The integration of these technologies into video surveillance systems allows for proactive rather than reactive security measures. Additionally, the growth of smart airports and the increasing adoption of smart technologies in infrastructure development further contribute to the demand for advanced surveillance cameras, which are crucial for implementing smart security solutions.

The expansion of airport infrastructure globally is also a significant growth driver for the airport video surveillance cameras market. With the rise in global air traffic, there is a corresponding need for expanded and upgraded airport facilities, including the enhancement of existing security infrastructure. This trend is particularly evident in emerging markets, where the development of new airports is being accompanied by the installation of state-of-the-art security systems. Furthermore, government regulations and policies mandating stringent security measures at airports are compelling airport authorities to invest in upgraded video surveillance solutions, thereby augmenting market growth.

Regionally, North America holds a significant share of the airport video surveillance cameras market, primarily due to high-security concerns and the presence of major airports implementing advanced surveillance technologies. However, the Asia Pacific region is anticipated to exhibit the highest growth rate during the forecast period, driven by significant infrastructural developments, increasing air passenger traffic, and the adoption of smart airport technologies. Europe also represents a substantial market share, with countries like the UK, Germany, and France leading in the adoption of advanced security systems. The Middle East & Africa region is also witnessing considerable investments in airport infrastructure, further enhancing the demand for sophisticated surveillance solutions.

In the realm of aviation security, Cabin Surveillance Systems have emerged as a pivotal component in ensuring the safety and security of passengers and crew members. These systems are designed to monitor activities within the aircraft cabin, providing real-time data and video feeds to ground control and flight crew. The integration of these systems into the broader airport security framework enhances situational awareness and allows for immediate response to any suspicious activities or security breaches. As technology continues to advance, the capabilities of Cabin Surveillance Systems are expanding, offering features such as facial recognition and behavioral analysis to further bolster in-flight security measures.

Camera Type Analy

The “Sixtiers Museum” Collection is located in a small museum in Kyiv, Ukraine in a building belonging to the Ukrainian political party Rukh. Nadia Svitlychna and Mykola Plakhotniuk founded this museum as way of honouring and documenting the struggles of a cohort of Soviet Ukrainian dissidents during the 1960s-1980s. Included in the permanent exhibition are paintings, graphics, sculptures, embroidery and other artworks produced by artists affiliated with the sixtiers movement. The museum also displays the poems, letters and literary works of the writers in their midst, as well as their typewriters, handcrafted items made while in the GULag, or clothes worn while living in exile, like Svitlychna’s own camp uniform. Also figuring prominently are posters for events and exhibitions organized by this group. The guided tour is a moving, concise rendition of their struggle, aimed at the museum’s target audiences, young students, scholars, and the general public. These materials depict the lives of a dynamic group of Soviet Ukrainians engaged in a principled creative and ideological struggle with the Soviet regime in the 1960s and 1970s. They were poets, artists, graphic designers, historians, doctors, and even a Soviet army official, all of whom became deeply involved in human rights activism under late socialism. Many were members of large Soviet institutions—like the Ukrainian writers and artist unions, the Literary Institute in Kyiv, the Soviet armed forces. The Soviet government’s ideological retrenchment after Khrushchev transformed these dissidents, who had worked hard to try and reform the system and make it more humane, into individuals in open conflict with the authorities.

Digital AESA Ground Surveillance Radar Market Outlook

According to our latest research, the global Digital AESA Ground Surveillance Radar market size reached USD 4.2 billion in 2024, reflecting robust growth driven by increasing defense modernization and border security initiatives worldwide. The market is expected to grow at a CAGR of 7.8% from 2025 to 2033, reaching a projected value of USD 8.3 billion by 2033. This growth is primarily fueled by heightened demand for advanced surveillance technologies, rapid technological advancements in radar systems, and the rising need for real-time situational awareness across both military and commercial sectors.

The expansion of the Digital AESA Ground Surveillance Radar market is largely attributed to the surge in global security threats, including cross-border terrorism, smuggling, and illegal immigration. Governments and defense agencies are increasingly investing in state-of-the-art surveillance systems to enhance national security and safeguard critical infrastructure. The adoption of Active Electronically Scanned Array (AESA) technology enables superior detection capabilities, faster target acquisition, and improved reliability compared to traditional radar systems. Additionally, the integration of digital signal processing and artificial intelligence is enabling these radars to offer more accurate and timely threat identification, further driving market growth.

Another significant growth factor is the technological evolution of radar components and platforms. Modern AESA radars are now more compact, energy-efficient, and capable of operating in diverse environmental conditions. The shift towards digital architectures allows for seamless integration with command and control systems, enhancing interoperability and network-centric warfare capabilities. The increasing deployment of mobile and portable ground surveillance radars is also expanding the market’s reach beyond traditional military applications, finding use in border surveillance, perimeter security, and critical infrastructure protection. These innovations are making AESA radars more accessible and cost-effective for a broad range of end-users.

The market’s growth trajectory is further bolstered by rising investments from both public and private stakeholders. Defense budgets across North America, Europe, and Asia Pacific are being allocated towards upgrading legacy radar systems with digital AESA technology, reflecting a strategic shift towards multi-mission, multi-domain operations. Simultaneously, commercial applications such as airport security, industrial facility monitoring, and event perimeter management are gaining traction, contributing to the diversification of the market. The presence of a strong innovation ecosystem, government-led R&D initiatives, and strategic partnerships between radar manufacturers and technology providers are expected to sustain the market’s momentum over the forecast period.

Regionally, North America continues to lead the Digital AESA Ground Surveillance Radar market owing to substantial defense spending, advanced technological infrastructure, and the presence of major defense contractors. Europe follows closely, propelled by collaborative security initiatives and modernization programs. The Asia Pacific region is emerging as a high-growth market, underpinned by rising geopolitical tensions, border disputes, and increased investments in homeland security. Latin America and the Middle East & Africa are also witnessing steady adoption, driven by efforts to combat transnational crime and protect critical assets. The interplay of these regional dynamics is shaping the competitive landscape and creating new opportunities for market participants.

Component Analysis

The component segment of the Digital AESA Ground Surveillance Radar market encompasses a diverse array of critical elements, including transmitters, receivers, antennas, signal processors, power supplies, and others. Each component p

Study question: To what extent do financial, demographic, and cultural determinants explain the vast cross-national differences in assisted reproductive technology (ART) treatments in Europe?

Summary answer: The normative cultural acceptance of ART is a major driver of ART treatments in Europe, above and beyond differences in country wealth, demographic aspects, and religious composition.

What is known already: There are vast differences in the number of ART treatments across European countries, which are to some extent related to country affluence, regulation, and insurance coverage and costs. The role and impact of cultural and normative factors has not been explored in a larger cross-national comparison.

Study design, size, duration: A descriptive and comparative cross-national analysis of ART treatment prevalence in over 30 European countries in 2010, with the outcome defined as the total number of ART cycles per million women of reproductive age (15–44 years). Data is drawn from multiple sources (ICMART, US Census Bureau Library, World Bank, Barro–Lee Educational Attainment Dataset, IFFS Surveillance reports, European Values Study, and World Religion Database).

Participants/materials, setting, methods: Our sample includes data from 35 European countries, where we describe the associations between demographic and cultural factors and the prevalence of ART treatments. Bivariate correlation and ordinary least squares (OLS) multiple regression analysis serves to establish the relationships between predictor variables and the number of ART treatments per million women aged 15–44 years in a country.

Main results and the role of chance: A one-percent increase in national GDP is associated with 382 (95% CI: 177–587) additional ART procedures per million women of reproductive age, yet this effect is reduced to 99 (-92–290) treatments once cultural values are accounted for. In our fully adjusted model, normative cultural values about the acceptability of ART are the strongest predictor of ART usage, with a one-point increase of average approval in a country associated with 276 (167–385) additional ART treatments per million women of reproductive age.

Limitations, reasons for caution: Findings are based on a cross-sectional, cross-national analysis, making formal tests of causality impossible and prohibiting inferences to the individual level.

Wider implications of the findings: Results indicate that reproductive health policy should openly acknowledge the importance of cultural norms in informally shaping and regulating the wider availability of ART treatment.