CHICAGO – DePaul women’s basketball pushed crosstown foe UIC deep into the fourth quarter Wednesday night, but a late Flames run proved decisive as the Blue demons fell 77-67 at Credit union 1 Arena.

DePaul (3-10, 0-2 BIG EAST) erased a double-digit second-quarter deficit and surged ahead behind a strong third quarter, but could not withstand a 26-point fourth period from the Flames.

Junior guard Shakara McCline paced DePaul offensively for the second straight game,finishing wiht a career-high 25 points,knocking down four three-pointers on 44-percent shooting from long range. Redshirt freshman Ally Timm added 11 points, connecting on three triples.

The Blue Demons were active on the glass, pulling down 18 offensive rebounds, one shy of their season high, and converting those opportunities into 24 second-chance points, good for a +11 advantage. depaul also moved the ball effectively, recording 20 assists on 24 made field goals for its fifth game this season with 20 or more assists. Sophomore Devin Hagemann handed out six assists, while Natiah Nelson set a career high with five.

UIC (6-5,1-0 MVC) was lead by Julia Coleman and Jessica Carrothers,who scored 29 and 24 points,respectively.

How It Happened

UIC opened the game on a 7-0 run before DePaul answered with a 6-0 spurt of its own to pull within a possession midway through the first quarter. Michelle Ojo’s free throws in the final minute accounted for DePaul’s last points of the period as the Flames held a 14-11 edge after 10 minutes.

McCline scored DePaul’s first nine points of the second quarter as the Blue Demons stayed within striking distance, but an 8-0 Flames run pushed the lead to double digits late in the half. DePaul closed the quarter with the final five points to trim the deficit to eight at the break, 37-29.

DePaul took just its second lead of the game at the 3:33 mark of the third quarter when McCline buried her second three-pointer of the night off a second-chance chance. The Blue Demons carried a 52-51 advantage into the final period.

Navigating the New Instagram Algorithm: A Guide for Brands in 2024

Instagram’s algorithm is a constantly evolving beast,leaving marketers perpetually playing catch-up. What worked last year – or even last month – might not deliver the same results today. As we move further into 2024, understanding the current landscape is crucial for maximizing reach, engagement, and ultimately, ROI. This guide breaks down the key factors influencing the Instagram algorithm and provides actionable strategies for brands to thrive.

What’s Driving the Algorithm Now?

Instagram has publicly stated its algorithm prioritizes content based on a multitude of signals, broadly categorized as:

* Relationships: Instagram prioritizes content from accounts users interact with most frequently.This includes likes, comments, shares, saves, and direct messages. The closer the relationship, the higher the likelihood of seeing content.
* Interest: the algorithm predicts how interested a user will be in a post based on their past behavior. This includes the types of content they’ve engaged with, accounts they follow, and topics they explore.
* Timeliness: While not the dominant factor it once was, recent posts still have an advantage.Instagram wants to show users fresh content.
* Frequency: How often a user opens instagram impacts what they see. More frequent users will see a more up-to-date feed.
* Following: The number of accounts a user follows influences the saturation of their feed. Following more accounts means more competition for visibility.
* Usage: How long a user spends on Instagram influences the type of content they are shown. Longer sessions allow the algorithm to gather more data and refine its predictions.

Key Changes to Watch in 2024:

* Emphasis on original Content: Instagram is actively downranking reposted content, particularly from TikTok.The platform wants to incentivize creators to post directly to instagram. Watermarked content from other platforms is also being penalized.
* Video Dominance Continues: Reels remain a priority, and instagram is pushing longer-form video content (up to 90 minutes) with features like Instagram Broadcast Channels.
* increased Focus on “Close Friends” & Group Stories: instagram is investing in features that foster smaller, more intimate communities. Content shared within thes groups is highly likely to receive higher engagement and visibility among those users.
* AI-Powered Recommendations: Instagram’s Explore page and Reels feed are increasingly driven by AI recommendations, meaning understanding relevant hashtags and trending topics is more significant than ever.

Strategies for brands to Win:

Here’s how brands can adapt their Instagram strategy to align with the current algorithm:

1. Prioritize High-Quality, Original Video Content:

* Reels are King: Continue to invest in Reels, experimenting with different formats (tutorials, behind-the-scenes, challenges, etc.). Focus on creating engaging hooks within the first 3 seconds to capture attention.
* Embrace Longer-Form Video: Explore instagram Broadcast Channels and longer Reels to tell more in-depth stories and build deeper connections with your audience.
* Avoid Reposted content: Create content specifically for Instagram. If you must share content from other platforms, edit it significantly and add unique value.

2. Foster Meaningful Engagement:

* Respond to Comments & DMs: Actively engage with your audience. Respond to comments and direct messages promptly and thoughtfully.
* Ask Questions: Encourage conversation by asking questions in your captions and Stories.
* Run Polls & Quizzes: Interactive Story features like polls and quizzes are grate for boosting engagement and gathering audience insights.
* Host Live sessions: live videos provide a real-time connection with your audience and can significantly boost engagement.

3.Optimize for Discoverability:

* Strategic Hashtags: Use a mix of broad, niche, and branded hashtags. Research relevant trending hashtags. Don’t overdo it – 3-5 highly relevant hashtags are often more effective than a long list.
* Keyword Research: Incorporate relevant keywords into your captions and profile bio to improve search visibility.
* Location Tagging: Tag your location to reach users in your area.
* Collaborate with Influencers: Partner

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The Rise of AI-Powered Threat Hunting

For years, cybersecurity professionals have engaged in a constant arms race against increasingly complex cyber threats. Traditional security measures, while essential, often struggle to keep pace with the speed and complexity of modern attacks. This has led to the growing adoption of threat hunting – a proactive search for malicious activity that has evaded existing security systems. Now, a new evolution is underway: the integration of artificial Intelligence (AI) into threat hunting processes.

The Limitations of Traditional Threat Hunting

Traditional threat hunting relies heavily on skilled analysts poring over logs, network traffic, and endpoint data, looking for anomalies and indicators of compromise (IOCs). This process is time-consuming, resource-intensive, and prone to human error. Analysts can easily become overwhelmed by the sheer volume of data, leading to alert fatigue and missed threats. Furthermore, traditional methods often struggle to identify novel attacks that don’t match known patterns.

How AI Enhances Threat Hunting

AI offers a powerful solution to these challenges. By leveraging machine learning algorithms, AI-powered threat hunting tools can automate many of the tedious and repetitive tasks associated with traditional hunting. Here’s how:

• Anomaly detection: AI algorithms can establish a baseline of normal behavior and quickly identify deviations that may indicate malicious activity. This is particularly useful for detecting zero-day exploits and insider threats.
• Behavioral Analysis: AI can analyze user and entity behavior to identify patterns that are indicative of compromise. For example, an AI system might flag a user accessing sensitive data outside of normal working hours.
• Automated Examination: AI can automatically investigate alerts, correlating data from multiple sources to provide analysts with a more complete picture of the threat.
• Threat Prioritization: AI can prioritize alerts based on their severity and potential impact, allowing analysts to focus on the most critical threats first.
• Predictive Analysis: Some AI systems can even predict future attacks based on past data and threat intelligence.

Key AI Techniques Used in Threat Hunting

Several AI techniques are proving particularly effective in threat hunting:

• Machine Learning (ML): ML algorithms are trained on large datasets of both benign and malicious activity, allowing them to identify patterns and anomalies.
• Natural Language Processing (NLP): NLP can be used to analyze unstructured data, such as security reports and threat intelligence feeds, to extract valuable insights.
• Deep Learning: A subset of ML, deep learning uses artificial neural networks with multiple layers to analyze complex data and identify subtle patterns.
• Unsupervised Learning: This technique is used to identify anomalies in data without requiring pre-labeled examples of malicious activity.

Benefits of AI-Powered Threat Hunting

The benefits of integrating AI into threat hunting are significant:

• Improved Threat Detection: AI can detect threats that would likely be missed by traditional methods.
• Reduced False Positives: AI algorithms can filter out false positives, reducing alert fatigue and allowing analysts to focus on genuine threats.
• Faster Response Times: AI can automate many of the tasks associated with incident response, reducing the time it takes to contain and remediate threats.
• increased Efficiency: AI frees up analysts to focus on more strategic tasks, such as threat modeling and vulnerability management.
• Enhanced Security Posture: By proactively identifying and mitigating threats, AI-powered threat hunting can significantly improve an organization’s overall security posture.

challenges and Considerations

While AI offers tremendous potential, there are also challenges to consider:

• Data Quality: AI algorithms are only as good as the data they are trained on.Poor data quality can lead to inaccurate results.
• explainability: Some AI models, particularly deep learning models, can be difficult to interpret. This can make it challenging to understand why an AI system made a particular decision.
• Skills Gap: Implementing and managing AI-powered threat hunting tools requires specialized skills.
• Cost: AI-powered threat hunting tools can be expensive.
• Adversarial AI: Attackers are increasingly using AI to evade detection. Security teams need to be prepared to defend against these attacks.

The Future of Threat Hunting

AI is poised to play an increasingly important role in threat hunting. As AI technology continues to evolve, we can expect to see even more sophisticated tools and techniques emerge. The future of threat hunting will likely involve a collaborative approach,where AI augments the skills of human analysts,allowing them to work more effectively and efficiently. Organizations that embrace AI-powered threat hunting will be better positioned to defend against the ever-evolving threat landscape.

Publication Date: 2025/12/18 17:35:51