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diff --git a/publications/cicirello2024applsci.bib b/publications/cicirello2024applsci.bib
@@ -1,9 +1,14 @@
 @ARTICLE{cicirello2024applsci,
-TITLE = {Special Issue on Evolutionary Computation: Theories, Techniques, and Applications},
-EDITOR = {Vincent A. Cicirello},
+AUTHOR = {Vincent A. Cicirello},
+TITLE = {Evolutionary Computation: Theories, Techniques, and Applications},
+ARTICLENO = {2542},
+DOI = {10.3390/app14062542},
 JOURNAL = {Applied Sciences},
-MONTH = {January},
+MONTH = {March},
+NUMBER = {6},
+NUMPAGES = {6},
+VOLUME = {14},
 YEAR = {2024},
-URL = {https://www.mdpi.com/journal/applsci/special_issues/Evolutionary_Computation}
+URL = {https://www.cicirello.org/publications/applsci-14-02542.pdf}
 }
 
diff --git a/publications/cicirello2024applsci.html b/publications/cicirello2024applsci.html
@@ -7,52 +7,20 @@
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-<title>Special Issue on Evolutionary Computation: Theories, Techniques, and Applications - Applied Sciences - 2024</title>
-<meta name="title" content="Special Issue on Evolutionary Computation: Theories, Techniques, and Applications - Applied Sciences - 2024">
-<meta itemprop="name" content="Special Issue on Evolutionary Computation: Theories, Techniques, and Applications - Applied Sciences - 2024">
-<meta name="description" content="Evolutionary computation offers powerful problem-solving methodologies inspired
-by models of natural genetics and evolutionary processes. Potential
-applications are wide-ranging and include problems from combinatorial
-optimization, numerical optimization, multi-objective optimization, and others
-as well as specific applications of these problems in diverse domains, such as
-engineering, design, medicine, robotics, science, etc. Techniques from
-evolutionary computation often lend themselves well to parallel and distributed
-implementations and are often more effective in dealing with challenging
-problem characteristics such as non-linearity and high-dimensionality than
-alternative approaches.    This Special Issue invites submissions on
-recent advances in the theory and applications of evolutionary computation.
-Papers on all forms of evolutionary computation are welcome, including but not
-limited to genetic algorithms, genetic programming, ...">
-<meta itemprop="description" content="Evolutionary computation offers powerful problem-solving methodologies inspired
-by models of natural genetics and evolutionary processes. Potential
-applications are wide-ranging and include problems from combinatorial
-optimization, numerical optimization, multi-objective optimization, and others
-as well as specific applications of these problems in diverse domains, such as
-engineering, design, medicine, robotics, science, etc. Techniques from
-evolutionary computation often lend themselves well to parallel and distributed
-implementations and are often more effective in dealing with challenging
-problem characteristics such as non-linearity and high-dimensionality than
-alternative approaches.    This Special Issue invites submissions on
-recent advances in the theory and applications of evolutionary computation.
-Papers on all forms of evolutionary computation are welcome, including but not
-limited to genetic algorithms, genetic programming, evolution strategies,
-evolutionary programming, and memetic algorithms, as well as papers at the
-intersection of evolutionary computation and machine learning, such as
-neuroevolution. Additionally, we welcome papers that employ closely related
-techniques such as simulated annealing, ant colony optimization, particle swarm
-optimization, artificial immune systems, and other metaheuristics. All
-application areas are welcome, but submissions that deal with problems in the
-applied sciences are especially encouraged.    The special issue covers
-the following non-exclusive list of topics:     ant colony
-optimization;   artificial immune systems;   combinatorial
-optimization;   evolution strategies;   evolutionary
-computation;   evolutionary data mining;   evolutionary machine
-learning;   evolutionary programming;   evolvable hardware; 
- genetic algorithms;   genetic programming;   memetic
-algorithms;   metaheuristics;   multi-objective
-optimization;   neuroevolution;   numerical optimization; 
- particle swarm optimization;   simulated annealing.   
- Submissions opened May 2022 with a deadline of January 30, 2024.">
+<title>Evolutionary Computation: Theories, Techniques, and Applications - Applied Sciences - 2024</title>
+<meta name="title" content="Evolutionary Computation: Theories, Techniques, and Applications - Applied Sciences - 2024">
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+<meta itemprop="description" content="Evolutionary Computation: Theories, Techniques, and Applications - Applied Sciences - 2024">
+<meta name="citation_title" content="Evolutionary Computation: Theories, Techniques, and Applications">
+<meta name="citation_author" content="Vincent A. Cicirello">
+<meta name="citation_publication_date" content="2024">
+<meta name="citation_date" content="2024">
+<meta name="citation_journal_title" content="Applied Sciences">
+<meta name="citation_volume" content="14">
+<meta name="citation_issue" content="6">
+<meta name="citation_pdf_url" content="https://www.cicirello.org/publications/applsci-14-02542.pdf">
+<meta name="citation_doi" content="10.3390/app14062542">
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@@ -92,40 +60,11 @@
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-<meta property="og:title" content="Special Issue on Evolutionary Computation: Theories, Techniques, and Applications - Applied Sciences - 2024">
+<meta property="og:title" content="Evolutionary Computation: Theories, Techniques, and Applications - Applied Sciences - 2024">
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-<meta property="og:description" content="Evolutionary computation offers powerful problem-solving methodologies inspired
-by models of natural genetics and evolutionary processes. Potential
-applications are wide-ranging and include problems from combinatorial
-optimization, numerical optimization, multi-objective optimization, and others
-as well as specific applications of these problems in diverse domains, such as
-engineering, design, medicine, robotics, science, etc. Techniques from
-evolutionary computation often lend themselves well to parallel and distributed
-implementations and are often more effective in dealing with challenging
-problem characteristics such as non-linearity and high-dimensionality than
-alternative approaches.    This Special Issue invites submissions on
-recent advances in the theory and applications of evolutionary computation.
-Papers on all forms of evolutionary computation are welcome, including but not
-limited to genetic algorithms, genetic programming, evolution strategies,
-evolutionary programming, and memetic algorithms, as well as papers at the
-intersection of evolutionary computation and machine learning, such as
-neuroevolution. Additionally, we welcome papers that employ closely related
-techniques such as simulated annealing, ant colony optimization, particle swarm
-optimization, artificial immune systems, and other metaheuristics. All
-application areas are welcome, but submissions that deal with problems in the
-applied sciences are especially encouraged.    The special issue covers
-the following non-exclusive list of topics:     ant colony
-optimization;   artificial immune systems;   combinatorial
-optimization;   evolution strategies;   evolutionary
-computation;   evolutionary data mining;   evolutionary machine
-learning;   evolutionary programming;   evolvable hardware; 
- genetic algorithms;   genetic programming;   memetic
-algorithms;   metaheuristics;   multi-objective
-optimization;   neuroevolution;   numerical optimization; 
- particle swarm optimization;   simulated annealing.   
- Submissions opened May 2022 with a deadline of January 30, 2024.">
+<meta property="og:description" content="Evolutionary Computation: Theories, Techniques, and Applications - Applied Sciences - 2024">
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@@ -148,45 +87,15 @@ <h2><a href="/"><img src="/images/vac.svg" alt="Vincent A. Cicirello, Ph.D. - Pr
 </header>
 <article itemprop="mainEntity" itemscope itemtype="https://schema.org/ScholarlyArticle">
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-<h2><a itemprop="url" href="https://www.mdpi.com/journal/applsci/special_issues/Evolutionary_Computation"><span itemprop="name headline">Special Issue on Evolutionary Computation: Theories, Techniques, and Applications</span></a></h2>
-<h3><span itemprop="editor" itemscope itemtype="https://schema.org/Person"><a itemprop="url mainEntityOfPage" href="/"><span itemprop="name">Vincent A. Cicirello</span></a></span> (Editor)</h3>
+<h2><a itemprop="url" href="/publications/applsci-14-02542.pdf"><span itemprop="name headline">Evolutionary Computation: Theories, Techniques, and Applications</span></a></h2>
+<h3><span itemprop="author" itemscope itemtype="https://schema.org/Person"><a itemprop="url mainEntityOfPage" href="/"><span itemprop="name">Vincent A. Cicirello</span></a></span></h3>
 <h4><span itemprop="isPartOf" itemscope itemtype="https://schema.org/Periodical"><i><span itemprop="name">Applied Sciences</span></i></span>,
-<time datetime="2024-01" itemprop="datePublished">January 2024</time>.</h4>
+<span itemprop="isPartOf" itemscope itemtype="https://schema.org/PublicationVolume"><span itemprop="volumeNumber">14</span></span>(<span itemprop="isPartOf" itemscope itemtype="https://schema.org/PublicationIssue"><span itemprop="issueNumber">6</span></span>), Article 2542,
+<time datetime="2024-03" itemprop="datePublished">March 2024</time>. doi:10.3390/app14062542</h4>
 </header>
-<p><a href="/publications/cicirello2024applsci.bib">[BIB]</a> <a itemprop="sameAs" href="https://www.mdpi.com/journal/applsci/special_issues/Evolutionary_Computation">[PUB]</a></p>
-<h4>Abstract</h4>
-<div itemprop="description"><p>Evolutionary computation offers powerful problem-solving methodologies inspired
-by models of natural genetics and evolutionary processes. Potential
-applications are wide-ranging and include problems from combinatorial
-optimization, numerical optimization, multi-objective optimization, and others
-as well as specific applications of these problems in diverse domains, such as
-engineering, design, medicine, robotics, science, etc. Techniques from
-evolutionary computation often lend themselves well to parallel and distributed
-implementations and are often more effective in dealing with challenging
-problem characteristics such as non-linearity and high-dimensionality than
-alternative approaches. </p><p> This Special Issue invites submissions on
-recent advances in the theory and applications of evolutionary computation.
-Papers on all forms of evolutionary computation are welcome, including but not
-limited to genetic algorithms, genetic programming, evolution strategies,
-evolutionary programming, and memetic algorithms, as well as papers at the
-intersection of evolutionary computation and machine learning, such as
-neuroevolution. Additionally, we welcome papers that employ closely related
-techniques such as simulated annealing, ant colony optimization, particle swarm
-optimization, artificial immune systems, and other metaheuristics. All
-application areas are welcome, but submissions that deal with problems in the
-applied sciences are especially encouraged. </p><p> The special issue covers
-the following non-exclusive list of topics:</p> <ul> <li>ant colony
-optimization;</li> <li>artificial immune systems;</li> <li>combinatorial
-optimization;</li> <li>evolution strategies;</li> <li>evolutionary
-computation;</li> <li>evolutionary data mining;</li> <li>evolutionary machine
-learning;</li> <li>evolutionary programming;</li> <li>evolvable hardware;</li>
-<li>genetic algorithms;</li> <li>genetic programming;</li> <li>memetic
-algorithms;</li> <li>metaheuristics;</li> <li>multi-objective
-optimization;</li> <li>neuroevolution;</li> <li>numerical optimization;</li>
-<li>particle swarm optimization;</li> <li>simulated annealing.</li> </ul>
-<p>Submissions opened May 2022 with a deadline of January 30, 2024.</p></div>
+<p><span itemprop="encoding" itemscope itemtype="https://schema.org/MediaObject"><a itemprop="contentUrl" href="/publications/applsci-14-02542.pdf">[<span itemprop="encodingFormat">PDF</span>]</a></span> <a href="/publications/cicirello2024applsci.bib">[BIB]</a> <a itemprop="sameAs" href="https://doi.org/10.3390/app14062542">[DOI]</a></p>
 <h4>Graphical Abstract</h4>
-<img itemprop="image" src="/publications/images/GraphicalAbstract-SI-on-Evolutionary-Computation.png" alt="Graphical Abstract: Special Issue on Evolutionary Computation: Theories, Techniques, and Applications" width="1200" height="630" class="respimg">
+<img itemprop="image" src="/publications/images/GraphicalAbstract-SI-on-Evolutionary-Computation.png" alt="Graphical Abstract: Evolutionary Computation: Theories, Techniques, and Applications" width="1200" height="630" class="respimg">
 </article>
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diff --git a/publications/cicirello2024techreport.bib b/publications/cicirello2024techreport.bib
@@ -0,0 +1,11 @@
+@TECHREPORT{cicirello2024techreport,
+AUTHOR = {Vincent A. Cicirello},
+TITLE = {On the Average Runtime of an Open Source Binomial Random Variate Generation Algorithm},
+INSTITUTION = {Cicirello.org},
+MONTH = {March},
+NUMBER = {ALG-24-007},
+TYPE = {Technical Report},
+YEAR = {2024},
+URL = {https://reports.cicirello.org/24/007/ALG-24-007.pdf}
+}
+